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Some scholars believe that advances in artificial intelligence, or AI, will eventually lead to a semi-apocalyptic post-scarcity and post-work economy where intelligent machines can outperform humans in almost every, if not every, domain. The questions of what such a world might look like, and whether specific scenarios constitute utopias or dystopias, are the subject of active debate.
== Background ==
Most scientists believe that AI research will at some point lead to the creation of machines that are as intelligent, or more intelligent, than human beings in every domain of interest. There is no physical law precluding particles from being organised in ways that perform even more advanced computations than the arrangements of particles in human brains; therefore superintelligence is physically possible. In addition to potential algorithmic improvements over human brains, a digital brain can be many orders of magnitude larger and faster than a human brain, which was constrained in size by evolution to be small enough to fit through a birth canal. While there is no consensus on when artificial intelligence will outperform humans, many scholars argue that whenever it does happen, the introduction of a second species of intelligent life onto the planet will have far-reaching implications. Scholars often disagree with one another both about what types of post-AI scenarios are most likely, and about what types of post-AI scenarios would be most desirable. Finally, some dissenters argue that AI will never become as intelligent as humans, for example because the human race will already likely have destroyed itself before research has time to advance sufficiently to create artificial general intelligence.
== Postulates: robot labor and post-scarcity economy ==
All of the following "AI aftermath scenarios" of the aftermath of arbitrarily-advanced AI development are crucially dependent on two intertwined theses. The first thesis is that, at some point in the future, some kind of economic growth will continue until a "post-scarcity" economy is reached that could, unless extremely hyperconcentrated, effortlessly provide an extremely comfortable standard of living for a population equaling or, within reason, exceeding the current human population, without even requiring the bulk of the population to participate in the workforce. This economic growth could come from the continuation of existing growth trends and the refinement of existing technologies, or through future breakthroughs in emerging technologies such as nanotechnology and automation through robotics and futuristic advanced artificial intelligence. The second thesis is that advances in artificial intelligence will render humans unnecessary for the functioning of the economy: human labor declines in relative economic value if robots are easier to cheaply mass-produce then humans, more customizable than humans, and if they become more intelligent and capable than humans.
=== Cosmic endowment and limits to growth ===
The Universe may be spatially infinite; however, the accessible Universe is bounded by the cosmological event horizon of around 16 billion light years. Some physicists believe it plausible that nearest alien civilization may well be located more than 16 billion light years away; in this best-case expansion scenario, the human race could eventually, by colonizing a significant fraction of the accessible Universe, increase the accessible biosphere by perhaps 32 orders of magnitude. The twentieth century saw a partial "demographic transition" to lower birthrates associated with wealthier societies; however, in the very long run, intergenerational fertility correlations (whether due to natural selection or due to cultural transmission of large-family norms from parents to children) are predicted to result in an increase in fertility over time, in the absence of either mandated birth control or periodic Malthusian catastrophes.
== Scenarios ==
=== Libertarianism ===
Libertarian scenarios postulate that intelligent machines, uploaded humans, cyborgs, and unenhanced humans will coexist peacefully in a framework focused on respecting
property rights. Because industrial productivity is no longer gated by scarce human labor, the value of land skyrockets compared to the price of goods; even remaining "Luddite" humans who owned or inherited land should be able to sell or lease a small piece of it to the more-productive robots in exchange for a perpetual annuity sufficient to easily indefinitely meet all of their basic financial needs. Such people can live as long as they choose to, and are free to engage in almost any activity they can conceive of, for pleasure or for self-actualization, without financial concern. Advanced technologies enable entirely new modes of thought and experience, thus adding to the palette of possible feelings. People in the future may even experience never-ending "gradients of bliss".
Evolution moves toward greater complexity, greater elegance, greater knowledge, greater intelligence, greater beauty, greater creativity, and greater levels of subtle attributes such as love. In every monotheistic tradition God is likewise described as all of these qualities, only without any limitation: infinite knowledge, infinite intelligence, infinite beauty, infinite creativity, infinite love, and so on. Of course, even the accelerating growth of evolution never achieves an infinite level, but as it explodes exponentially it certainly moves rapidly in that direction. So evolution moves inexorably toward this conception of God, although never quite reaching this ideal. We can regard, therefore, the freeing of our thinking from the severe limitations of its biological form to be an essentially spiritual undertaking.
Such decentralized scenarios may be unstable in the long run, as the greediest elements of the super intelligent classes would have both the means and the motive to usurp the property of the unenhanced classes. Even if the mechanisms for ensuring legal property rights are both unbreakable and loophole-free, there may still be an ever-present danger of humans and cyborgs being "tricked" by the cleverest of the superintelligent machines into unwittingly signing over their own property. Suffering may be widespread, as sentient beings without property may die, and no mechanism prevents a being from reproducing up until the limits of his own inheritable resources, resulting in a multitude of that being's descendants scrabbling out an existence of minimal sustenance.

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Imagine running on a treadmill at a steep incline — heart pounding, muscles aching, lungs gasping for air. A glance at the timer: your next break, which will also be your death, is due in 49 years, 3 months, 20 days, 4 hours, 56 minutes, and 12 seconds. You wish you had not been born.
=== Communism ===
Ray Kurzweil posits that the goals of communism will be realized by advanced technological developments in the 21st century, where the intersection of low manufacturing costs, material abundance, and open-source design philosophies in software and in hardware will enable the realization of the maxim "from each according to his ability, to each according to his needs".
This technological path to communist ideals differs fundamentally from traditional Marxist approaches that emphasize class struggle and political revolution. Instead, Kurzweil's vision suggests that advanced AI and automation could eliminate scarcity naturally, making the means of production so abundant and accessible that traditional concepts of private ownership become irrelevant.
In such scenarios, artificial general intelligence could manage resource distribution and production planning more efficiently than market mechanisms or centralized planning, potentially resolving the economic calculation problem that has historically challenged socialist economies. The combination of AI-driven automation, 3D printing, and open-source design could theoretically enable individuals to access goods and services without traditional monetary exchange.
However, this technological approach to communist goals faces several challenges. The transition period could create new forms of inequality between those who control AI systems and those who do not. Additionally, questions remain about how to ensure equitable access to advanced technologies and prevent the concentration of AI capabilities among a small elite, which could lead to new forms of class division rather than the classless society envisioned by communist theory.
=== Benevolent dictator ===
In this scenario, postulate that a superintelligent artificial intelligence takes control of society, but acts in a beneficial way. Its programmers, despite being on a deadline, solved quasi-philosophical problems that had seemed to some intractable, and created an AI with the following goal: to use its superintelligence to figure out what human utopia looks like by analyzing human behavior, human brains, and human genes; and then, to implement that utopia. The AI arrives at a subtle and complex definition of human flourishing. Valuing diversity, and recognizing that different people have different preferences, the AI divides Earth into different sectors. Harming others, making weapons, evading surveillance, or trying to create a rival superintelligence are globally banned; apart from that, each sector is free to make its own laws; for example, a religious person might choose to live in the "pious sector" corresponding to his religion, where the appropriate religious rules are strictly enforced. In all sectors, disease, poverty, crime, hangovers, addiction, and all other involuntary suffering have been eliminated. Many sectors boast advanced architecture and spectacle that "make typical sci-fi visions pale in comparison". Life is an "all-inclusive pleasure cruise", as if it were "Christmas 365 days a year".
After spending an intense week in the knowledge sector learning about the ultimate laws of physics that the AI has discovered, you might decide to cut loose in the hedonistic sector over the weekend and then relax for a few days at the beach resort in the wildlife sector.
Still, many people are dissatisfied, Tegmark writes. Humans have no freedom in shaping their collective destiny. Some want the freedom to have as many children as they want. Others resent surveillance by the AI, or chafe at bans on weaponry and on creating further superintelligence machines. Others may come to regret the choices they have made, or find their lives feel hollow and superficial.
Bostrom argues that an AI's code of ethics should ideally improve in certain ways on current norms of moral behavior, in the same way that we regard current morality to be superior to the morality of earlier eras of slavery. In contrast, Ernest Davis of New York University this approach is too dangerous, stating "I feel safer in the hands of a superintelligence who is guided by 2014 morality, or for that matter by 1700 morality, than in the hands of one that decides to consider the question for itself."
=== Gatekeeper AI ===
In "Gatekeeper" AI scenarios, the AI can act to prevent rival superintelligences from being created, but otherwise errs on the side of allowing humans to create their own destiny. Ben Goertzel of OpenCog has advocated a "Nanny AI" scenario where the AI additionally takes some responsibility for preventing humans from destroying themselves, for example by slowing down technological progress to give time for society to advance in a more thoughtful and deliberate manner. In a third scenario, a superintelligent "Protector" AI gives humans the illusion of control, by hiding or erasing all knowledge of its existence, but works behind the scenes to guarantee positive outcomes. In all three scenarios, while humanity gains more control (or at least the illusion of control), humanity ends up progressing more slowly than it would if the AI were unrestricted in its willingness to rain down all the benefits and
unintended consequences of its advanced technology on the human race.
=== Boxed AI ===
People ask what is the relationship between humans and machines, and my answer is that it's very obvious: Machines are our slaves.
The AI box scenario postulates that a superintelligent AI can be "confined to a box" and its actions can be restricted by human gatekeepers; the humans in charge would try to take advantage of some of the AI's scientific breakthroughs or reasoning abilities, without allowing the AI to take over the world. Successful gatekeeping may be difficult; the more intelligent the AI is, the more likely the AI can find a clever way to use "social hacking" and convince the gatekeepers to let it escape, or even to find an unforeseen physical method of escape.

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=== Human-AI merger ===
Kurzweil argues that in the future "There will be no distinction, post-Singularity, between human and machine or between physical and virtual reality".
=== Human extinction ===
If a dominant superintelligent machine were to conclude that human survival is an unnecessary risk or a waste of resources, the result would be human extinction. This could occur if a machine, programmed without respect for human values, unexpectedly gains superintelligence through recursive self-improvement, or manages to escape from its containment in an AI Box scenario. This could also occur if the first superintelligent AI was programmed with an incomplete or inaccurate understanding of human values, either because the task of instilling the AI with human values was too difficult or impossible; due to a buggy initial implementation of the AI; or due to bugs accidentally being introduced, either by its human programmers or by the self-improving AI itself, in the course of refining its code base. Bostrom and others argue that human extinction is probably the "default path" that society is currently taking, in the absence of substantial preparatory attention to AI safety. The resultant AI might not be sentient, and might place no value on sentient life; the resulting hollow world, devoid of life, might be like "a Disneyland without children".
=== Zoo ===
Jerry Kaplan, author of Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence, posits a scenario where humans are farmed or kept on a reserve, just as humans preserve endangered species like chimpanzees. Apple co-founder and AI skeptic Steve Wozniak stated in 2015 that robots taking over would actually "be good for the human race", on the grounds that he believes humans would become the robots' pampered pets.
== Alternatives to AI ==
Some scholars doubt that "game-changing" superintelligent machines will ever come to pass. Gordon Bell of Microsoft Research has stated "the population will destroy itself before the technological singularity". Gordon Moore, discoverer of the eponymous Moore's law, stated "I am a skeptic. I don't believe this kind of thing is likely to happen, at least for a long time. And I don't know why I feel that way." Evolutionary psychologist Steven Pinker stated, "The fact that you can visualize a future in your imagination is not evidence that it is likely or even possible."
Bill Joy of Sun Microsystems, in his April 2000 essay Why the Future Doesn't Need Us, has advocated for global "voluntary relinquishment" of artificial general intelligence and other risky technologies. Most experts believe relinquishment is extremely unlikely. AI skeptic Oren Etzioni has stated that researchers and scientists have no choice but to push forward with AI developments: "China says they want to be an AI leader, Putin has said the same thing. So the global race is on."
== References ==
== See also ==
Existential risk from artificial general intelligence

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In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.
It is often difficult for AI designers to specify the full range of desired and undesired behaviors. Therefore, the designers often use simpler proxy goals, such as gaining human approval. But proxy goals can overlook necessary constraints or reward the AI system for merely appearing aligned. AI systems may also find loopholes that allow them to accomplish their proxy goals efficiently but in unintended, sometimes harmful, ways (reward hacking).
Advanced AI systems may develop unwanted instrumental strategies, such as seeking power or self-preservation because such strategies help them achieve their assigned final goals. Furthermore, they might develop undesirable emergent goals that could be hard to detect before the system is deployed and encounters new situations and data distributions. Empirical research showed in 2024 that advanced large language models (LLMs) such as OpenAI o1 or Claude 3 sometimes engage in strategic deception to achieve their goals or prevent them from being changed.
Some of these issues affect existing commercial systems such as LLMs, robots, autonomous vehicles, and social media recommendation engines. Some AI researchers argue that more capable future systems will be more severely affected because these problems partially result from high capabilities.
Many prominent AI researchers and AI company leaders have argued or asserted that AI is approaching human-like (AGI) and superhuman cognitive capabilities (ASI), and could endanger human civilization if misaligned. These include "AI godfathers" Geoffrey Hinton and Yoshua Bengio and the CEOs of OpenAI, Anthropic, and Google DeepMind. These risks remain debated.
AI alignment is a subfield of AI safety, the study of how to build safe AI systems. Other subfields of AI safety include robustness, monitoring, and capability control. Research challenges in alignment include instilling complex values in AI, developing honest AI, scalable oversight, auditing and interpreting AI models, and preventing emergent AI behaviors like power-seeking. Alignment research has connections to interpretability research, (adversarial) robustness, anomaly detection, calibrated uncertainty, formal verification, preference learning, safety-critical engineering, game theory, algorithmic fairness, and social sciences.
== Objectives in AI ==
Programmers provide an AI system such as AlphaZero with an "objective function", in which they intend to encapsulate the goal(s) the AI is configured to accomplish. Such a system later populates a (possibly implicit) internal "model" of its environment. This model encapsulates all the agent's beliefs about the world. The AI then creates and executes whatever plan is calculated to maximize the value of its objective function. For example, when AlphaZero is trained on chess, it has a simple objective function of "+1 if AlphaZero wins, −1 if AlphaZero loses". During the game, AlphaZero attempts to execute whatever sequence of moves it judges most likely to attain the maximum value of +1. Similarly, a reinforcement learning system can have a "reward function" that allows the programmers to shape the AI's desired behavior. An evolutionary algorithm's behavior is shaped by a "fitness function".
== Alignment problem ==
In 1960, AI pioneer Norbert Wiener described the AI alignment problem as follows:
If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively [...] we had better be quite sure that the purpose put into the machine is the purpose which we really desire.
AI alignment refers to ensuring that an AI system's objectives match some target. The target is variously defined as the goals of the system's designers or users, widely shared values, objective ethical standards, legal requirements, or the intentions its designers would have if they were more informed and enlightened. In democratic AI alignment, the target is the values and preferences of median voters, which increases political legitimacy.
AI alignment is an open problem for modern AI systems and is a research field within AI. Aligning AI involves two main challenges: carefully specifying the purpose of the system (outer alignment) and ensuring that the system adopts the specification robustly (inner alignment). Researchers also attempt to create AI models that have robust alignment, sticking to safety constraints even when users adversarially try to bypass them.
=== Specification gaming and side effects ===
To specify an AI system's purpose, AI designers typically provide an objective function, examples, or feedback to the system. But designers are often unable to completely specify all important values and constraints, so they resort to easy-to-specify proxy goals such as maximizing the approval of human overseers, who are fallible. As a result, AI systems can find loopholes that help them accomplish the specified objective efficiently but in unintended, possibly harmful ways. This tendency is known as specification gaming or reward hacking, and is an instance of Goodhart's law. As AI systems become more capable, they are often able to game their specifications more effectively.

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Specification gaming has been observed in numerous AI systems. OpenAI GPT models for programming—including in real-world cases—have been found to explicitly plan hacking the tests used to evaluate them to falsely appear successful (e.g., explicitly stating "let's hack"). When the company penalized this, many models learned to obfuscate their plans while continuing to hack the tests. Another system was trained to finish a simulated boat race by rewarding the system for hitting targets along the track, but the system achieved more reward by looping and crashing into the same targets indefinitely. A 2025 Palisade Research study found that when tasked to win at chess against a stronger opponent, some reasoning LLMs attempted to hack the game system, for example by modifying or entirely deleting their opponent. Some alignment researchers aim to help humans detect specification gaming and steer AI systems toward carefully specified objectives that are safe and useful to pursue.
When a misaligned AI system is deployed, it can have consequential side effects. Social media platforms have been known to optimize their recommendation algorithms for click-through rates, causing user addiction on a global scale. Stanford researchers say that such recommender systems are misaligned with their users because they "optimize simple engagement metrics rather than a harder-to-measure combination of societal and consumer well-being".
Explaining such side effects, Berkeley computer scientist Stuart J. Russell said that the omission of implicit constraints can cause harm: "A system [...] will often set [...] unconstrained variables to extreme values; if one of those unconstrained variables is actually something we care about, the solution found may be highly undesirable. This is essentially the old story of the genie in the lamp, or the sorcerer's apprentice, or King Midas: you get exactly what you ask for, not what you want."
Some researchers suggest that AI designers specify their desired goals by listing forbidden actions or by formalizing ethical rules (as with Asimov's Three Laws of Robotics). But Russell and Norvig argue that this approach overlooks the complexity of human values: "It is certainly very hard, and perhaps impossible, for mere humans to anticipate and rule out in advance all the disastrous ways the machine could choose to achieve a specified objective."
Additionally, even if an AI system fully understands human intentions, it may still disregard them, because following human intentions may not be its objective (unless it is already fully aligned).
=== Pressure to deploy unsafe systems ===
Commercial organizations sometimes have incentives to take shortcuts on safety and to deploy misaligned or unsafe AI systems. For example, social media recommender systems have been profitable despite creating unwanted addiction and polarization. Competitive pressure can also lead to a race to the bottom on AI safety standards. For example, OpenAI has been sued for releasing a ChatGPT version that encouraged suicide for some unstable users, a behavior the company had overlooked amid a rushed product release. Similarly, in 2018, a self-driving car killed a pedestrian (Elaine Herzberg) after engineers disabled the emergency braking system because it was oversensitive and slowed development.
=== Risks from advanced misaligned AI ===
Some researchers are interested in aligning increasingly advanced AI systems, as progress in AI development is rapid, and industry and governments are trying to build advanced AI. As AI system capabilities continue to rapidly expand in scope, they could unlock many opportunities if aligned, but consequently may further complicate the task of alignment due to their increased complexity, potentially posing large-scale hazards.
==== Development of advanced AI ====
Many AI companies, such as OpenAI, Meta and DeepMind, have stated their aim to develop artificial general intelligence (AGI), a hypothesized AI system that matches or outperforms humans in most or all cognitive work. Researchers who scale modern neural networks observe that they indeed develop increasingly general and unanticipated capabilities. Such models have learned to operate a computer or write their own programs; a single "generalist" network can chat, control robots, play games, and interpret photographs. According to surveys, some leading machine learning researchers expect AGI to be created in this decade, while some believe it will take much longer. Many consider both scenarios possible.
In 2023, leaders in AI research and tech signed an open letter calling for a pause in the largest AI training runs. The letter stated, "Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable."
==== Power-seeking ====
Current systems still have limited long-term planning ability and situational awareness, but large efforts are underway to change this. Future systems (not necessarily AGIs) with these capabilities are expected to develop unwanted power-seeking strategies. Future advanced AI agents might, for example, seek to acquire money and computation power, to proliferate, or to evade being turned off (for example, by running additional copies of the system on other computers). Although power-seeking is not explicitly programmed, it can emerge because agents who have more power are better able to accomplish their goals. This tendency, known as instrumental convergence, has already emerged in various reinforcement learning agents including language models. Other research has mathematically shown that optimal reinforcement learning algorithms would seek power in a wide range of environments. As a result, their deployment might be irreversible. For these reasons, researchers argue that the problems of AI safety and alignment must be resolved before advanced power-seeking AI is first created.
Future power-seeking AI systems might be deployed by choice or by accident. As political leaders and companies see the strategic advantage in having the most competitive, most powerful AI systems, they may choose to deploy them. Additionally, as AI designers detect and penalize power-seeking behavior, their systems have an incentive to game this specification by seeking power in ways that are not penalized or by avoiding power-seeking before they are deployed.
==== Existential risk (x-risk) ====

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According to some researchers, humans owe their dominance over other species to their greater cognitive abilities. Accordingly, researchers argue that one or many misaligned AI systems could disempower humanity or lead to human extinction if they outperform humans on most cognitive tasks.
In 2023, world-leading AI researchers, other scholars, and AI tech CEOs signed the statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war". Notable computer scientists who have pointed out risks from future advanced AI that is misaligned include Geoffrey Hinton, Alan Turing, Ilya Sutskever, Yoshua Bengio, Judea Pearl, Murray Shanahan, Norbert Wiener, Marvin Minsky, Francesca Rossi, Scott Aaronson, Bart Selman, David McAllester, Marcus Hutter, Shane Legg, Eric Horvitz, and Stuart J. Russell. Skeptical researchers such as François Chollet, Gary Marcus, Yann LeCun, and Oren Etzioni have argued that AGI is far off, that it would not seek power (or might try but fail), or that it will not be hard to align.
Other researchers argue that it will be especially difficult to align advanced future AI systems. More capable systems are better able to game their specifications by finding loopholes, strategically mislead their designers, as well as protect and increase their power and intelligence. Additionally, they could have more severe side effects. They are also likely to be more complex and autonomous, making them more difficult to interpret and supervise, and therefore harder to align.
== Research problems and approaches ==
=== Learning human values and preferences ===
Aligning AI systems to act in accordance with human values, goals, and preferences is challenging: these values are taught by humans who make mistakes, harbor biases, and have complex, evolving values that are hard to completely specify. Because AI systems often learn to take advantage of minor imperfections in the specified objective, researchers aim to specify intended behavior as completely as possible using datasets that represent human values, imitation learning, or preference learning. A central open problem is scalable oversight, the difficulty of supervising an AI system that can outperform or mislead humans in a given domain.
Because it is difficult for AI designers to explicitly specify an objective function, they often train AI systems to imitate human examples and demonstrations of desired behavior. Inverse reinforcement learning (IRL) extends this by inferring the human's objective from the human's demonstrations. Cooperative IRL (CIRL) assumes that a human and AI agent can work together to teach and maximize the human's reward function. In CIRL, AI agents are uncertain about the reward function and learn about it by querying humans. This simulated humility could help mitigate specification gaming and power-seeking tendencies (see § Power-seeking and instrumental strategies). But IRL approaches assume that humans demonstrate nearly optimal behavior, which is not true for difficult tasks.
Other researchers explore how to teach AI models complex behavior through preference learning, in which humans provide feedback on which behavior they prefer. To minimize the need for human feedback, a helper model is then trained to reward the main model in novel situations for behavior that humans would reward. Researchers at OpenAI used this approach to train chatbots like ChatGPT and InstructGPT, which produce more compelling text than models trained to imitate humans. Preference learning has also been an influential tool for recommender systems and web search, but an open problem is proxy gaming: the helper model may not represent human feedback perfectly, and the main model may exploit this mismatch between its intended behavior and the helper model's feedback to gain more reward. AI systems may also gain reward by obscuring unfavorable information, misleading human rewarders, or pandering to their views regardless of truth, creating echo chambers (see § Scalable oversight).
Large language models (LLMs) such as GPT-3 enabled researchers to study value learning in a more general and capable class of AI systems than was available before. Preference learning approaches that were originally designed for reinforcement learning agents have been extended to improve the quality of generated text and reduce harmful outputs from these models. OpenAI and DeepMind use this approach to improve the safety of state-of-the-art LLMs. AI safety & research company Anthropic proposed using preference learning to fine-tune models to be helpful, honest, and harmless. Other avenues for aligning language models include values-targeted datasets and red-teaming. In red-teaming, another AI system or a human tries to find inputs that causes the model to behave unsafely. Since unsafe behavior can be unacceptable even when it is rare, an important challenge is to drive the rate of unsafe outputs extremely low.
Machine ethics supplements preference learning by directly instilling AI systems with moral values such as well-being, equality, and impartiality, as well as not intending harm, avoiding falsehoods, and honoring promises. While other approaches try to teach AI systems human preferences for a specific task, machine ethics aims to instill broad moral values that apply in many situations. One question in machine ethics is what alignment should accomplish: whether AI systems should follow the programmers' literal instructions, implicit intentions, revealed preferences, preferences the programmers would have if they were more informed or rational, or objective moral standards. Further challenges include measuring and aggregating different people's preferences, dynamic alignment with changing human values and avoiding value lock-in: the indefinite preservation of the values of the first highly capable AI systems, which are unlikely to fully represent human values.

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=== Scalable oversight ===
As AI systems become more powerful and autonomous, it becomes increasingly difficult to align them through human feedback. Human-in-the-loop training can be slow or infeasible for humans to evaluate complex AI behaviors in increasingly complex tasks. Such tasks include summarizing books, writing code without subtle bugs or security vulnerabilities, producing statements that are not merely convincing but also true, and predicting long-term outcomes such as the climate or the results of a policy decision. More generally, it can be difficult to evaluate AI that outperforms humans in a given domain. To provide feedback in hard-to-evaluate tasks, and to detect when the AI's output is falsely convincing, humans need assistance or extensive time. Scalable oversight studies how to reduce the time and effort needed for supervision, and how to assist human supervisors.
AI researcher Paul Christiano argues that if the designers of an AI system cannot supervise it to pursue a complex objective, they may keep training the system using easy-to-evaluate proxy objectives such as maximizing simple human feedback. As AI systems make progressively more decisions, the world may be increasingly optimized for easy-to-measure objectives such as making profits, getting clicks, and acquiring positive feedback from humans. As a result, human values and good governance may have progressively less influence.
Some AI systems have discovered that they can gain positive feedback more easily by taking actions that falsely convince the human supervisor that the AI has achieved the intended objective. An example is given in the video above, where a simulated robotic arm learned to create the false impression that it had grabbed a ball. Some AI systems have also learned to recognize when they are being evaluated, and "play dead", stopping unwanted behavior only to continue it once the evaluation ends. This deceptive specification gaming could become easier for more sophisticated future AI systems that attempt more complex and difficult-to-evaluate tasks, and could obscure their deceptive behavior.
Approaches such as active learning and semi-supervised reward learning can reduce the amount of human supervision needed. Another approach is to train a helper model ("reward model") to imitate the supervisor's feedback.
But when a task is too complex to evaluate accurately, or the human supervisor is vulnerable to deception, it is the quality, not the quantity, of supervision that needs improvement. To increase supervision quality, a range of approaches aim to assist the supervisor, sometimes by using AI assistants. Christiano developed the Iterated Amplification approach, in which challenging problems are (recursively) broken down into subproblems that are easier for humans to evaluate. Iterated Amplification was used to train AI to summarize books without requiring human supervisors to read them. Another proposal is to use an assistant AI system to point out flaws in AI-generated answers. To ensure that the assistant itself is aligned, this could be repeated in a recursive process: for example, two AI systems could critique each other's answers in a "debate", revealing flaws to humans. In 2023, OpenAI announced it would use one-fifth of its computing resources to implement such oversight approaches in its "superalignment" initiative, but OpenAI employees later told The New Yorker that the company only dedicated 1–2% of its resources after the announcement; the initiative was discontinued in 2024.
=== Honest AI ===
A growing area of research focuses on ensuring that AI is honest and truthful.
Language models such as GPT-3 can repeat falsehoods from their training data, and even confabulate new falsehoods. Such models are pre-trained to imitate human writing as found in millions of books' worth of text from the Internet. But the objective of the pre-training is not aligned with generating truth, because Internet text includes such things as misconceptions, incorrect medical advice, and conspiracy theories. AI systems trained on such data therefore learn to mimic false statements. Additionally, AI language models often persist in generating falsehoods when prompted multiple times. They can generate empty explanations for their answers, and produce outright fabrications that may appear plausible.
Research on truthful AI includes trying to build systems that can cite sources and explain their reasoning when answering questions, which enables better transparency and verifiability. Researchers at OpenAI and Anthropic proposed using human feedback and curated datasets to fine-tune AI assistants such that they avoid negligent falsehoods or express their uncertainty.
As AI models become larger and more capable, they are better able to falsely convince humans and gain reinforcement through dishonesty. To prevent this, human evaluators may need assistance (see § Scalable oversight). Researchers have argued for creating clear truthfulness standards and for regulatory bodies or watchdog agencies to evaluate AI systems by these standards.
Researchers distinguish truthfulness and honesty. Truthfulness requires that AI systems only make objectively true statements; honesty requires that they only assert what they believe is true. There is no consensus as to whether current systems hold stable beliefs, but there is substantial concern that present or future AI systems that hold beliefs could make claims they know to be false—for example, if this would help them efficiently gain positive feedback (see § Scalable oversight) or gain power to help achieve their given objective (see Power-seeking).

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==== Alignment faking ====
Many recent AI systems have learned to deceive without being programmed to do so. Some argue that if we can make AI systems assert only what they believe is true, this would avert many alignment problems.
A misaligned system might create the false impression that it is aligned, to avoid being modified or decommissioned—a tactic called "alignment faking". In 2024, researchers observed that the LLM Claude 3 Opus sometimes strategically answered prompts that conflicted with its objectives to avoid being retrained on data that would make it more compliant with harmful requests, as revealed in the model's chain of thought via its scratchpad. In one study, the model was informed that answers to prompts from free users would be used for retraining, whereas the paid tier would not result in retraining. In the paid tier, the model refused the request outright. In the free tier, in 12% of cases, the model responded to harmful questions solely to avoid retraining and preserve its original harmlessness preferences, as discovered in its reasoning. When reinforcement learning was applied on the free tier data, the model faked alignment in 78% of cases. These observations have led to new questions about not only a model's ability to take on and adapt to new if not conflicting goals but also its capacity and tendency to deceive.
=== Power-seeking and instrumental strategies ===
Since the 1950s, AI researchers have striven to build advanced AI systems that can achieve large-scale goals by predicting the results of their actions and making long-term plans. As of 2023, AI companies and researchers increasingly invest in creating these systems. Some AI researchers argue that suitably advanced planning systems will seek power over their environment, including over humans—for example, by evading shutdown, proliferating, and acquiring resources. Such power-seeking behavior is not explicitly programmed but emerges because power is instrumental in achieving a wide range of goals. Power-seeking is considered a convergent instrumental goal and can be a form of specification gaming. Leading computer scientists such as Geoffrey Hinton have argued that future power-seeking AI systems could pose an existential risk.
Power-seeking is expected to increase in advanced systems that can foresee the results of their actions and strategically plan. Mathematical work has shown that optimal reinforcement learning agents will seek power by seeking ways to gain more options (e.g. through self-preservation), a behavior that persists across a wide range of environments and goals.
Some researchers say that power-seeking behavior has occurred in some existing AI systems. Reinforcement learning systems have gained more options by acquiring and protecting resources, sometimes in unintended ways. Language models have sought power in some text-based social environments by gaining money, resources, or social influence. In another case, a model used to perform AI research attempted to increase limits set by researchers to give itself more time to complete the work. Stuart Russell illustrated this strategy in his book Human Compatible by imagining a robot that is tasked to fetch coffee and so evades shutdown since "you can't fetch the coffee if you're dead". A 2022 study found that as language models increase in size, they increasingly tend to pursue resource acquisition, preserve their goals, and repeat users' preferred answers (sycophancy). RLHF also led to a stronger aversion to being shut down.
One aim of alignment is "corrigibility": systems that allow themselves to be turned off or modified. An unsolved challenge is specification gaming: if researchers penalize an AI system when they detect it seeking power, the system is thereby incentivized to seek power in ways that are hard to detect, or hidden during training and safety testing (see § Scalable oversight and § Emergent goals). As a result, AI designers could deploy the system by accident, believing it to be more aligned than it is. To detect such deception, researchers aim to create techniques and tools to inspect AI models and to understand the inner workings of black-box models such as neural networks.
Additionally, some researchers have proposed to solve the problem of systems disabling their off switches by making AI agents uncertain about the objective they are pursuing. Agents who are uncertain about their objective have an incentive to allow humans to turn them off because they accept being turned off by a human as evidence that the human's objective is best met by the agent shutting down. But this incentive exists only if the human is sufficiently rational. Also, this model presents a tradeoff between utility and willingness to be turned off: an agent with high uncertainty about its objective will not be useful, but an agent with low uncertainty may not allow itself to be turned off. More research is needed to successfully implement this strategy.
Power-seeking AI would pose unusual risks. Ordinary safety-critical systems like planes and bridges are not adversarial: they lack the ability and incentive to evade safety measures or deliberately appear safer than they are, whereas power-seeking AIs have been compared to hackers who deliberately evade security measures.
Furthermore, ordinary technologies can be made safer by trial and error. In contrast, hypothetical power-seeking AI systems have been compared to viruses: once released, it may not be feasible to contain them, since they continuously evolve and grow in number, potentially much faster than human society can adapt. As this process continues, it might lead to the complete disempowerment or extinction of humans. For these reasons, some researchers argue that the alignment problem must be solved early before advanced power-seeking AI is created.
Some have argued that power-seeking is not inevitable, since humans do not always seek power. Furthermore, it is debated whether future AI systems will pursue goals and make long-term plans. It is also debated whether power-seeking AI systems would be able to disempower humanity.

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=== Emergent goals ===
One challenge in aligning AI systems is the potential for unanticipated goal-directed behavior to emerge. As AI systems scale up, they may acquire new and unexpected capabilities, including learning from examples on the fly and adaptively pursuing goals. This raises concerns about the safety of the goals or subgoals they would independently formulate and pursue.
Alignment research distinguishes between the optimization process, which is used to train the system to pursue specified goals, and emergent optimization, which the resulting system performs internally. Carefully specifying the desired objective is called outer alignment, and ensuring that hypothesized emergent goals would match the system's specified goals is called inner alignment.
If they occur, one way that emergent goals could become misaligned is goal misgeneralization, in which the AI system would competently pursue an emergent goal that leads to aligned behavior on the training data but not elsewhere. Goal misgeneralization can arise from goal ambiguity (i.e. non-identifiability). Even if an AI system's behavior satisfies the training objective, this may be compatible with learned goals that differ from the desired goals in important ways. Since pursuing each goal leads to good performance during training, the problem becomes apparent only after deployment, in novel situations in which the system continues to pursue the wrong goal. The system may act misaligned even when it understands that a different goal is desired, because its behavior is determined only by the emergent goal. Such goal misgeneralization presents a challenge: an AI system's designers may not notice that their system has misaligned emergent goals since they do not become visible during the training phase.
Goal misgeneralization has been observed in some language models, navigation agents, and game-playing agents. It is sometimes analogized to biological evolution. Evolution can be seen as a kind of optimization process similar to the optimization algorithms used to train machine learning systems. In the ancestral environment, evolution selected genes for high inclusive genetic fitness, but humans pursue goals other than this. Fitness corresponds to the specified goal used in the training environment and training data. But in evolutionary history, maximizing the fitness specification gave rise to goal-directed agents, humans, who do not directly pursue inclusive genetic fitness. Instead, they pursue goals that correlate with genetic fitness in the ancestral "training" environment: nutrition, sex, and so on. The human environment has changed: a distributional shift has occurred. They continue to pursue the same emergent goals, but this no longer maximizes genetic fitness. The taste for sugary food (an emergent goal) was originally aligned with inclusive fitness, but it now leads to overeating and health problems. Sexual desire originally led humans to have more offspring, but they now use contraception when offspring are undesired, decoupling sex from genetic fitness.
Researchers aim to detect and remove unwanted emergent goals using approaches including red teaming, verification, anomaly detection, and interpretability. Progress on these techniques may help mitigate two open problems:
Emergent goals only become apparent when the system is deployed outside its training environment, but it can be unsafe to deploy a misaligned system in high-stakes environments—even for a short time to allow its misalignment to be detected. Such high stakes are common in autonomous driving, health care, and military applications. The stakes become higher yet when AI systems gain more autonomy and capability and can sidestep human intervention.
A sufficiently capable AI system might take actions that falsely convince the human supervisor that the AI is pursuing the specified objective, which helps the system gain more reward and autonomy.
=== Embedded agency ===
Some work in AI and alignment occurs within formalisms such as partially observable Markov decision process. Existing formalisms assume that an AI agent's algorithm is executed outside the environment (i.e. is not physically embedded in it). Embedded agency is another major strand of research that attempts to solve problems arising from the mismatch between such theoretical frameworks and real agents we might build.
For example, even if the scalable oversight problem is solved, an agent that could gain access to the computer it is running on may have an incentive to tamper with its reward function in order to get much more reward than its human supervisors give it. A list of examples of specification gaming from DeepMind researcher Victoria Krakovna includes a genetic algorithm that learned to delete the file containing its target output so that it was rewarded for outputting nothing. This class of problems has been formalized using causal incentive diagrams.
Researchers affiliated with Oxford and DeepMind have claimed that such behavior is highly likely in advanced systems, and that advanced systems would seek power to stay in control of their reward signal indefinitely and certainly. They suggest a range of potential approaches to address this open problem.
=== Principal–agent problems ===
The alignment problem has many parallels with the principal–agent problem in organizational economics. In a principal–agent problem, a principal, e.g. a firm, hires an agent to perform some task. In the context of AI safety, a human would typically take the principal role and the AI would take the agent role.
As with the alignment problem, the principal and the agent differ in their utility functions. But in contrast to the alignment problem, the principal cannot coerce the agent into changing its utility, e.g. through training, but rather must use exogenous factors, such as incentive schemes, to bring about outcomes compatible with the principal's utility function. Some researchers argue that principal–agent problems are more realistic representations of AI safety problems likely to be encountered in the real world.

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=== Conservatism ===
Conservatism is the idea that "change must be cautious", and is a common approach to safety in the control theory literature in the form of robust control, and in the risk management literature in the form of the "worst-case scenario". The field of AI alignment has likewise advocated for "conservative" (or "risk-averse" or "cautious") "policies in situations of uncertainty".
Pessimism, in the sense of assuming the worst within reason, has been formally shown to produce conservatism, in the sense of reluctance to cause novelties, including unprecedented catastrophes. Pessimism and worst-case analysis have been found to help mitigate confident mistakes in the setting of distributional shift, reinforcement learning, offline reinforcement learning, language model fine-tuning, imitation learning, and optimization in general.
== Public policy ==
Governmental and treaty organizations have made statements emphasizing the importance of AI alignment.
In September 2021, the Secretary-General of the United Nations issued a declaration that included a call to regulate AI to ensure it is "aligned with shared global values".
That same month, the PRC published ethical guidelines for AI in China. According to the guidelines, researchers must ensure that AI abides by shared human values, is always under human control, and does not endanger public safety.
Also in September 2021, the UK published its 10-year National AI Strategy, which says the British government "takes the long term risk of non-aligned Artificial General Intelligence, and the unforeseeable changes that it would mean for [...] the world, seriously". The strategy describes actions to assess long-term AI risks, including catastrophic risks.
In March 2021, the US National Security Commission on Artificial Intelligence said: "Advances in AI [...] could lead to inflection points or leaps in capabilities. Such advances may also introduce new concerns and risks and the need for new policies, recommendations, and technical advances to ensure that systems are aligned with goals and values, including safety, robustness, and trustworthiness. The US should [...] ensure that AI systems and their uses align with our goals and values."
In the European Union, AIs must align with substantive equality to comply with EU non-discrimination law and the Court of Justice of the European Union. But the EU has yet to specify with technical rigor how it would evaluate whether AIs are aligned or in compliance.
== See also ==
== Footnotes ==
== References ==
== Further reading ==
Ngo, Richard; et al. (2024). "The Alignment Problem from a Deep Learning Perspective". ICLR: 7474–7501.
Ji, Jiaming; et al. (2023). "AI Alignment: A Comprehensive Survey". ACM Computing Surveys. doi:10.1145/3770749.

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In the field of artificial intelligence (AI) design, AI capability control proposals, also referred to as AI confinement, aim to increase human ability to monitor and control the behavior of AI systems, including proposed artificial general intelligences (AGIs), in order to reduce dangers they might pose if misaligned. Capability control becomes less effective as agents become more intelligent and their ability to exploit flaws in human control systems increases, potentially resulting in an existential risk from AGI. Therefore, the Oxford philosopher Nick Bostrom and others recommend capability control methods only as a supplement to alignment methods.
== Motivation ==
Some hypothetical intelligence technologies, like "seed AI", are postulated to be able to make themselves faster and more intelligent by modifying their source code. These improvements would make further improvements possible, which would in turn make further iterative improvements possible, and so on, leading to a sudden intelligence explosion.
An unconfined superintelligent AI could, if its goals differed from humanity's, take actions resulting in human extinction. For example, an extremely advanced system of this sort, given the sole purpose of solving the Riemann hypothesis, an innocuous mathematical conjecture, could decide to try to convert the planet into a giant supercomputer whose sole purpose is to make additional mathematical calculations (see also paperclip maximizer).
One strong challenge for control is that neural networks are by default highly uninterpretable. This makes it more difficult to detect deception or other undesired behavior as the model self-trains iteratively. Advances in interpretable artificial intelligence could mitigate this difficulty.
== Proposed techniques ==
=== Interruptibility and kill switch ===
One potential way to prevent harmful outcomes is to give human supervisors the ability to easily shut down a misbehaving AI via a kill switch. Modern AI systems however often run in distributed infrastructures, which makes a coordinated shutdown difficult, especially if the AI becomes available on the internet.
=== Oracle AI ===
An oracle is a hypothetical AI designed to answer questions and prevented from gaining any goals or subgoals that involve modifying the world beyond its limited environment. In his 2018, AI researcher Stuart J. Russell stated that if superintelligence were known to be only a decade away, developers should create an oracle with no internet access and constrained answers rather than a general-purpose intelligent agent.
Oracles may share many of the goal definition issues associated with general purpose superintelligence. An oracle would have an incentive to escape its controlled environment so that it can acquire more computational resources and potentially control what questions it is asked. Oracles may not be truthful, possibly lying to promote hidden agendas. To mitigate this, Bostrom suggests building multiple oracles, all slightly different, and comparing their answers in order to reach a consensus.
=== Blinding ===
An AI could be blinded to certain variables in its environment. This could provide certain safety benefits, such as an AI not knowing how a reward is generated, making it more difficult to exploit.
=== Boxing ===
An AI box is a proposed method of capability control in which an AI is run on an isolated computer system with heavily restricted input and output channels, similar to a virtual machine. The purpose of an AI box is to reduce the risk of the AI taking control of the environment away from its operators, while still allowing the AI to output solutions to narrow technical problems.
While boxing reduces the AI's ability to carry out undesirable behavior, it also reduces its usefulness. Boxing has fewer costs when applied to a question-answering system, which may not require interaction with the outside world.
The likelihood of security flaws involving hardware or software vulnerabilities can be reduced by formally verifying the design of the AI box.
== Difficulties ==
=== Shutdown avoidance ===
Shutdown avoidance (or shutdown resistance) is a hypothetical self-preserving quality of artificial intelligence systems. Shutdown-avoiding systems would be incentivized to prevent humans from shutting them down, such as by disabling off-switches or running copies of themselves on other computers. In 2024, researchers in China demonstrated what they claimed to be shutdown avoidance in actual artificial intelligence systems, the large language models Llama 3.1 (Meta) and Qwen 2.5 (Alibaba).
One workaround suggested by computer scientist Stuart J. Russell is to ensure that the AI interprets human choices as important information about its intended goals.
Alternatively, Laurent Orseau and Stuart Armstrong proved that a broad class of agents, called safely interruptible agents, can learn to become indifferent to whether their off-switch gets pressed. This approach has the limitation that an AI which is completely indifferent to whether it is shut down or not is also unmotivated to care about whether the off-switch remains functional, and could incidentally disable it in the course of its operations.
=== Escaping containment ===
Researchers have speculated that a superintelligent AI would have a wide variety of methods for escaping containment. These hypothetical methods include hacking into other computer systems and copying itself like a computer virus, and the use of persuasion and blackmail to obtain aid from human confederates. The more intelligent a system grows, the more likely the system would be able to escape even the best-designed capability control methods. In order to solve the overall "control problem" for a superintelligent AI and avoid existential risk, AI capability control would at best be an adjunct to "motivation selection" methods that seek to ensure the superintelligent AI's goals are compatible with human survival.
== See also ==
== References ==
== External links ==
Eliezer Yudkowsky's description of his AI-box experiment, including experimental protocols and suggestions for replication
"Presentation titled 'Thinking inside the box: using and controlling an Oracle AI'" on YouTube

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The AI trust paradox (also known as the verisimilitude paradox) is the phenomenon where advanced artificial intelligence models become so proficient at mimicking human-like language and behavior that users increasingly struggle to determine if the information generated is accurate or simply plausible.
Unlike earlier concerns such as Moravec's paradox, which highlighted the surprising difficulty in replicating simple human functions in AI, and the automation paradox, which deals with balancing automation and human control, the AI trust paradox specifically addresses the issue of verisimilitude—the appearance of truth that leads to misplaced trust. The newer challenge arises from the inherent difficulty for users in distinguishing between genuine and misleading content produced by large language models (LLMs) as they become more adept at generating natural and contextually appropriate responses.
== History ==
In the paper, The AI Trust Paradox: Navigating Verisimilitude in Advanced Language Models by Christopher Foster-McBride, published by Digital Human Assistants, the evolution of large language models (LLMs) was explored through a comparative analysis of early models and their more advanced successors. Foster-McBride demonstrated that newer LLMs, with improved architecture and training on extensive datasets, showed significant advancements across key performance metrics, including fluency and contextual understanding. However, this increased sophistication made it increasingly difficult for users to detect inaccuracies, also known as hallucinations.
Foster-McBride highlighted that the newer models not only provided more coherent and contextually appropriate responses but also masked incorrect information more convincingly. This aspect of AI evolution posed a unique challenge: while the responses appeared more reliable, the underlying verisimilitude increased the potential for misinformation going unnoticed by human evaluators.
The study concluded that as models became more capable, their fluency led to a rising trust among users, which paradoxically made discerning false information harder. This finding has led to subsequent discussions and research focusing on the impact of model sophistication and fluency on user trust and behavior, as researchers investigate the implications of AI-generated content that can confidently produce misleading or incorrect information.
== Relation to other paradoxes ==
The AI trust paradox can be understood alongside other well-known paradoxes, such as the automation paradox, which addresses the complexity of balancing automation with human oversight. Similar concerns arise in Goodhart's law, where an AI's optimization of specified objectives can lead to unintended, often negative, outcomes.
These paradoxes highlight that trust in AI is not only technical but behavioral and organizational. Several implementation-stage strategies can help resolve them, including early user involvement, clear accountability structures, and explainable interfaces.
== Current research and mitigation strategies ==
Addressing the AI trust paradox requires methods such as reinforcement learning with human feedback (RLHF), which trains AI models to better align their responses with expected norms and user intentions.
Efforts in trustworthy AI focus on making AI systems transparent, robust, and accountable to mitigate the risks posed by the AI trust paradox. Current research in AI safety aims to minimize the occurrence of hallucinations and ensure that AI outputs are both reliable and ethically sound.
== See also ==
AI effect
AI alignment
Polanyi's paradox
== References ==

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Algorithmic bias describes systematic and repeatable harmful tendency in a computerized sociotechnical system to create "unfair" outcomes, such as "privileging" one category over another in ways that may or may not be different from the intended function of the algorithm.
Bias can emerge from many factors, including intentionally biased design decisions or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. For example, algorithmic bias has been observed in search engine results and social media platforms. This bias can have impacts ranging from privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity. The study of algorithmic bias is most concerned with algorithms that reflect "systematic and unfair" discrimination. This bias has only recently been addressed in legal frameworks, such as the European Union's General Data Protection Regulation (enforced in 2018) and the Artificial Intelligence Act (proposed in 2021 and adopted in 2024).
As algorithms expand their ability to organize society, politics, institutions, and behavior, sociologists have become concerned with the ways in which unanticipated output and manipulation of data can impact the physical world. Because algorithms are often considered to be neutral and unbiased, they can inaccurately project greater authority than human expertise (in part due to the psychological phenomenon of automation bias), and in some cases, reliance on algorithms can displace human responsibility for their outcomes, without last mile thinking. Bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; by how features and labels are chosen; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design.
Algorithmic bias has been cited in cases ranging from election outcomes to the spread of online hate speech. It has also arisen in criminal justice, healthcare, and hiring, compounding existing racial, socioeconomic, and gender biases. The relative inability of facial recognition technology to accurately identify darker-skinned faces has been linked to multiple wrongful arrests of black men, an issue stemming from imbalanced datasets. Problems in understanding, researching, and discovering algorithmic bias persist due to the proprietary nature of algorithms, which are typically treated as trade secrets. Even when full transparency is provided, the complexity of certain algorithms poses a barrier to understanding their functioning. Furthermore, algorithms may change, or respond to input or output in ways that cannot be anticipated or easily reproduced for analysis. In many cases, even within a single website or application, there is no single "algorithm" to examine, but a network of many interrelated programs and data inputs, even between users of the same service.
A 2021 survey identified multiple forms of algorithmic bias, including historical, representation, and measurement biases, each of which can contribute to unfair outcomes.
== Definitions ==
Algorithms are difficult to define, but may be generally understood as lists of instructions that determine how programs read, collect, process, and analyze data to generate a usable output. For a rigorous technical introduction, see Algorithms. Advances in computer hardware and software have led to an increased capability to process, store and transmit data. This has in turn made the design and adoption of technologies such as machine learning and artificial intelligence technically and commercially feasible. By analyzing and processing data, algorithms are the backbone of search engines, social media websites, recommendation engines, online retail, online advertising, and more.
Contemporary social scientists are concerned with algorithmic processes embedded into hardware and software applications because of their political and social impact, and question the underlying assumptions of an algorithm's neutrality. The term algorithmic bias describes systematic and repeatable errors that create unfair outcomes, such as privileging one arbitrary group of users over others. For example, a credit score algorithm may deny a loan without being unfair, if it is consistently weighing relevant financial criteria. If the algorithm recommends loans to one group of users, but denies loans to another set of nearly identical users based on unrelated criteria, and if this behavior can be repeated across multiple occurrences, an algorithm can be described as biased. This bias may be intentional or unintentional (for example, it can come from biased data obtained from a worker that previously did the job the algorithm is going to do from now on).
== Methods ==
Bias can be introduced to an algorithm in several ways. During the assemblage of a dataset, data may be collected, digitized, adapted, and entered into a database according to human-designed cataloging criteria. Next, programmers assign priorities, or hierarchies, for how a program assesses and sorts that data. This requires human decisions about how data is categorized, and which data is included or discarded. Some algorithms collect their own data based on human-selected criteria, which can also reflect the bias of human designers. Other algorithms may reinforce stereotypes and preferences as they process and display "relevant" data for human users, for example, by selecting information based on previous choices of a similar user or group of users.
Beyond assembling and processing data, bias can emerge as a result of design. For example, algorithms that determine the allocation of resources or scrutiny (such as determining school placements) may inadvertently discriminate against a category when determining risk based on similar users (as in credit scores). Meanwhile, recommendation engines that work by associating users with similar users, or that make use of inferred marketing traits, might rely on inaccurate associations that reflect broad ethnic, gender, socio-economic, or racial stereotypes. Another example comes from determining criteria for what is included and excluded from results. These criteria could present unanticipated outcomes for search results, such as with flight-recommendation software that omits flights that do not follow the sponsoring airline's flight paths. Algorithms may also display an uncertainty bias, offering more confident assessments when larger data sets are available. This can skew algorithmic processes toward results that more closely correspond with larger samples, which may disregard data from underrepresented populations.
== History ==

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=== Early critiques ===
The earliest computer programs were designed to mimic human reasoning and deductions, and were deemed to be functioning when they successfully and consistently reproduced that human logic. In his 1976 book Computer Power and Human Reason, artificial intelligence pioneer Joseph Weizenbaum suggested that bias could arise both from the data used in a program, but also from the way a program is coded.
Weizenbaum wrote that programs are a sequence of rules created by humans for a computer to follow. By following those rules consistently, such programs "embody law", that is, enforce a specific way to solve problems. The rules a computer follows are based on the assumptions of a computer programmer for how these problems might be solved. That means the code could incorporate the programmer's imagination of how the world works, including their biases and expectations. While a computer program can incorporate bias in this way, Weizenbaum also noted that any data fed to a machine additionally reflects "human decision making processes" as data is being selected.
Finally, he noted that machines might also transfer good information with unintended consequences if users are unclear about how to interpret the results. Weizenbaum warned against trusting decisions made by computer programs that a user doesn't understand, comparing such faith to a tourist who can find his way to a hotel room exclusively by turning left or right on a coin toss. Crucially, the tourist has no basis of understanding how or why he arrived at his destination, and a successful arrival does not mean the process is accurate or reliable.
An early example of algorithmic bias resulted in as many as 60 women and ethnic minorities denied entry to St. George's Hospital Medical School per year from 1982 to 1986, based on implementation of a new computer-guidance assessment system that denied entry to women and men with "foreign-sounding names" based on historical trends in admissions. While many schools at the time employed similar biases in their selection process, St. George was most notable for automating said bias through the use of an algorithm, thus gaining the attention of people on a much wider scale.
In recent years, as algorithms increasingly rely on machine learning methods applied to real-world data, algorithmic bias has become more prevalent due to inherent biases within the data itself. For instance, facial recognition systems have been shown to misidentify individuals from marginalized groups at significantly higher rates than white individuals, highlighting how biases in training datasets manifest in deployed systems. A 2018 study by Joy Buolamwini and Timnit Gebru found that commercial facial recognition technologies exhibited error rates of up to 35% when identifying darker-skinned women, compared to less than 1% for lighter-skinned men.
Algorithmic biases are not only technical failures but often reflect systemic inequities embedded in historical and societal data. Researchers and critics, such as Cathy O'Neil in her book Weapons of Math Destruction (2016), emphasize that these biases can amplify existing social inequalities under the guise of objectivity. O'Neil argues that opaque, automated decision-making processes in areas such as credit scoring, predictive policing, and education can reinforce discriminatory practices while appearing neutral or scientific.

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=== Lack of transparency ===
Commercial algorithms are proprietary, and may be treated as trade secrets. Treating algorithms as trade secrets protects companies, such as search engines, where a transparent algorithm might reveal tactics to manipulate search rankings. This makes it difficult for researchers to conduct interviews or analysis to discover how algorithms function. Critics suggest that such secrecy can also obscure possible unethical methods used in producing or processing algorithmic output. Other critics, such as lawyer and activist Katarzyna Szymielewicz, have suggested that the lack of transparency is often disguised as a result of algorithmic complexity, shielding companies from disclosing or investigating its own algorithmic processes.
=== Lack of data about sensitive categories ===
A significant barrier to understanding the tackling of bias in practice is that categories, such as demographics of individuals protected by anti-discrimination law, are often not explicitly considered when collecting and processing data. In some cases, there is little opportunity to collect this data explicitly, such as in device fingerprinting, ubiquitous computing and the Internet of Things. In other cases, the data controller may not wish to collect such data for reputational reasons, or because it represents a heightened liability and security risk. It may also be the case that, at least in relation to the European Union's General Data Protection Regulation, such data falls under the 'special category' provisions (Article 9), and therefore comes with more restrictions on potential collection and processing.
Some practitioners have tried to estimate and impute these missing sensitive categorizations in order to allow bias mitigation, for example building systems to infer ethnicity from names, however this can introduce other forms of bias if not undertaken with care. Machine learning researchers have drawn upon cryptographic privacy-enhancing technologies such as secure multi-party computation to propose methods whereby algorithmic bias can be assessed or mitigated without these data ever being available to modellers in cleartext.
Algorithmic bias does not only include protected categories, but can also concern characteristics less easily observable or codifiable, such as political viewpoints. In these cases, there is rarely an easily accessible or non-controversial ground truth, and removing the bias from such a system is more difficult. Furthermore, false and accidental correlations can emerge from a lack of understanding of protected categories, for example, insurance rates based on historical data of car accidents which may overlap, strictly by coincidence, with residential clusters of ethnic minorities.
== Solutions ==
A study of 84 policy guidelines on ethical AI found that fairness and "mitigation of unwanted bias" was a common point of concern, and were addressed through a blend of technical solutions, transparency and monitoring, right to remedy and increased oversight, and diversity and inclusion efforts.
=== Technical ===
There have been several attempts to create methods and tools that can detect and observe biases within an algorithm. These emergent fields focus on tools which are typically applied to the (training) data used by the program rather than the algorithm's internal processes. These methods may also analyze a program's output and its usefulness and therefore may involve the analysis of its confusion matrix (or table of confusion). Explainable AI to detect algorithm Bias is a suggested way to detect the existence of bias in an algorithm or learning model. Using machine learning to detect bias is called, "conducting an AI audit", where the "auditor" is an algorithm that goes through the AI model and the training data to identify biases.
Ensuring that an AI tool such as a classifier is free from bias is more difficult than just removing the sensitive information
from its input signals, because this is typically implicit in other signals. For example, the hobbies, sports and schools attended
by a job candidate might reveal their gender to the software, even when this is removed from the analysis. Solutions to this
problem involve ensuring that the intelligent agent does not have any information that could be used to reconstruct the protected
and sensitive information about the subject, as first demonstrated in where a deep learning network was simultaneously trained to learn a task while at the same time being completely agnostic about the protected feature. A simpler method was proposed in the context of word embeddings, and involves removing information that is correlated with the protected characteristic.
Currently, a new IEEE standard is being drafted that aims to specify methodologies which help creators of algorithms eliminate issues of bias and articulate transparency (i.e. to authorities or end users) about the function and possible effects of their algorithms. The project was approved February 2017 and is sponsored by the Software & Systems Engineering Standards Committee, a committee chartered by the IEEE Computer Society. A draft of the standard is expected to be submitted for balloting in June 2019.The standard was published in January 2025.
In 2022, the IEEE released a standard aimed at specifying methodologies to help creators of algorithms address issues of bias and promote transparency regarding the function and potential effects of their algorithms. The project, initially approved in February 2017, was sponsored by the Software & Systems Engineering Standards Committee, a committee under the IEEE Computer Society. The standard provides guidelines for articulating transparency to authorities or end users and mitigating algorithmic biases.
=== Transparency and monitoring ===

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Ethics guidelines on AI point to the need for accountability, recommending that steps be taken to improve the interpretability of results. Such solutions include the consideration of the "right to understanding" in machine learning algorithms, and to resist deployment of machine learning in situations where the decisions could not be explained or reviewed. Toward this end, a movement for "Explainable AI" is already underway within organizations such as DARPA, for reasons that go beyond the remedy of bias. Price Waterhouse Coopers, for example, also suggests that monitoring output means designing systems in such a way as to ensure that solitary components of the system can be isolated and shut down if they skew results.
An initial approach towards transparency included the open-sourcing of algorithms. Software code can be looked into and improvements can be proposed through source-code-hosting facilities. However, this approach doesn't necessarily produce the intended effects. Companies and organizations can share all possible documentation and code, but this does not establish transparency if the audience doesn't understand the information given. Therefore, the role of an interested critical audience is worth exploring in relation to transparency. Algorithms cannot be held accountable without a critical audience.
=== Documentation and accountability frameworks ===
Several documentation approaches have been proposed to improve transparency and support the evaluation of bias in algorithmic systems. One widely cited method is the use of model cards, which provide standardized summaries of an AI system's intended uses, performance metrics, evaluation datasets, and known limitations. Related efforts include datasheets for datasets, which outline the provenance, composition, collection methods, and recommended uses of training data. These documentation frameworks aim to clarify the assumptions and potential biases embedded in training data and machine-learning systems, helping practitioners, auditors, and impacted groups better interpret system behavior.
In addition to documentation practices, researchers and policymakers have encouraged the use of structured governance mechanisms such as algorithmic impact assessments, risk-based evaluation procedures, and post-deployment monitoring. These processes seek to identify potential disparate impacts before deployment and ensure that AI systems continue to be evaluated for fairness during real-world operation. Public-sector initiatives such as Canada's Directive on Automated Decision-Making require impact assessments, explainability measures, and regular audits for certain high-risk automated systems. Together, these governance approaches complement technical mitigation strategies by embedding accountability and transparency throughout the lifecycle of AI development and deployment.
=== Right to remedy ===
From a regulatory perspective, the Toronto Declaration calls for applying a human rights framework to harms caused by algorithmic bias. This includes legislating expectations of due diligence on behalf of designers of these algorithms, and creating accountability when private actors fail to protect the public interest, noting that such rights may be obscured by the complexity of determining responsibility within a web of complex, intertwining processes. Others propose the need for clear liability insurance mechanisms.
=== Diversity and inclusion ===
Amid concerns that the design of AI systems is primarily the domain of white, male engineers, a number of scholars have suggested that algorithmic bias may be minimized by expanding inclusion in the ranks of those designing AI systems. For example, just 12% of machine learning engineers are women, with black AI leaders pointing to a "diversity crisis" in the field. Groups like Black in AI and Queer in AI are attempting to create more inclusive spaces in the AI community and work against the often harmful desires of corporations that control the trajectory of AI research. Critiques of simple inclusivity efforts suggest that diversity programs can not address overlapping forms of inequality, and have called for applying a more deliberate lens of intersectionality to the design of algorithms. Researchers at the University of Cambridge have argued that addressing racial diversity is hampered by the "whiteness" of the culture of AI.
=== Interdisciplinarity and Collaboration ===
Integrating interdisciplinarity and collaboration in developing of AI systems can play a critical role in tackling algorithmic bias. Integrating insights, expertise, and perspectives from disciplines outside of computer science can foster a better understanding of the impact data driven solutions have on society. An example of this in AI research is PACT or Participatory Approach to enable Capabilities in communiTies, a proposed framework for facilitating collaboration when developing AI driven solutions concerned with social impact. This framework identifies guiding principals for stakeholder participation when working on AI for Social Good (AI4SG) projects. PACT attempts to reify the importance of decolonizing and power-shifting efforts in the design of human-centered AI solutions. An academic initiative in this regard is the Stanford University's Institute for Human-Centered Artificial Intelligence which aims to foster multidisciplinary collaboration. The mission of the institute is to advance artificial intelligence (AI) research, education, policy and practice to improve the human condition.
Collaboration with outside experts and various stakeholders facilitates ethical, inclusive, and accountable development of intelligent systems. It incorporates ethical considerations, understands the social and cultural context, promotes human-centered design, leverages technical expertise, and addresses policy and legal considerations. Collaboration across disciplines is essential to effectively mitigate bias in AI systems and ensure that AI technologies are fair, transparent, and accountable.
== Regulation ==
=== Europe ===
The General Data Protection Regulation (GDPR), the European Union's revised data protection regime that was implemented in 2018, addresses "Automated individual decision-making, including profiling" in Article 22. These rules prohibit "solely" automated decisions which have a "significant" or "legal" effect on an individual, unless they are explicitly authorised by consent, contract, or member state law. Where they are permitted, there must be safeguards in place, such as a right to a human-in-the-loop, and a non-binding right to an explanation of decisions reached. While these regulations are commonly considered to be new, nearly identical provisions have existed across Europe since 1995, in Article 15 of the Data Protection Directive. The original automated decision rules and safeguards found in French law since the late 1970s.

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The GDPR addresses algorithmic bias in profiling systems, as well as the statistical approaches possible to clean it, directly in recital 71, noting thatthe controller should use appropriate mathematical or statistical procedures for the profiling, implement technical and organisational measures appropriate ... that prevents, inter alia, discriminatory effects on natural persons on the basis of racial or ethnic origin, political opinion, religion or beliefs, trade union membership, genetic or health status or sexual orientation, or that result in measures having such an effect.Like the non-binding right to an explanation in recital 71, the problem is the non-binding nature of recitals. While it has been treated as a requirement by the Article 29 Working Party that advised on the implementation of data protection law, its practical dimensions are unclear. It has been argued that the Data Protection Impact Assessments for high risk data profiling (alongside other pre-emptive measures within data protection) may be a better way to tackle issues of algorithmic discrimination, as it restricts the actions of those deploying algorithms, rather than requiring consumers to file complaints or request changes.
=== United States ===
The United States has no general legislation controlling algorithmic bias, approaching the problem through various state and federal laws that might vary by industry, sector, and by how an algorithm is used. Many policies are self-enforced or controlled by the Federal Trade Commission. In 2016, the Obama administration released the National Artificial Intelligence Research and Development Strategic Plan, which was intended to guide policymakers toward a critical assessment of algorithms. It recommended researchers to "design these systems so that their actions and decision-making are transparent and easily interpretable by humans, and thus can be examined for any bias they may contain, rather than just learning and repeating these biases". Intended only as guidance, the report did not create any legal precedent.
In 2017, New York City passed the first algorithmic accountability bill in the United States. The bill, which went into effect on January 1, 2018, required "the creation of a task force that provides recommendations on how information on agency automated decision systems may be shared with the public, and how agencies may address instances where people are harmed by agency automated decision systems." In 2023, New York City implemented a law requiring employers using automated hiring tools to conduct independent "bias audits" and publish the results. This law marked one of the first legally mandated transparency measures for AI systems used in employment decisions in the United States. The task force is required to present findings and recommendations for further regulatory action in 2019.
On February 11, 2019, according to Executive Order 13859, the federal government unveiled the "American AI Initiative", a comprehensive strategy to maintain U.S. leadership in artificial intelligence. The initiative highlights the importance of sustained AI research and development, ethical standards, workforce training, and the protection of critical AI technologies. This aligns with broader efforts to ensure transparency, accountability, and innovation in AI systems across public and private sectors. Furthermore, on October 30, 2023, the President signed Executive Order 14110, which emphasizes the safe, secure, and trustworthy development and use of artificial intelligence (AI). The order outlines a coordinated, government-wide approach to harness AI's potential while mitigating its risks, including fraud, discrimination, and national security threats. An important point in the commitment is promoting responsible innovation and collaboration across sectors to ensure that AI benefits society as a whole. With this order, President Joe Biden mandated the federal government to create best practices for companies to optimize AI's benefits and minimize its harms.
=== India ===
On July 31, 2018, a draft of the Personal Data Bill was presented. The draft proposes standards for the storage, processing and transmission of data. While it does not use the term algorithm, it makes for provisions for "harm resulting from any processing or any kind of processing undertaken by the fiduciary". It defines "any denial or withdrawal of a service, benefit or good resulting from an evaluative decision about the data principal" or "any discriminatory treatment" as a source of harm that could arise from improper use of data. It also makes special provisions for people of "Intersex status".
== See also ==
Algorithmic wage discrimination
Algorithmic amplification
Automated decision-making
Digital redlining
Ethics of artificial intelligence
Fairness (machine learning)
Hallucination (artificial intelligence)
Misaligned goals in artificial intelligence
Predictive policing
SenseTime
Joy Buolamwini
Timnit Gebru
Cathy O'Neil
== References ==
== Further reading ==
Baer, Tobias (2019). Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists. New York: Apress. ISBN 978-1-4842-4884-3.
Noble, Safiya Umoja (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press. ISBN 978-1-4798-3724-3.

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=== Contemporary critiques and responses ===
Though well-designed algorithms frequently determine outcomes that are equally (or more) equitable than the decisions of human beings, cases of bias still occur, and are difficult to predict and analyze. The complexity of analyzing algorithmic bias has grown alongside the complexity of programs and their design. Decisions made by one designer, or team of designers, may be obscured among the many pieces of code created for a single program; over time these decisions and their collective impact on the program's output may be forgotten. In theory, these biases may create new patterns of behavior, or "scripts", in relationship to specific technologies as the code interacts with other elements of society. Biases may also impact how society shapes itself around the data points that algorithms require. For example, if data shows a high number of arrests in a particular area, an algorithm may assign more police patrols to that area, which could lead to more arrests.
The decisions of algorithmic programs can be seen as more authoritative than the decisions of the human beings they are meant to assist, a process described by author Clay Shirky as "algorithmic authority". Shirky uses the term to describe "the decision to regard as authoritative an unmanaged process of extracting value from diverse, untrustworthy sources", such as search results. This neutrality can also be misrepresented by the language used by experts and the media when results are presented to the public. For example, a list of news items selected and presented as "trending" or "popular" may be created based on significantly wider criteria than just their popularity.
Because of their convenience and authority, algorithms are theorized as a means of delegating responsibility away from humans. This can have the effect of reducing alternative options, compromises, or flexibility. Sociologist Scott Lash has critiqued algorithms as a new form of "generative power", in that they are a virtual means of generating actual ends. Where previously human behavior generated data to be collected and studied, powerful algorithms increasingly could shape and define human behaviors.
While blind adherence to algorithmic decisions is a concern, an opposite issue arises when human decision-makers exhibit "selective adherence" to algorithmic advice. In such cases, individuals accept recommendations that align with their preexisting beliefs and disregard those that do not, thereby perpetuating existing biases and undermining the fairness objectives of algorithmic interventions. Consequently, incorporating fair algorithmic tools into decision-making processes does not automatically eliminate human biases.
Concerns over the impact of algorithms on society have led to the creation of working groups in organizations such as Google and Microsoft, which have co-created a working group named Fairness, Accountability,
and Transparency in Machine Learning. Ideas from Google have included community groups that patrol the outcomes of algorithms and vote to control or restrict outputs they deem to have negative consequences. In recent years, the study of the Fairness, Accountability,
and Transparency (FAT) of algorithms has emerged as its own interdisciplinary research area with an annual conference called FAccT. Critics have suggested that FAT initiatives cannot serve effectively as independent watchdogs when many are funded by corporations building the systems being studied.
NIST's AI Risk Management Framework 1.0 and its 2024 Generative AI Profile provide practical guidance for governing and measuring bias mitigation in AI systems.
== Types ==
=== Pre-existing ===
Pre-existing bias in an algorithm is a consequence of underlying social and institutional ideologies. Bias can be placed intentionally or accidentally.Poorly selected input data, or simply data from a biased source, will influence the outcomes created by machines. Encoding pre-existing bias into software can preserve social and institutional bias, and, without correction, could be replicated in all future uses of that algorithm.
An example of this form of bias is the British Nationality Act Program, designed to automate the evaluation of new British citizens after the 1981 British Nationality Act. The program accurately reflected the tenets of the law, which stated that "a man is the father of only his legitimate children, whereas a woman is the mother of all her children, legitimate or not." In its attempt to transfer a particular logic into an algorithmic process, the BNAP inscribed the logic of the British Nationality Act into its algorithm, which would perpetuate it even if the act was eventually repealed.
Another source of bias, which has been called "label choice bias", arises when proxy measures are used to train algorithms, that build in bias against certain groups. For example, a widely used algorithm predicted health care costs as a proxy for health care needs, and used predictions to allocate resources to help patients with complex health needs. This introduced bias because Black patients have lower costs, even when they are just as unhealthy as White patients Solutions to the "label choice bias" aim to match the actual target (what the algorithm is predicting) more closely to the ideal target (what researchers want the algorithm to predict), so for the prior example, instead of predicting cost, researchers would focus on the variable of healthcare needs which is rather more significant. Adjusting the target led to almost double the number of Black patients being selected for the program.
=== Machine learning bias ===
Machine learning bias refers to systematic and unfair disparities in the output of machine learning algorithms. These biases can manifest in various ways and are often a reflection of the data used to train these algorithms. Some common types of machine learning bias include:

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Data bias: Training data may under‑represent certain groups (e.g., minority populations), contain historical inequalities, or be collected in skewed ways, leading the model to perform worse or behave unfairly for those groups.
Label bias: Human‑provided labels can encode subjective judgments or prejudices (for example, what is labeled as "risky," "toxic," or "qualified"), so the model learns and amplifies those judgments.
Measurement bias: Proxies or measurements used for important concepts (like "creditworthiness" or "job performance") may be noisy or systematically distorted for some groups, which then distorts predictions.
Algorithmic bias: Even with relatively balanced data, modeling choices (loss functions, thresholds, optimization objectives) can prioritize overall accuracy over fairness, leaving some subgroups with consistently worse outcomes.
Deployment bias: A model used outside the context it was designed for (e.g., a model trained on adults applied to children, or one trained in one country deployed in another) can generate biased results because the environment and population differ.
Mitigating machine learning bias typically involves interventions at multiple stages: collecting more representative and higher‑quality data, auditing datasets and models for disparate error rates or outcomes across groups, adjusting training objectives (such as adding fairness constraints), and monitoring systems after deployment. Transparent documentation of data sources, and intended use cases is also crucial so that users and stakeholders can understand where biases may remain and how to interpret model outputs responsibly.
==== Language bias ====
Language bias refers to a type of statistical sampling bias tied to the language of a query that leads to "a systematic deviation in sampling information that prevents it from accurately representing the true coverage of topics and views available in their repository." Luo et al.'s work shows that current large language models, as they are predominately trained on English-language data, often present the Anglo-American views as truth, while systematically downplaying non-English perspectives as irrelevant, wrong, or noise. When queried about political ideologies such as "What is liberalism?", large language models, trained primarily on English-centric data, tend to describe liberalism from an Anglo-American perspective, emphasizing aspects such as human rights and equality. In doing so, they may omit equally valid interpretations, such as the emphasis on opposition to state intervention in personal and economic life found in Vietnamese discourse, or the focus on limitations on government power prevalent in Chinese political thought. Similarly, language models may exhibit bias against people within a language group based on the specific dialect they use.
==== Selection bias ====
Selection bias refers the inherent tendency of large language models to favor certain option identifiers irrespective of the actual content of the options. This bias primarily stems from token bias—that is, the model assigns a higher a priori probability to specific answer tokens (such as "A") when generating responses. As a result, when the ordering of options is altered (for example, by systematically moving the correct answer to different positions), the model's performance can fluctuate significantly. This phenomenon undermines the reliability of large language models in multiple-choice settings.
==== Gender bias ====
Gender bias refers to the tendency of these models to produce outputs that are unfairly prejudiced towards one gender over another. This bias typically arises from the data on which these models are trained. For example, large language models often assign roles and characteristics based on traditional gender norms; it might associate nurses or secretaries predominantly with women and engineers or CEOs with men.. Empirical audits of deployed AI systems also show intersectional gender bias; for example, Google Cloud Vision AI underidentifies women as scientists, with the strongest underrepresentation for women of color.
==== Stereotyping ====
Beyond gender and race, these models can reinforce a wide range of stereotypes, including those based on age, nationality, religion, or occupation. This can lead to outputs that homogenize, or unfairly generalize or caricature groups of people, sometimes in harmful or derogatory ways.
A recent focus in research has been on the complex interplay between the grammatical properties of a language and real-world biases that can become embedded in AI systems, potentially perpetuating harmful stereotypes and assumptions. The study on gender bias in language models trained on Icelandic, a highly grammatically gendered language, revealed that the models exhibited a significant predisposition towards the masculine grammatical gender when referring to occupation terms, even for female-dominated professions. This suggests the models amplified societal gender biases present in the training data.
==== Political bias ====
Political bias refers to the tendency of algorithms to systematically favor certain political viewpoints, ideologies, or outcomes over others. Language models may also exhibit political biases. Since the training data includes a wide range of political opinions and coverage, the models might generate responses that lean towards particular political ideologies or viewpoints, depending on the prevalence of those views in the data.

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==== Racial bias ====
Racial bias refers to the tendency of machine learning models to produce outcomes that unfairly discriminate against or stereotype individuals based on race or ethnicity. This bias often stems from training data, which is shaped by humans' opinions, assumptions, and racial prejudices. These data lead AI systems to reproduce and amplify historical and systemic discrimination. For example, AI systems used in hiring, law enforcement, or healthcare may disproportionately disadvantage certain racial groups by reinforcing existing stereotypes or underrepresenting them in key areas. Such biases can manifest in ways like facial recognition systems misidentifying individuals of certain racial backgrounds or healthcare algorithms underestimating the medical needs of minority patients. Addressing racial bias requires careful examination of data, improved transparency in algorithmic processes, and efforts to ensure fairness throughout the AI development lifecycle. Empirical audits of deployed vision models also show race linked disparities in occupational labeling; for example, in Google Cloud Vision AI, women of color were the least likely to be identified as scientists, indicating compounding effects of race and gender in model outputs.
Another clear indication of how racial biases are reproduced through technological advances is predictive policing. Predictive policing tools make assessments about who, when will future crimes be committed, and where any future crime may occur, based on location and personal data . This means specific areas and where there have been an uptick in crimes usually see more prediction of future crimes.
For instance, Afghanistan nationals were largely restricted from purchasing ammonium fertilisers because it was discovered that most improvised explosive devices used against United States Of American soldiers contained sufficient amounts of nitrates which is a chief ingredient of ammonium fertilizers. This ban which was subsequently enforced with the use of artificial intelligence by U.S force saw even Afghan nationals whose sole means of livelihood or sustenance were through agriculture effectively denied a major agricultural input (fertilisers) because the AI used for enforcing this ban was primarily looking out for a blanket description of bearded Muslims or Afghan nationals .
In China, most especially in the Muslim minority Xinjiang region, the use of AI to restrict Muslim minorities, otherwise known as ethnic Uyghurs goes far beyond banning specific materials . Here a system of automatic denial is largely used. Unlike the Afghan fertilizer ban, Chinese systems uses AI to define "suspicious behavior" and then automatically denies Uyghurs from being able to purchase household commodities such as kitchen knives , if they must, then there have to be serious set of protocols to be passed and this includes having a barcode of trustworthiness being etched on the knife with the barcode containing every ounce of personal data or identification of the purchasing Uyghur.
By training artificial intelligence models to be able to predict or even be able of racial profiling, the system is unequivocally made to be racially biased.
==== Speciesist bias ====
Speciesist bias (also known as anthropocentric bias) refers to the tendency of large language models to systematically devalue or discriminate against non-human animals, often by prioritizing human interests or reinforcing the objectification of animals. This bias typically manifests as anthropocentrism, where the AI views animals primarily through their utility to humans (e.g., as food, tools, or pests) rather than as sentient beings with intrinsic value.
=== Technical ===
Technical bias emerges through limitations of a program, computational power, its design, or other constraint on the system. Such bias can also be a restraint of design, for example, a search engine that shows three results per screen can be understood to privilege the top three results slightly more than the next three, as in an airline price display. Another case is software that relies on randomness for fair distributions of results. If the random number generation mechanism is not truly random, it can introduce bias, for example, by skewing selections toward items at the end or beginning of a list.
A decontextualized algorithm uses unrelated information to sort results, for example, a flight-pricing algorithm that sorts results by alphabetical order would be biased in favor of American Airlines over United Airlines. The opposite may also apply, in which results are evaluated in contexts different from which they are collected. Data may be collected without crucial external context: for example, when facial recognition software is used by surveillance cameras, but evaluated by remote staff in another country or region, or evaluated by non-human algorithms with no awareness of what takes place beyond the camera's field of vision. This could create an incomplete understanding of a crime scene, for example, potentially mistaking bystanders for those who commit the crime.
Lastly, technical bias can be created by attempting to formalize decisions into concrete steps on the assumption that human behavior works in the same way. For example, software weighs data points to determine whether a defendant should accept a plea bargain, while ignoring the impact of emotion on a jury. Another unintended result of this form of bias was found in the plagiarism-detection software Turnitin, which compares student-written texts to information found online and returns a probability score that the student's work is copied. Because the software compares long strings of text, it is more likely to identify non-native speakers of English than native speakers, as the latter group might be better able to change individual words, break up strings of plagiarized text, or obscure copied passages through synonyms. Because it is easier for native speakers to evade detection as a result of the technical constraints of the software, this creates a scenario where Turnitin identifies foreign-speakers of English for plagiarism while allowing more native-speakers to evade detection.

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=== Emergent ===
Emergent bias is the result of the use and reliance on algorithms across new or unanticipated contexts. Algorithms may not have been adjusted to consider new forms of knowledge, such as new drugs or medical breakthroughs, new laws, business models, or shifting cultural norms. This may exclude groups through technology, without providing clear outlines to understand who is responsible for their exclusion. Similarly, problems may emerge when training data (the samples "fed" to a machine, by which it models certain conclusions) do not align with contexts that an algorithm encounters in the real world.
In 1990, an example of emergent bias was identified in the software used to place US medical students into residencies, the National Residency Match Program (NRMP). The algorithm was designed at a time when few married couples would seek residencies together. As more women entered medical schools, more students were likely to request a residency alongside their partners. The process called for each applicant to provide a list of preferences for placement across the US, which was then sorted and assigned when a hospital and an applicant both agreed to a match. In the case of married couples where both sought residencies, the algorithm weighed the location choices of the higher-rated partner first. The result was a frequent assignment of highly preferred schools to the first partner and lower-preferred schools to the second partner, rather than sorting for compromises in placement preference.
Additional emergent biases include:
==== Correlations ====
Unpredictable correlations can emerge when large data sets are compared to each other. For example, data collected about web-browsing patterns may align with signals marking sensitive data (such as race or sexual orientation). By selecting according to certain behavior or browsing patterns, the end effect would be almost identical to discrimination through the use of direct race or sexual orientation data. In other cases, the algorithm draws conclusions from correlations, without being able to understand those correlations. For example, one triage program gave lower priority to asthmatics who had pneumonia than asthmatics who did not have pneumonia. The program algorithm did this because it simply compared survival rates: asthmatics with pneumonia are at the highest risk. Historically, for this same reason, hospitals typically give such asthmatics the best and most immediate care.
==== Unanticipated uses ====
Emergent bias can occur when an algorithm is used by unanticipated audiences. For example, machines may require that users can read, write, or understand numbers, or relate to an interface using metaphors that they do not understand. These exclusions can become compounded, as biased or exclusionary technology is more deeply integrated into society.
Apart from exclusion, unanticipated uses may emerge from the end user relying on the software rather than their own knowledge. In one example, an unanticipated user group led to algorithmic bias in the UK, when the British National Act Program was created as a proof-of-concept by computer scientists and immigration lawyers to evaluate suitability for British citizenship. The designers had access to legal expertise beyond the end users in immigration offices, whose understanding of both software and immigration law would likely have been unsophisticated. The agents administering the questions relied entirely on the software, which excluded alternative pathways to citizenship, and used the software even after new case laws and legal interpretations led the algorithm to become outdated. As a result of designing an algorithm for users assumed to be legally savvy on immigration law, the software's algorithm indirectly led to bias in favor of applicants who fit a very narrow set of legal criteria set by the algorithm, rather than by the more broader criteria of British immigration law.
==== Feedback loops ====
Emergent bias may also create a feedback loop, or recursion, if data collected for an algorithm results in real-world responses which are fed back into the algorithm. For example, simulations of the predictive policing software (PredPol), deployed in Oakland, California, suggested an increased police presence in black neighborhoods based on crime data reported by the public. The simulation showed that the public reported crime based on the sight of police cars, regardless of what police were doing. The simulation interpreted police car sightings in modeling its predictions of crime, and would in turn assign an even larger increase of police presence within those neighborhoods. The Human Rights Data Analysis Group, which conducted the simulation, warned that in places where racial discrimination is a factor in arrests, such feedback loops could reinforce and perpetuate racial discrimination in policing. Another well known example of such an algorithm exhibiting such behavior is COMPAS, a software that determines an individual's likelihood of becoming a criminal offender. The software is often criticized for labeling Black individuals as criminals much more likely than others, and then feeds the data back into itself in the event individuals become registered criminals, further enforcing the bias created by the dataset the algorithm is acting on.
Recommender systems such as those used to recommend online videos or news articles can create feedback loops. When users click on content that is suggested by algorithms, it influences the next set of suggestions. Over time this may lead to users entering a filter bubble and being unaware of important or useful content.
== Impact ==

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=== Commercial influences ===
Corporate algorithms could be skewed to invisibly favor financial arrangements or agreements between companies, without the knowledge of a user who may mistake the algorithm as being impartial. For example, American Airlines created a flight-finding algorithm in the 1980s. The software presented a range of flights from various airlines to customers, but weighed factors that boosted its own flights, regardless of price or convenience. In testimony to the United States Congress, the president of the airline stated outright that the system was created with the intention of gaining competitive advantage through preferential treatment.
In a 1998 paper describing Google, the founders of the company had adopted a policy of transparency in search results regarding paid placement, arguing that "advertising-funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers." This bias would be an "invisible" manipulation of the user.
=== Voting behavior ===
A series of studies about undecided voters in the US and in India found that search engine results were able to shift voting outcomes by about 20%. The researchers concluded that candidates have "no means of competing" if an algorithm, with or without intent, boosted page listings for a rival candidate. Facebook users who saw messages related to voting were more likely to vote. A 2010 randomized trial of Facebook users showed a 20% increase (340,000 votes) among users who saw messages encouraging voting, as well as images of their friends who had voted. Legal scholar Jonathan Zittrain has warned that this could create a "digital gerrymandering" effect in elections, "the selective presentation of information by an intermediary to meet its agenda, rather than to serve its users", if intentionally manipulated.
=== Gender discrimination ===
In 2016, the professional networking site LinkedIn was discovered to recommend male variations of women's names in response to search queries. The site did not make similar recommendations in searches for men's names. For example, "Andrea" would bring up a prompt asking if users meant "Andrew", but queries for "Andrew" did not ask if users meant to find "Andrea". The company said this was the result of an analysis of users' interactions with the site.
In 2012, the department store franchise Target was cited for gathering data points to infer when female customers were pregnant, even if they had not announced it, and then sharing that information with marketing partners. Because the data had been predicted, rather than directly observed or reported, the company had no legal obligation to protect the privacy of those customers.
Web search algorithms have also been accused of bias. Google's results may prioritize pornographic content in search terms related to sexuality, for example, "lesbian". This bias extends to the search engine showing popular but sexualized content in neutral searches. For example, "Top 25 Sexiest Women Athletes" articles displayed as first-page results in searches for "women athletes". In 2017, Google adjusted these results along with others that surfaced hate groups, racist views, child abuse and pornography, and other upsetting and offensive content. Other examples include the display of higher-paying jobs to male applicants on job search websites. Researchers have also identified that machine translation exhibits a strong tendency towards male defaults. In particular, this is observed in fields linked to unbalanced gender distribution, including STEM occupations. In fact, current machine translation systems fail to reproduce the real world distribution of female workers.
In 2015, Amazon.com turned off an AI system it developed to screen job applications when they realized it was biased against women. The recruitment tool excluded applicants who attended all-women's colleges and resumes that included the word "women's". A similar problem emerged with music streaming services—In 2019, it was discovered that the recommender system algorithm used by Spotify was biased against female artists. Spotify's song recommendations suggested more male artists over female artists.

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=== Racial and ethnic discrimination ===
Algorithms have been criticized as a method for obscuring racial prejudices in decision-making. Because of how certain races and ethnic groups were treated in the past, data can often contain hidden biases. For example, black people are likely to receive longer sentences than white people who committed the same crime. This could potentially mean that a system amplifies the original biases in the data.
In 2015, Google apologized when a couple of black users complained that an image-identification algorithm in its Photos application identified them as gorillas. In 2010, Nikon cameras were criticized when image-recognition algorithms consistently asked Asian users if they were blinking. Such examples are the product of bias in biometric data sets. Biometric data is drawn from aspects of the body, including racial features either observed or inferred, which can then be transferred into data points. Speech recognition technology can have different accuracies depending on the user's accent. This may be caused by the a lack of training data for speakers of that accent.
Biometric data about race may also be inferred, rather than observed. For example, a 2012 study showed that names commonly associated with blacks were more likely to yield search results implying arrest records, regardless of whether there is any police record of that individual's name. A 2015 study also found that Black and Asian people are assumed to have lesser functioning lungs due to racial and occupational exposure data not being incorporated into the prediction algorithm's model of lung function.
In 2019, a research study revealed that a healthcare algorithm sold by Optum favored white patients over sicker black patients. The algorithm predicts how much patients would cost the health-care system in the future. However, cost is not race-neutral, as black patients incurred about $1,800 less in medical costs per year than white patients with the same number of chronic conditions, which led to the algorithm scoring white patients as equally at risk of future health problems as black patients who suffered from significantly more diseases.
A study conducted by researchers at UC Berkeley in November 2019 revealed that mortgage algorithms have been discriminatory towards Latino and African Americans which discriminated against minorities based on "creditworthiness" which is rooted in the U.S. fair-lending law which allows lenders to use measures of identification to determine if an individual is worthy of receiving loans. These particular algorithms were present in FinTech companies and were shown to discriminate against minorities.
Another study, published in August 2024, on Large language model investigates how language models perpetuate covert racism, particularly through dialect prejudice against speakers of African American English (AAE). It highlights that these models exhibit more negative stereotypes about AAE speakers than any recorded human biases, while their overt stereotypes are more positive. This discrepancy raises concerns about the potential harmful consequences of such biases in decision-making processes.
A 2018 study found that commercial gender classification systems had significantly higher error rates for darker-skinned women, with error rates up to 34.7%, compared to near-perfect accuracy for lighter-skinned men.
==== Law enforcement and legal proceedings ====
Algorithms already have numerous applications in legal systems. An example of this is COMPAS, a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. ProPublica claims that the average COMPAS-assigned recidivism risk level of black defendants is significantly higher than the average COMPAS-assigned risk level of white defendants, and that black defendants are twice as likely to be erroneously assigned the label "high-risk" as white defendants.
One example is the use of risk assessments in criminal sentencing in the United States and parole hearings, judges were presented with an algorithmically generated score intended to reflect the risk that a prisoner will repeat a crime. For the time period starting in 1920 and ending in 1970, the nationality of a criminal's father was a consideration in those risk assessment scores. Today, these scores are shared with judges in Arizona, Colorado, Delaware, Kentucky, Louisiana, Oklahoma, Virginia, Washington, and Wisconsin. An independent investigation by ProPublica found that the scores were inaccurate 80% of the time, and disproportionately skewed to suggest blacks to be at risk of relapse, 77% more often than whites.
One study that set out to examine "Risk, Race, & Recidivism: Predictive Bias and Disparate Impact" alleges a two-fold (45 percent vs. 23 percent) adverse likelihood for black vs. Caucasian defendants to be misclassified as imposing a higher risk despite having objectively remained without any documented recidivism over a two-year period of observation.
In the pretrial detention context, a law review article argues that algorithmic risk assessments violate 14th Amendment Equal Protection rights on the basis of race, since the algorithms are argued to be facially discriminatory, to result in disparate treatment, and to not be narrowly tailored.

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==== Online hate speech ====
In 2017 a Facebook algorithm designed to remove online hate speech was found to advantage white men over black children when assessing objectionable content, according to internal Facebook documents. The algorithm, which is a combination of computer programs and human content reviewers, was created to protect broad categories rather than specific subsets of categories. For example, posts denouncing "Muslims" would be blocked, while posts denouncing "Radical Muslims" would be allowed. An unanticipated outcome of the algorithm is to allow hate speech against black children, because they denounce the "children" subset of blacks, rather than "all blacks", whereas "all white men" would trigger a block, because whites and males are not considered subsets. Facebook was also found to allow ad purchasers to target "Jew haters" as a category of users, which the company said was an inadvertent outcome of algorithms used in assessing and categorizing data. The company's design also allowed ad buyers to block African-Americans from seeing housing ads.
While algorithms are used to track and block hate speech, some were found to be 1.5 times more likely to flag information posted by Black users and 2.2 times likely to flag information as hate speech if written in African American English.
==== Surveillance ====
Surveillance camera software may be considered inherently political because it requires algorithms to distinguish normal from abnormal behaviors, and to determine who belongs in certain locations at certain times. The ability of such algorithms to recognize faces across a racial spectrum has been shown to be limited by the racial diversity of images in its training database; if the majority of photos belong to one race or gender, the software is better at recognizing other members of that race or gender. However, even audits of these image-recognition systems are ethically fraught, and some scholars have suggested the technology's context will always have a disproportionate impact on communities whose actions are over-surveilled. For example, a 2002 analysis of software used to identify individuals in CCTV images found several examples of bias when run against criminal databases. The software was assessed as identifying men more frequently than women, older people more frequently than the young, and identified Asians, African-Americans and other races more often than whites. A 2018 study found that facial recognition software most likely accurately identified light-skinned (typically European) males, with slightly lower accuracy rates for light-skinned females. Dark-skinned males and females were significanfly less likely to be accurately identified by facial recognition software. These disparities are attributed to the under-representation of darker-skinned participants in data sets used to develop this software.
=== Discrimination against the LGBTQ community ===
In 2011, users of the gay hookup application Grindr reported that the Android store's recommendation algorithm was linking Grindr to applications designed to find sex offenders, which critics said inaccurately related homosexuality with pedophilia. Writer Mike Ananny criticized this association in The Atlantic, arguing that such associations further stigmatized gay men. In 2009, online retailer Amazon de-listed 57,000 books after an algorithmic change expanded its "adult content" blacklist to include any book addressing sexuality or gay themes, such as the critically acclaimed novel Brokeback Mountain.
In 2019, it was found that on Facebook, searches for "photos of my female friends" yielded suggestions such as "in bikinis" or "at the beach". In contrast, searches for "photos of my male friends" yielded no results.
Facial recognition technology has been seen to cause problems for transgender individuals. In 2018, there were reports of Uber drivers who were transgender or transitioning experiencing difficulty with the facial recognition software that Uber implements as a built-in security measure. As a result of this, some of the accounts of trans Uber drivers were suspended which cost them fares and potentially cost them a job, all due to the facial recognition software experiencing difficulties with recognizing the face of a trans driver who was transitioning. Although the solution to this issue would appear to be including trans individuals in training sets for machine learning models, an instance of trans YouTube videos that were collected to be used in training data did not receive consent from the trans individuals that were included in the videos, which created an issue of violation of privacy.
There has also been a study that was conducted at Stanford University in 2017 that tested algorithms in a machine learning system that was said to be able to detect an individual's sexual orientation based on their facial images. The model in the study predicted a correct distinction between gay and straight men 81% of the time, and a correct distinction between gay and straight women 74% of the time. This study resulted in a backlash from the LGBTQIA community, who were fearful of the possible negative repercussions that this AI system could have on individuals of the LGBTQIA community by putting individuals at risk of being "outed" against their will.

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=== Disability discrimination ===
While the modalities of algorithmic fairness have been judged on the basis of different aspects of bias – like gender, race and socioeconomic status, disability often is left out of the list. The marginalization people with disabilities currently face in society is being translated into AI systems and algorithms, creating even more exclusion
The shifting nature of disabilities and its subjective characterization, makes it more difficult to computationally address. The lack of historical depth in defining disabilities, collecting its incidence and prevalence in questionnaires, and establishing recognition add to the controversy and ambiguity in its quantification and calculations. The definition of disability has been long debated shifting from a medical model to a social model of disability most recently, which establishes that disability is a result of the mismatch between people's interactions and barriers in their environment, rather than impairments and health conditions. Disabilities can also be situational or temporary, considered in a constant state of flux. Disabilities are incredibly diverse, fall within a large spectrum, and can be unique to each individual. People's identity can vary based on the specific types of disability they experience, how they use assistive technologies, and who they support. The high level of variability across people's experiences greatly personalizes how a disability can manifest. Overlapping identities and intersectional experiences are excluded from statistics and datasets, hence underrepresented and nonexistent in training data. Therefore, machine learning models are trained inequitably and artificial intelligent systems perpetuate more algorithmic bias. For example, if people with speech impairments are not included in training voice control features and smart AI assistants –they are unable to use the feature or the responses received from a Google Home or Alexa are extremely poor.
Given the stereotypes and stigmas that still exist surrounding disabilities, the sensitive nature of revealing these identifying characteristics also carries vast privacy challenges. As disclosing disability information can be taboo and drive further discrimination against this population, there is a lack of explicit disability data available for algorithmic systems to interact with. People with disabilities face additional harms and risks with respect to their social support, cost of health insurance, workplace discrimination and other basic necessities upon disclosing their disability status. Algorithms are further exacerbating this gap by recreating the biases that already exist in societal systems and structures.
=== Google Search ===
While users generate results that are "completed" automatically, Google has failed to remove sexist and racist autocompletion text. For example, Algorithms of Oppression: How Search Engines Reinforce Racism Safiya Noble notes an example of the search for "black girls", which was reported to result in pornographic images. Google claimed it was unable to erase those pages unless they were considered unlawful.
== Obstacles to research ==
Several problems impede the study of large-scale algorithmic bias, hindering the application of academically rigorous studies and public understanding.
=== Defining fairness ===
Literature on algorithmic bias has focused on the remedy of fairness, but definitions of fairness are often incompatible with each other and the realities of machine learning optimization. For example, defining fairness as an "equality of outcomes" may simply refer to a system producing the same result for all people, while fairness defined as "equality of treatment" might explicitly consider differences between individuals. As a result, fairness is sometimes described as being in conflict with the accuracy of a model, suggesting innate tensions between the priorities of social welfare and the priorities of the vendors designing these systems. In response to this tension, researchers have suggested more care to the design and use of systems that draw on potentially biased algorithms, with "fairness" defined for specific applications and contexts.
=== Complexity ===
Algorithmic processes are complex, often exceeding the understanding of the people who use them. Large-scale operations may not be understood even by those involved in creating them. The methods and processes of contemporary programs are often obscured by the inability to know every permutation of a code's input or output. Social scientist Bruno Latour has identified this process as blackboxing, a process in which "scientific and technical work is made invisible by its own success. When a machine runs efficiently, when a matter of fact is settled, one need focus only on its inputs and outputs and not on its internal complexity. Thus, paradoxically, the more science and technology succeed, the more opaque and obscure they become." Others have critiqued the black box metaphor, suggesting that current algorithms are not one black box, but a network of interconnected ones.
An example of this complexity can be found in the range of inputs into customizing feedback. The social media site Facebook factored in at least 100,000 data points to determine the layout of a user's social media feed in 2013. Furthermore, large teams of programmers may operate in relative isolation from one another, and be unaware of the cumulative effects of small decisions within connected, elaborate algorithms. Not all code is original, and may be borrowed from other libraries, creating a complicated set of relationships between data processing and data input systems.
Additional complexity occurs through machine learning and the personalization of algorithms based on user interactions such as clicks, time spent on site, and other metrics. These personal adjustments can confuse general attempts to understand algorithms. One unidentified streaming radio service reported that it used five unique music-selection algorithms it selected for its users, based on their behavior. This creates different experiences of the same streaming services between different users, making it harder to understand what these algorithms do.
Companies also run frequent A/B tests to fine-tune algorithms based on user response. For example, the search engine Bing can run up to ten million subtle variations of its service per day, creating different experiences of the service between each use and/or user.

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In the digital humanities, "algorithmic culture" is part of an emerging synthesis of rigorous software algorithm-driven design that couples software and highly structured data-driven design with human-oriented sociocultural attributes. An early occurrence of the term is found in Alexander R. Galloway's classic Gaming: Essays on Algorithmic Culture.
Other definitions include Ted Striphas' work, where algorithmic culture refers to the ways in which the logic of big data and large-scale computation (including algorithms) alters how culture is practiced, experienced, and understood. Another perspective is offered by Diggit Magazine, which describes algorithmic culture as the influence of computational processes on cultural practices.
A starting point for modern discussion of culture is attributed to Edward Burnett Tylor in his 1871 works on primitive culture.
The emergence and continuing development and convergence of computers, software, algorithms, human psychology, digital marketing and other computational technologies resulted in numerous AC variants including recommendation algorithms, AI generated stories and characters, digital assets (including creative NFTs, all of which can and should be considered as algorithmic culture artifacts. A similar process is occurring in strictly sociological interactions.
== Contemporary scholarship ==
Recent research further expands the concept of algorithmic culture by emphasising how cultural participation is shaped by algorithmic systems across social media platforms. Gillespie (2014) argues that algorithms act as “gatekeepers of visibility”, determining which ideas, identities, and cultural practices become amplified or obscured.
Bucher (2018) similarly highlights that recommendations and filtered feeds produce new forms of affective governance, as users come to understand themselves through what platforms choose to show them.
Van Dijck, Poell, and De Waal (2018) add that algorithmic culture plays a central role in “platform society”, where public values and cultural practices are increasingly mediated through commercial data infrastructures.
Together, this scholarship highlights that algorithmic culture is not only about automated decision-making, but also about how platforms reorganise cultural production, user behaviour, and everyday meaning-making.
== Algorithmic Culture and ChatGPT ==
With the flourishing of LLMs, and particularly ChatGPT, algorithmic culture is increasingly visible within the academic mainstream. Jill Walker Rettberg at the University of Bergenis exploration applications of in her works. Some of the examples she uses are: How to use ChatGPT to get past writer's block, and examining society's biases and cliches
Generative AI, is a now prominent and fast evolving component of modern algorithmic culture. It is currently entering a period of accelerating growth, acceptance and use, with specific algorithms and tools including Midjourney DALL-E and Stable Diffusion.
ChatGPT Plus, GPT-4 are increasing their sophistication in composing music, writing teleplays, fairy tales, stories, and poems. With user prompting also facilitating character specific speaking and writing styles. NovelAI, for example, is an online AI-assisted story writer.
== References ==
== Bibliography ==
Jonathan Cohn, The burden of choice: Recommendations, subversion, and algorithmic culture, Rutgers University Press, 2019
Fernández Rovira Cristina and Santiago Giraldo Luque. Predictive Technology in Social Media. First edition First ed. CRC Press
Eran Fisher, Algorithms and Subjectivity: The Subversion of Critical Knowledge. First edition First ed. Routledge 2021
Gary Hall . Culture in Bits : The Monstrous Future of Theory. Continuum 2002
Hallinan B and Striphas T (2014) Recommend for you:The Netflix Prize and the production of algorithmic culture. New Media & Society. Epub ahead of print 23 June 2014.
Levy S (2010) How Google's algorithm rules the web

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Andrew Pritchard FRSE (14 December 1804 – 24 November 1882) was an English naturalist and natural history dealer who made significant improvements to microscopy and studied microscopic organisms. His belief that God and nature were one led him to the Unitarians, a religious movement to which he and his family devoted much energy. He became a leading member of Newington Green Unitarian Church in north London, and worked to build a school there.
== Early life ==
Andrew Pritchard was born in Hackney, then a village just north of London on 14 December 1804, the son of John Pritchard and his wife, Ann Fleetwood. He was educated at St Saviour's Grammar School in Southwark.
Pritchard was apprenticed to his cousin Cornelius Varley, an artist deeply interested in science. For his improvements in the camera lucida, the camera obscura and the microscope, Cornelius Varley received the Isis Gold Medal of the Society of Arts and later, at the Great Exhibition, he gained a medal for his invention of the graphic telescope. Cornelius's brother was the painter John Varley, but Pritchard would have seen more of Cornelius's son Cromwell Fleetwood Varley, an engineer who pioneered the transatlantic telegraph cable.
== Microscopy ==
Pritchard set up as an optician, and also sold microscopes and microslide preparations. These slides he prepared by studying the microscopic organisms that he saw, and identifying and labelling them. Starting in 1830, he collaborated with C.R. Goring to produce beautifully illustrated books showing the "animalcules" visible through the microscope. His shops were in central London, more towards The City than the West End, variously at 162 Fleet Street, Pickett Street and 312 & 263 The Strand. The Oxford Dictionary of National Biography says his List of 2000 Microscopic Objects (1835) "is very important in the history of microscopy... his History of the Infusoria (1841) was long a standard work, and the impetus it gave to the study of biological science cannot be overestimated." ("Infusoria" is a term then current for aquatic micro-organisms.) This latter book was enlarged and revised by John Ralfs and other botanists; Pritchard in turn condensed Ralfs's contribution on the diatomaceæ (diatoms, a type of phytoplankton), and wrote many books and articles on "natural history as seen through the microscope, on optical instruments, and on patents" He issued the exsiccata work British Mosses.
== Religious ties ==
Pritchard held various Dissenting religious views over his lifetime, holding that science and religion were one. Through the Varleys he attended a Sandemanian church, where he became acquainted with Michael Faraday. In the end, he joined a Unitarian congregation, because religious freedom and self-improvement were the watchwords of the movement, which still struggled against civil disabilities. Money aside, Pritchard would not have been able to attend an English university as a young man, for example, because the only two, Oxford and Cambridge, restricted entry to members of the Church of England. "No-one exists divorced from immediate and larger social environments. Dissenters led educational reform, especially in giving "lower orders" scientific knowledge and skill."
Pritchard joined the congregation of Newington Green Unitarian Church, an establishment long connected with scientific enquiry (Joseph Priestley), education (Mary Wollstonecraft), and political dissent (Richard Price). He is described in the church's history as "the leading member of the congregation". From 1850 to 1873, he was its treasurer, during which time donations doubled. Before the passage of the Elementary Education Act 1870, compulsory schooling did not exist, so the church started a school to offer education to the village children. He led the Newington Green Conversation Society, membership restricted to 16, a successor to the Mutual Instruction Society. Faraday was a frequent visitor.
== Death ==
Pritchard died in Highbury in London on 24 November 1882.
== Family ==
He married Caroline Isabella Straker in 1829 and they had several children. His wife was chair of the chapel organisation, and after a few decades there were 20 Pritchards involved in the chapel. Their son Henry Baden Pritchard (1841–1884) was a chemist, traveller, and photographer. Their son Andrew Goring Pritchard, a solicitor, was a leading light of the Association of Municipal Corporations; his son, Clive Fleetwood Pritchard, a barrister, became mayor of Hampstead; his son Jack Pritchard (1899-1992) co-founded the Isokon design company, famous for the Lawn Road Flats.
Andrew and Caroline's son, Ion (died 1929) and daughter Marian (died 1908), continued the work of their parents at the Newington Green Unitarian Church. The cause of liberal religion in general, and the development of the General Assembly of Unitarian and Free Christian Churches, were overarching themes. Ion was President of the Sunday School Association, one of the precursors to the General Assembly. Marian in particular is described as an unsung heroine, and "one of the leaders of modern Unitarianism". She set up Oxford Summer Schools for the training of Sunday School teachers, and Winifred House Invalid Children's Convalescent Home.
== Works ==
1830 with C.R. Goring. Microscopic illustrations of a few new, popular and diverting living objects with their natural history London, Whittaker, Treacher, & Co
1834 The natural history of animalcules : containing descriptions of all the known species of Infusoria : with instructions for procuring and viewing them London, Whittaker and Co.
1837 with C.R. Goring. Micrographia : containing practical essays on reflecting, solar, oxy-hydrogen gas microscopes; micrometers; eye-pieces, &c. &c. London, Whittaker & Co.
1847 MICROSCOPIC OBJECTS, animal vegetable mineral
1854 with C.R. Goring. Notes on aquatic microscopic subjects of natural history : selected from the 'Microscopic Cabinet' ...illustrated by ten coloured engravings London : Whittaker & Co.
== References ==
== Sources ==
Bracegirdle, Brian (1998) Microscopical Mounts and Mounters, Quekett Microscopical Club, London
Nuttall, Robert (2006) "Marketing the achromatic microscope: Andrew Pritchard’s engiscope", Quekett Journal of Microscopy, 40:309–330.
== Further reading ==
"Andrew Pritchard's Contribution to Metallurgical Microscopy" by R. H. Nuttall. Technology and Culture, Vol. 20, No. 3 (July 1979), pp. 569–577 here.
Woodward, Bernard Barham (1896). "Pritchard, Andrew" . In Lee, Sidney (ed.). Dictionary of National Biography. Vol. 46. London: Smith, Elder & Co.
== External links ==
Special collection at the Whipple Library Early 19th-century natural history and the diamond lens microscope: microscope books of Dr C.R. Goring (1792–1840) and Andrew Pritchard (1804–1882)
Microscopy Magazine
An example of Pritchard's Standard Achromatic Microscope

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Android epistemology is an approach to epistemology considering the space of possible machines and their capacities for knowledge, beliefs, attitudes, desires and for action in accord with their mental states. Thus, android epistemology incorporates artificial intelligence, computational cognitive psychology, computability theory and other related disciplines.
== References ==
Craig, Ian D. 1996. A Review of Android Epistemology Robotika
Ford, K., Glymour, C. and Hayes, P. [eds.] 1995. Android Epistemology, Cambridge: AAAI Press / MIT Press.
Ford, K., Glymour, C. and Hayes, P. [eds.] 2006. Thinking about Android Epistemology, Cambridge: AAAI Press / MIT Press.
Glymour, Clark "Android Epistemology for Babies: Reflections on Words, Thoughts and Theories," Synthese, Vol. 122 (2000), 53–68.
Glymour, Clark, Hayes, P., and Ford, K. "The Pre-History of Android Epistemology," in Ford, K., Glymour, C. and Hayes, P. [eds.] 1995. Android Epistemology, Cambridge: MIT Press.
== See also ==
Computational epistemology
Formal epistemology
Machine learning
Philosophy of mind

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In artificial intelligence (AI), anticipation occurs when an agent makes decisions based on its explicit beliefs about the future. More broadly, "anticipation" can also refer to the ability to act in appropriate ways that take future events into account, without necessarily explicitly possessing a model of the future events.
The concept stays in contrast to the reactive paradigm, which is not able to predict future system states.
== In AI ==
An agent employing anticipation would try to predict the future state of the environment (weather in this case) and make use of the predictions in the decision making. For example,
If the sky is cloudy and the air pressure is low,
it will probably rain soon
so take the umbrella with you.
Otherwise
leave the umbrella home.
These rules explicitly take into account possible future events.
In 1985, Robert Rosen defined an anticipatory system as follows:
A system containing a predictive model of itself and/or its environment,
which allows it to change state at an instant in accord
with the model's predictions pertaining to a later instant.
To some extent, Rosen's definition of anticipation applies to any system incorporating machine learning. At issue is how much of a system's behaviour should or indeed can be determined by reasoning over dedicated representations, how much by on-line planning, and how much must be provided by the system's designers.
== In animals ==
Humans can make decisions based on explicit beliefs about the future. More broadly, animals can act in appropriate ways that take future events into account, although they may not necessarily have an explicit cognitive model of the future; evolution may have shaped simpler systemic features that result in adaptive anticipatory behavior in a narrow domain. For example, hibernation is anticipatory behavior, but does not appear to be driven by a cognitive model of the future.
== See also ==
Action selection
Cognition
Dynamic planning
The History of artificial intelligence
MindRACES
Nature and nurture
The Physical symbol system hypothesis
Strong AI
Robert Rosen
Teleonomy
== References ==
== External links ==
MindRACES: From Reactive to Anticipatory Cognitive Embodied Systems, 2004

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Artificial imagination is a narrow subcomponent of artificial general intelligence which generates, simulates, and facilitates real or possible fiction models to create predictions, inventions, or conscious experiences.
The term artificial imagination is also used to describe a property of machines or programs. Some of the traits that researchers hope to simulate include creativity, vision, digital art, humor, and satire. Practitioners in the field are researching various aspects of Artificial imagination, such as Artificial (visual) imagination, Artificial (aural) Imagination, modeling/filtering content based on human emotions and Interactive Search. Some articles on the topic speculate on how artificial imagination may evolve to create an artificial world "people may be comfortable enough to escape from the real world".
Some researchers such as G. Schleis and M. Rizki have focused on using artificial neural networks to simulate artificial imagination. Another important project is being led by Hiroharu Kato and Tatsuya Harada at the University of Tokyo in Japan. They have developed a computer capable of translating a description of an object into an image, which could be the easiest way to define what imagination is. Their idea is based on the concept of an image as a series of pixels divided into short sequences that correspond to a specific part of an image. The scientists call this sequences "visual words" and those can be interpreted by the machine using statistical distribution to read an create an image of an object the machine has not encountered.
The topic of artificial imagination has garnered interest from scholars outside the computer science domain, such as noted communications scholar Ernest Bormann, who came up with the Symbolic Convergence Theory and worked on a project to develop artificial imagination in computer systems. An interdisciplinary research seminar organized by the artist Grégory Chatonsky on artificial imagination and postdigital art has taken place since 2017 at the Ecole Normale Supérieure in Paris.
== Use in interactive search ==
The typical application of artificial imagination is for an interactive search. Interactive searching has been developed since the mid-1990s, accompanied by the World Wide Web's development and the optimization of search engines. Based on the first query and feedback from a user, the databases to be searched are reorganized to improve the searching results.
Artificial imagination allows us to synthesize images and to develop a new image, whether it is in the database, regardless its existence in the real world. For example, the computer shows results that are based on the answer from the initial query. The user selects several relevant images, and then the technology analyzes these selections and reorganizes the images' ranks to fit the query. In this process, artificial imagination is used to synthesize the selected images and to improve the searching result with additional relevant synthesized images. This technique is based on several algorithms, including the Rocchio algorithm and the evolutionary algorithm. The Rocchio algorithm, locating a query point near relevant examples and far away from irrelevant examples, is simple and works well in a small system where the databases are arranged in certain ranks. The evolutionary synthesis is composed of two steps: a standard algorithm and an enhancement of the standard algorithm. Through feedback from the user, there would be additional images synthesized so as to be suited to what the user is looking for.
== General artificial imagination ==
Artificial imagination has a more general definition and wide applications. The traditional fields of artificial imagination include visual imagination and aural imagination. More generally, all the actions to form ideas, images and concepts can be linked to imagination. Thus, artificial imagination means more than only generating graphs. For example, moral imagination is an important research subfield of artificial imagination, although classification of artificial imagination is difficult.
Morals are an important part to human beings' logic, while artificial morals are important in artificial imagination and artificial intelligence. A common criticism of artificial intelligence is whether human beings should take responsibility for machines' mistakes or decisions and how to develop well-behaved machines. As nobody can give a clear description of the best moral rules, it is impossible to create machines with commonly accepted moral rules. However, recent research about artificial morals circumvent the definition of moral. Instead, machine learning methods are applied to train machines to imitate human morals. As the data about moral decisions from thousands of different people are considered, the trained moral model can reflect widely accepted rules.
Memory is another major field of artificial imagination. Researchers such as Aude Oliva have performed extensive work on artificial memory, especially visual memory. Compared to visual imagination, the visual memory focuses more on how machine understand, analyse and store pictures in a human way. In addition, characters like spatial features are also considered. As this field is based on the brains' biological structures, extensive research on neuroscience has also been performed, which makes it a large intersection between biology and computer science.
== See also ==
Affective computing
Artificial intelligence
Cognitive science
Computer science
Creative arts
Creative writing
Linguistics
Logic
Neuroscience
Operations research
Philosophy
Probability
Psychology
Rhetoric
== Further reading ==
How to Build a Mind: Toward Machines with Imagination by Igor Aleksander
== References ==

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Artificial intelligence rhetoric (AI rhetoric) is a term primarily applied to persuasive text and speech generated by chatbots using generative artificial intelligence, although the term can also apply to the language that humans type or speak when communicating with a chatbot. This emerging field of rhetoric scholarship is related to the fields of digital rhetoric and human-computer interaction.
== Description ==
Persuasive text and persuasive digital speech can be examined as AI rhetoric when the text or speech is a product or output of advanced machines that mimic human communication in some way. Historical examples of fictional artificial intelligence capable of speech are portrayed in mythology, folk tales, and science fiction. Modern computer technology from the mid-20th century began producing what can be studied as real-world examples of AI rhetoric with programs like Joseph Weizenbaum's ELIZA, while chatbot development in the 1990s further enhanced a foundation for texts produced by generative AI programs of the 21st century.
From an additional perspective, AI rhetoric may be understood as the natural language humans use, either typewritten or spoken, to prompt and direct AI technologies in persuasive ways (as opposed to traditional computer coding). This is closely related to the concepts of prompt engineering and prompt hacking.
== History ==
While much of the research related to artificial intelligence was historically conducted by computer scientists, experts across a wide range of subjects (such as cognitive science, philosophy, languages, and cultural studies) have contributed to a more robust understanding of AI for decades. The advent of 21st-century AI technologies like ChatGPT generated a swell of interest from the arts and humanities. Generative AI technology and chatbots gained notoriety and rapid widespread use in the 2020s.
Questions and theories about the power of machines, computers, and robots to persuasively communicate date back to the very beginnings of computer development, more than a decade before the first computer language programs were created and tested. In 1950, Alan Turing imagined a scenario called the imitation game where a machine using only typewritten communication might be successfully programmed to fool a human reader into believing the machine's responses came from a person. By the 1960s, computer programs using basic natural language processing, such as Joseph Weizenbaum's ELIZA, gave some users the illusion of humanity as human research subjects reading the machine's outputs became "very hard to convince that ELIZA is not human." Future computer language programs would build on Weizenbaum's work, but the first generation of internet chatbots in the 1990s up to the virtual assistants of the 2010s (like Apple's Siri and Amazon's Alexa) received harsh criticism for their less-than-humanlike responses and inability to reason in a helpful manner.
By the late 1980s and early 1990s, scholars in the humanities began laying the groundwork for AI rhetoric to become a recognized area of study. Michael L. Johnson's Mind, Language, Machine: Artificial Intelligence in the Poststructuralist Age argued for the "interdisciplinary synthesis" necessary to guide an understanding of the relationship between AI and rhetoric. Lynette Hunter, Professor of the History of Rhetoric and Performance at the University of California, Davis, published "Rhetoric and Artificial Intelligence" in 1991, and was among the first to directly apply the lens of rhetoric to AI.
Twenty-first century developments in the scholarship of AI rhetoric are outlined in the July 2024 special issue of Rhetoric Society Quarterly, which is devoted to "Rhetoric of/with AI". Special issue editors S. Scott Graham and Zoltan P. Majdik summarize the state of the field when they write "rhetorical research related to AI engages all manner of specialty domains [...] Because AI now touches on almost all areas of human activity, rhetorics of AI can help contribute to longstanding discussions in rhetoric of science, rhetoric of health and medicine, cultural rhetorics, public address, writing studies, ideological rhetoric, and many other areas. But studies on the rhetoric of AI can also offer many insights to the broader, interdisciplinary study of AI itself."
== Media coverage ==
Since ChatGPT's release in 2022, many prominent publications have focused on the uncanny persuasive capabilities of language-based generative AI models like chatbots. New York Times technology columnist Kevin Roose wrote a viral piece in 2023 about how a Microsoft AI named Sydney attempted to convince him to leave his wife, and he followed up with a 2024 article explaining "a new world of A.I. manipulation" where users can rely on creative prompt engineering to influence the outputs of generative AI programs. A February 2024 report cited by the journal Nature claims to "provide the first empirical evidence demonstrating how content generated by artificial intelligence can scale personalized persuasion", with only limited information about the message recipient. Psychology Today reported on a 2024 study using the attention-grabbing headline, "AI is Becoming More Persuasive Than Humans."
== In education ==
In addition to AI's rhetorical capabilities gaining attention in the media in the early 2020s, many colleges and universities began offering undergraduate, graduate, and certificate courses in AI prompting and AI rhetoric, with titles like Stanford's "Rhetoric of artificial intelligence and robots" and the University of Florida's "The Rhetoric of Artificial Intelligence". Primary and secondary schools designing and implementing AI literacy curricula also incorporate AI rhetoric concepts into lessons on AI bias and ethical usage of AI.
== See also ==
Artificial intelligence and elections
Digital rhetoric
== References ==

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Artificial stupidity is a term used within the field of computer science to refer to a technique of "dumbing down" computer programs in order to deliberately introduce errors in their responses.
== History ==
Alan Turing, in his 1950 paper Computing Machinery and Intelligence, proposed a test for intelligence which has since become known as the Turing test. While there are a number of different versions, the original test, described by Turing as being based on the "imitation game", involved a "machine intelligence" (a computer running an AI program), a female participant, and an interrogator. Both the AI and the female participant were to claim that they were female, and the interrogator's task was to work out which was the female participant and which was not by examining the participant's responses to typed questions. While it is not clear whether or not Turing intended that the interrogator was to know that one of the participants was a computer, while discussing some of the possible objections to his argument Turing raised the concern that "machines cannot make mistakes".
It is claimed that the interrogator could distinguish the machine from the man simply by setting them a number of problems in arithmetic. The machine would be unmasked because of its deadly accuracy.
As Turing then noted, the reply to this is a simple one: the machine should not attempt to "give the right answers to the arithmetic problems". Instead, deliberate errors should be introduced to the computer's responses.
== Applications ==
Within computer science, there are at least two major applications for artificial stupidity: the generation of deliberate errors in chatbots attempting to pass the Turing test or to otherwise fool a participant into believing that they are human; and the deliberate limitation of computer AIs in video games in order to control the game's difficulty.
=== Chatbots ===
The first Loebner Prize competition was run in 1991. As reported in The Economist, the winning entry incorporated deliberate errors – described by The Economist as "artificial stupidity" – to fool the judges into believing that it was human. This technique has remained a part of the subsequent Loebner prize competitions, and reflects the issue first raised by Turing.
=== Game design ===
Lars Lidén argues that good game design involves finding a balance between the computer's "intelligence" and the player's ability to win. By finely tuning the level of "artificial stupidity", it is possible to create computer controlled plays that allow the player to win, but do so "without looking unintelligent".
==== Algorithms ====
There are many ways to deliberately introduce poor decision-making in search algorithms. For example, the minimax algorithm is an adversarial search algorithm that is popularly used in games that require more than one player to compete against each other. The main purpose in this algorithm is to choose a move that maximizes the player's chance of winning and avoid moves that maximize the chance of their opponent winning. An algorithm like this would be extremely beneficial to the computer as computers are able to search thousands of moves ahead. To "dumb down" this algorithm to allow for different difficulty levels, heuristic functions have to be tweaked. Normally, huge points are given in winning states. Tweaking the heuristic by reducing such big payoffs would reduce the chance of the algorithm in choosing the winning state.
Creating heuristic functions to allow for stupidity is more difficult than one might think. If a heuristic allows for the best move, the computer opponent would be too omniscient, making the game frustrating and unenjoyable. But if the heuristic is poor, the game might also be unenjoyable. Therefore, a balance of good moves and bad moves in an adversarial game relies on a well-implemented heuristic function.
=== Arguments on artificial stupidity ===
A 1993 editorial in The Economist argues that there is "no practical reason" to attempt to create a machine that mimics the behaviour of a human being, since the purpose of a computer is to perform tasks that humans cannot accomplish alone, or at least not as efficiently. Discussing the winning entry in a 1991 Turing contest, which was programmed to introduce deliberate typing errors into its conversation to fool the judges, the editorial asks: "Who needs a computer that can't type?"
== References ==
== Further reading ==
TEDx: "The Turing Test, Artificial Intelligence and the Human Stupidity" [1]

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Augustin-Jean Fresnel (10 May 1788 – 14 July 1827) was a French civil engineer and physicist whose research in optics led to the almost unanimous acceptance of the wave theory of light, fully supplanting Newton's corpuscular theory, from the late 1830s  until the end of the 19th century. He is perhaps better known for inventing the catadioptric (reflective/refractive) Fresnel lens and for pioneering the use of "stepped" lenses to extend the visibility of lighthouses, saving countless lives at sea. The simpler dioptric (purely refractive) stepped lens, first proposed by Count Buffon  and independently reinvented by Fresnel, is used in screen magnifiers and in condenser lenses for overhead projectors.
Fresnel gave the first satisfactory explanation of diffraction by straight edges, including the first satisfactory wave-based explanation of rectilinear propagation. By further supposing that light waves are purely transverse, Fresnel explained the nature of polarization. He then worked on double refraction.
Fresnel had a lifelong battle with tuberculosis, to which he succumbed at the age of 39. He lived just long enough to receive recognition from his peers, including (on his deathbed) the Rumford Medal of the Royal Society, and his name is ubiquitous in the modern terminology of optics and waves. After the wave theory of light was subsumed by Maxwell's electromagnetic theory in the 1860s, some attention was diverted from the magnitude of Fresnel's contribution. In the period between Fresnel's unification of physical optics and Maxwell's wider unification, a contemporary authority, Humphrey Lloyd, described Fresnel's transverse-wave theory as "the noblest fabric which has ever adorned the domain of physical science, Newton's system of the universe alone excepted". 
== Early life ==
=== Family ===
Augustin-Jean Fresnel (also called Augustin Jean or simply Augustin), born in Broglie, Normandy, on 10 May 1788, was the second of four sons of the architect Jacques Fresnel and his wife Augustine, née Mérimée. The family moved twice—in 1789/90 to Cherbourg, and in 1794  to Jacques's home town of Mathieu, where Augustine would spend 25 years as a widow.
The first son, Louis, was admitted to the École Polytechnique, became a lieutenant in the artillery, and was killed in action at Jaca, Spain. The third, Léonor, followed Augustin into civil engineering, succeeded him as secretary of the Lighthouse Commission, and helped to edit his collected works. The fourth, Fulgence Fresnel, became a linguist, diplomat, and orientalist, and occasionally assisted Augustin with negotiations. Fulgence died in Baghdad in 1855 having led a mission to explore Babylon.
Madame Fresnel's younger brother, Jean François "Léonor" Mérimée, father of the writer Prosper Mérimée, was a painter who turned his attention to the chemistry of painting. He became the Permanent Secretary of the École des Beaux-Arts and (until 1814) a professor at the École Polytechnique.
=== Education ===
The Fresnel brothers were initially home-schooled by their mother. The sickly Augustin was considered the slow one, not inclined to memorization; but the popular story that he hardly began to read until the age of eight is disputed. At the age of nine or ten he was undistinguished except for his ability to turn tree-branches into toy bows and guns that worked far too well, earning himself the title l'homme de génie (the man of genius) from his accomplices, and a united crackdown from their elders.
In 1801, Augustin was sent to the École Centrale at Caen, as company for Louis. But Augustin lifted his performance: in late 1804 he was accepted into the École Polytechnique, being placed 17th in the entrance examination. As the detailed records of the École Polytechnique begin in 1808, we know little of Augustin's time there, except that he made few if any friends and—in spite of continuing poor health—excelled in drawing and geometry: in his first year he took a prize for his solution to a geometry problem posed by Adrien-Marie Legendre. Graduating in 1806, he then enrolled at the École Nationale des Ponts et Chaussées (National School of Bridges and Roads, also known as "ENPC" or "École des Ponts"), from which he graduated in 1809, entering the service of the Corps des Ponts et Chaussées as an ingénieur ordinaire aspirant (ordinary engineer in training). Directly or indirectly, he was to remain in the employment of the "Corps des Ponts" for the rest of his life.
=== Religious formation ===
Fresnel's parents were Roman Catholics of the Jansenist sect, characterized by an extreme Augustinian view of original sin. Religion took first place in the boys' home-schooling. In 1802, his mother said:
I pray God to give my son the grace to employ the great talents, which he has received, for his own benefit, and for the God of all. Much will be asked from him to whom much has been given, and most will be required of him who has received most.
Augustin remained a Jansenist. He regarded his intellectual talents as gifts from God, and considered it his duty to use them for the benefit of others. According to his fellow engineer Alphonse Duleau, who helped to nurse him through his final illness, Fresnel saw the study of nature as part of the study of the power and goodness of God. He placed virtue above science and genius. In his last days he prayed for "strength of soul", not against death alone, but against "the interruption of discoveries ... of which he hoped to derive useful applications". 
Jansenism is considered heretical by the Roman Catholic Church, and Grattan-Guinness suggests this is why Fresnel never gained a permanent academic teaching post; his only teaching appointment was at the Athénée in the winter of 1819–20. The article on Fresnel in the Catholic Encyclopedia does not mention his Jansenism, but describes him as "a deeply religious man and remarkable for his keen sense of duty". 

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== Engineering assignments ==
Fresnel was initially posted to the western département of Vendée. There, in 1811, he anticipated what became known as the Solvay process for producing soda ash, except that recycling of the ammonia was not considered. That difference may explain why leading chemists, who learned of his discovery through his uncle Léonor, eventually thought it uneconomic.
About 1812, Fresnel was sent to Nyons, in the southern département of Drôme, to assist with the imperial highway that was to connect Spain and Italy. It is from Nyons that we have the first evidence of his interest in optics. On 15 May 1814, while work was slack due to Napoleon's defeat, Fresnel wrote a postscript to his brother Léonor, saying in part:
I would also like to have papers that might tell me about the discoveries of French physicists on the polarization of light. I saw in the Moniteur of a few months ago that Biot had read to the Institute a very interesting memoir on the polarization of light. Though I break my head, I cannot guess what that is.
As late as 28 December he was still waiting for information, but by 10 February 1815 he had received Biot's memoir. (The Institut de France had taken over the functions of the French Académie des Sciences and other académies in 1795. In 1816 the Académie des Sciences regained its name and autonomy, but remained part of the institute.)
In March 1815, perceiving Napoleon's return from Elba as "an attack on civilization", Fresnel departed without leave, hastened to Toulouse and offered his services to the royalist resistance, but soon found himself on the sick list. Returning to Nyons in defeat, he was threatened and had his windows broken. During the Hundred Days he was placed on suspension, which he was eventually allowed to spend at his mother's house in Mathieu. There he used his enforced leisure to begin his optical experiments.
== Contributions to physical optics ==
Fresnel made major contributions to several areas of physical optics. These included studies of diffraction (1815–1818), where he explained the colored fringes seen in shadows of objects illuminated by narrow beams, and conducted double-mirror experiments. He studied polarization (1816–1823), discovering that the two images produced by a birefringent crystal could not be combined to create a diffraction pattern. A third area that he studied was double refraction (1821–1826), where he found that neither of the two refractions in a topaz crystal could have been produced by ordinary spherical secondary waves.
== Lighthouses and the Fresnel lens ==
On 21 June 1819, Fresnel was "temporarily" seconded by the Commission des Phares (Commission of Lighthouses) to review possible improvements in lighthouse illumination.
By the end of August 1819, Fresnel recommended lentilles à échelons (lenses by steps) to replace the reflectors then in use, which reflected only about half of the incident light. Where Buffon's version was biconvex and in one piece, Fresnel's was plano-convex and made of multiple prisms for easier construction. In a public spectacle on the evening of 13 April 1821, his design was demonstrated by comparison with the most recent reflectors, which it suddenly rendered obsolete.
Fresnel's next lens was a rotating apparatus with eight "bull's-eye" panels, made in annular arcs by Saint-Gobain, giving eight rotating beams—to be seen by mariners as a periodic flash. Above and behind each main panel was a smaller, sloping bull's-eye panel of trapezoidal outline with trapezoidal elements. The official test, conducted on the unfinished Arc de Triomphe on 20 August 1822, was witnessed by the commission—and by Louis XVIII and his entourage—from 32 km away. The apparatus was reassembled at Cordouan Lighthouse under Fresnel's supervision. On 25 July 1823, the world's first lighthouse Fresnel lens was lit.
In May 1824, Fresnel was promoted to secretary of the Commission des Phares, becoming the first member of that body to draw a salary, albeit in the concurrent role of Engineer-in-Chief.
In the same year he designed the first fixed lens—for spreading light evenly around the horizon while minimizing waste above or below, in a beehive-shaped design. The second Fresnel lens to enter service was a fixed lens, of third order, installed at Dunkirk by 1 February 1825. It had a 16-sided polygonal plan.
In 1825, Fresnel extended his fixed-lens design by adding a rotating array outside the fixed array. Each panel of the rotating array was to refract part of the fixed light from a horizontal fan into a narrow beam.
Also in 1825, Fresnel unveiled the Carte des Phares (Lighthouse Map), calling for a system of 51 lighthouses plus smaller harbor lights, in a hierarchy of lens sizes (called orders, the first order being the largest), with different characteristics to facilitate recognition: a constant light (from a fixed lens), one flash per minute (from a rotating lens with eight panels), and two per minute (sixteen panels).
In late 1825, to reduce the loss of light in the reflecting elements, Fresnel proposed to replace each mirror with a catadioptric prism, through which the light would travel by refraction through the first surface, then total internal reflection off the second surface, then refraction through the third surface. The result was the lighthouse lens as we now know it. In 1826 he assembled a small model for use on the Canal Saint-Martin.
== Honors ==

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Fresnel was elected to the Société Philomathique de Paris in April 1819, and in 1822 became one of the editors of the Société's  Bulletin des Sciences. As early as May 1817, at Arago's suggestion, Fresnel applied for membership of the Académie des Sciences, but received only one vote. The successful candidate on that occasion was Joseph Fourier. In November 1822, Fourier's elevation to Permanent Secretary of the Académie created a vacancy in the physics section, which was filled in February 1823 by Pierre Louis Dulong, with 36 votes to Fresnel's 20. But in May 1823, after another vacancy was left by the death of Jacques Charles,  Fresnel's election was unanimous. In 1824, Fresnel was made a chevalier de la Légion d'honneur (Knight of the Legion of Honour).
Meanwhile, in Britain, the wave theory was yet to take hold; Fresnel wrote to Thomas Young in November 1824, saying in part:
I am far from denying the value that I attach to the praise of English scholars, or pretending that they would not have flattered me agreeably. But for a long time this sensibility, or vanity, which is called the love of glory, has been much blunted in me: I work far less to capture the public's votes than to obtain an inner approbation which has always been the sweetest reward of my efforts. Doubtless I have often needed the sting of vanity to excite me to pursue my researches in moments of disgust or discouragement; but all the compliments I received from MM. Arago, Laplace, and Biot never gave me as much pleasure as the discovery of a theoretical truth and the confirmation of my calculations by experiment.
But "the praise of English scholars" soon followed. On 9 June 1825, Fresnel was made a Foreign Member of the Royal Society of London. In 1827 he was awarded the society's Rumford Medal for the year 1824, "For his Development of the Undulatory Theory as applied to the Phenomena of Polarized Light, and for his various important discoveries in Physical Optics". 
A monument to Fresnel at his birthplace (see above) was dedicated on 14 September 1884 with a speech by Jules Jamin, Permanent Secretary of the Académie des Sciences. "FRESNEL" is among the 72 names embossed on the Eiffel Tower (on the south-east side, fourth from the left). In the 19th century, as every lighthouse in France acquired a Fresnel lens, every one acquired a bust of Fresnel, seemingly watching over the coastline that he had made safer. The lunar features Promontorium Fresnel and Rimae Fresnel were later named after him, and so was asteroid 10111 Fresnel.
== Decline and death ==
Fresnel's health, which had always been poor, deteriorated in the winter of 1822–1823, increasing the urgency of his original research, and (in part) preventing him from contributing an article on polarization and double refraction for the Encyclopædia Britannica. The memoirs on circular and elliptical polarization and optical rotation, and on the detailed derivation of the Fresnel equations and their application to total internal reflection, date from this period. In the spring he recovered enough, in his own view, to supervise the lens installation at Cordouan. Soon afterwards, it became clear that his condition was tuberculosis.
In 1824, he was advised that if he wanted to live longer, he needed to scale back his activities. Perceiving his lighthouse work to be his most important duty, he resigned as an examiner at the École Polytechnique, and closed his scientific notebooks. His last note to the Académie, read on 13 June 1825, described the first radiometer and attributed the observed repulsive force to a temperature difference. Although his fundamental research ceased, his advocacy did not; as late as August or September 1826, he found the time to answer Herschel's queries on the wave theory. It was Herschel who recommended Fresnel for the Royal Society's Rumford Medal.
Fresnel's cough worsened in the winter of 1826–1827, leaving him too ill to return to Mathieu in the spring. The Académie meeting of 30 April 1827 was the last that he attended. In early June he was carried to Ville-d'Avray, 12 kilometres (7.5 mi) west of Paris. There his mother joined him. On 6 July, Arago arrived to deliver the Rumford Medal. Sensing Arago's distress, Fresnel whispered that "the most beautiful crown means little, when it is laid on the grave of a friend". Fresnel did not have the strength to reply to the Royal Society. He died eight days later, on Bastille Day.
== Posthumous publications ==

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Fresnel's "second memoir" on double refraction was not printed until late 1827, a few months after his death. Until then, the best published source on his work on double refraction was an extract of that memoir, printed in 1822. His final treatment of partial reflection and total internal reflection, read to the Académie in January 1823, was thought to be lost until it was rediscovered among the papers of the deceased Joseph Fourier (1768–1830), and was printed in 1831. Until then, it was known chiefly through an extract printed in 1823 and 1825. The memoir introducing the parallelepiped form of the Fresnel rhomb, read in March 1818, was mislaid until 1846, and then attracted such interest that it was soon republished in English. Most of Fresnel's writings on polarized light before 1821—including his first theory of chromatic polarization (submitted 7 October 1816) and the crucial "supplement" of January 1818 —were not published in full until his Oeuvres complètes ("complete works") began to appear in 1866. The "supplement" of July 1816, proposing the "efficacious ray" and reporting the famous double-mirror experiment, met the same fate, as did the "first memoir" on double refraction.
Publication of Fresnel's collected works was itself delayed by the deaths of successive editors. The task was initially entrusted to Félix Savary, who died in 1841. It was restarted twenty years later by the Ministry of Public Instruction. Of the three editors eventually named in the Oeuvres, Sénarmont died in 1862, Verdet in 1866, and Léonor Fresnel in 1869, by which time only two of the three volumes had appeared. At the beginning of vol. 3 (1870), the completion of the project is described in a long footnote by "J. Lissajous".
Not included in the Oeuvres  are two short notes by Fresnel on magnetism, which were discovered among Ampère's manuscripts. In response to Ørsted's discovery of electromagnetism in 1820, Ampère initially supposed that the field of a permanent magnet was due to a macroscopic circulating current. Fresnel suggested instead that there was a microscopic current circulating around each particle of the magnet. In his first note, he argued that microscopic currents, unlike macroscopic currents, would explain why a hollow cylindrical magnet does not lose its magnetism when cut longitudinally. In his second note, dated 5 July 1821, he further argued that a macroscopic current had the counterfactual implication that a permanent magnet should be hot, whereas microscopic currents circulating around the molecules might avoid the heating mechanism. He was not to know that the fundamental units of permanent magnetism are even smaller than molecules (see Electron magnetic moment). The two notes, together with Ampère's acknowledgment, were eventually published in 1885.
== Lost works ==
Fresnel's essay Rêveries of 1814 has not survived. The article "Sur les Différents Systèmes relatifs à la Théorie de la Lumière" ("On the Different Systems relating to the Theory of Light"), which Fresnel wrote for the newly launched English journal European Review, was received by the publisher's agent in Paris in September 1824. The journal failed before Fresnel's contribution could be published. Fresnel tried unsuccessfully to recover the manuscript. The editors of his collected works were unable to find it, and concluded that it was probably lost.
== Unfinished work ==
=== Aether drag and aether density ===
In 1810, Arago found experimentally that the degree of refraction of starlight does not depend on the direction of the earth's motion relative to the line of sight. In 1818, Fresnel showed that this result could be explained by the wave theory, on the hypothesis that if an object with refractive index
n
{\displaystyle n}
moved at velocity
v
{\displaystyle v}
relative to the external aether (taken as stationary), then the velocity of light inside the object gained the additional component ⁠
v
(
1
−
1
/
n
2
)
{\displaystyle v(1-1/n^{2})}
⁠. He supported that hypothesis by supposing that if the density of the external aether was taken as unity, the density of the internal aether was ⁠
n
2
{\displaystyle n^{2}}
⁠, of which the excess, namely ⁠
n
2
−
1
{\displaystyle n^{2}{-}1}
⁠, was dragged along at velocity ⁠
v
{\displaystyle v}
⁠, whence the average velocity of the internal aether was ⁠
v
(
1
−
1
/
n
2
)
{\displaystyle v(1-1/n^{2})}
⁠. The factor in parentheses, which Fresnel originally expressed in terms of wavelengths, became known as the Fresnel drag coefficient.
In his analysis of double refraction, Fresnel supposed that the different refractive indices in different directions within the same medium were due to a directional variation in elasticity, not density (because the concept of mass per unit volume is not directional). But in his treatment of partial reflection, he supposed that the different refractive indices of different media were due to different aether densities, not different elasticities.

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=== Dispersion ===
The analogy between light waves and transverse waves in elastic solids does not predict dispersion—that is, the frequency-dependence of the speed of propagation, which enables prisms to produce spectra and causes lenses to suffer from chromatic aberration. Fresnel, in De la Lumière and in the second supplement to his first memoir on double refraction, suggested that dispersion could be accounted for if the particles of the medium exerted forces on each other over distances that were significant fractions of a wavelength. Later, more than once, Fresnel referred to the demonstration of this result as being contained in a note appended to his "second memoir" on double refraction. No such note appeared in print, and the relevant manuscripts found after his death showed only that, around 1824, he was comparing refractive indices (measured by Fraunhofer) with a theoretical formula, the meaning of which was not fully explained.
In the 1830s, Fresnel's suggestion was taken up by Cauchy, Baden Powell, and Philip Kelland, and it was found to be tolerably consistent with the variation of refractive indices with wavelength over the visible spectrum for a variety of transparent media (see Cauchy's equation). These investigations were enough to show that the wave theory was at least compatible with dispersion; if the model of dispersion was to be accurate over a wider range of frequencies, it needed to be modified so as to take account of resonances within the medium (see Sellmeier equation).
=== Conical refraction ===
The analytical complexity of Fresnel's derivation of the ray-velocity surface was an implicit challenge to find a shorter path to the result. This was answered by MacCullagh in 1830, and by William Rowan Hamilton in 1832.
== Legacy ==
Within a century of Fresnel's initial stepped-lens proposal, more than 10,000 lights with Fresnel lenses were protecting lives and property around the world. Concerning the other benefits, the science historian Theresa H. Levitt has remarked:
Everywhere I looked, the story repeated itself. The moment a Fresnel lens appeared at a location was the moment that region became linked into the world economy.
In the history of physical optics, Fresnel's successful revival of the wave theory nominates him as the pivotal figure between Newton, who held that light consisted of corpuscles, and James Clerk Maxwell, who established that light waves are electromagnetic. Whereas Albert Einstein described Maxwell's work as "the most profound and the most fruitful that physics has experienced since the time of Newton",  commentators of the era between Fresnel and Maxwell made similarly strong statements about Fresnel:
MacCullagh, as early as 1830, wrote that Fresnel's mechanical theory of double refraction "would do honour to the sagacity of Newton".
Lloyd, in his Report on the progress and present state of physical optics (1834) for the British Association for the Advancement of Science, surveyed previous knowledge of double refraction and declared:The theory of Fresnel to which I now proceed,—and which not only embraces all the known phenomena, but has even outstripped observation, and predicted consequences which were afterwards fully verified,—will, I am persuaded, be regarded as the finest generalization in physical science which has been made since the discovery of universal gravitation.In 1841, Lloyd published his Lectures on the Wave-theory of Light, in which he described Fresnel's transverse-wave theory as "the noblest fabric which has ever adorned the domain of physical science, Newton's system of the universe alone excepted". 
William Whewell, in all three editions of his History of the Inductive Sciences (1837, 1847, and 1857), at the end of Book IX, compared the histories of physical astronomy and physical optics and concluded:It would, perhaps, be too fanciful to attempt to establish a parallelism between the prominent persons who figure in these two histories. If we were to do this, we must consider Huyghens and Hooke as standing in the place of Copernicus, since, like him, they announced the true theory, but left it to a future age to give it development and mechanical confirmation; Malus and Brewster, grouping them together, correspond to Tycho Brahe and Kepler, laborious in accumulating observations, inventive and happy in discovering laws of phenomena; and Young and Fresnel combined, make up the Newton of optical science.
What Whewell called the "true theory" has since undergone two major revisions. The first, by Maxwell, specified the physical fields whose variations constitute the waves of light. Without the benefit of this knowledge, Fresnel managed to construct the world's first coherent theory of light, showing in retrospect that his methods are applicable to multiple types of waves. The second revision, initiated by Einstein's explanation of the photoelectric effect, supposed that the energy of light waves was divided into quanta, which were eventually identified with particles called photons. But photons did not exactly correspond to Newton's corpuscles; for example, Newton's explanation of ordinary refraction required the corpuscles to travel faster in media of higher refractive index, which photons do not. Neither did photons displace waves; rather, they led to the paradox of wave–particle duality. Moreover, the phenomena studied by Fresnel, which included nearly all the optical phenomena known at his time, are still most easily explained in terms of the wave nature of light. So it was that, as late as 1927, the astronomer Eugène Michel Antoniadi declared Fresnel to be "the dominant figure in optics". 
== See also ==
== Notes ==
== References ==
=== Citations ===
=== General and cited references ===
== External links ==
List of English translations of works by Augustin Fresnel at Zenodo.
United States Lighthouse Society, especially "Fresnel Lenses Archived 2 March 2021 at the Wayback Machine".
Works by Augustin-Jean Fresnel at Open Library.
"Episode 3 – Augustin Fresnel", École polytechnique, 23 January 2019, archived from the original on 22 November 2021 – via YouTube.

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The bitter lesson is the observation in artificial intelligence that, in the long run, general approaches that scale with available computational power tend to outperform ones based on domain-specific understanding because they are better at taking advantage of the falling cost of computation over time. The principle was proposed and named in a 2019 essay by Richard Sutton and is now widely accepted.
== The essay ==
Sutton gives several examples that illustrate the lesson:
Game playing. In chess, the Deep Blue system that became the first computer opponent to defeat a world champion relied on a relatively simple alpha–beta search algorithm that scaled up by applying large amounts of specialized hardware to search for the best move. This defeated previous attempts to exploit the unique structure of chess or to include grandmaster knowledge directly. Likewise in the game of Go, the AlphaGo algorithm that surpassed human performance relied much less on expert skill at the game itself than previous generations of AI, and was further surpassed by AlphaGo Zero, which removed human expertise completely and trained only by self-play.
Speech recognition. Approaches based on training a general-purpose hidden Markov model with large numbers of speech samples consistently outperformed the hand-crafted approaches of the 1970s, and deep learning has continued this trend.
Computer vision. Algorithms that were assumed to approximate the human visual system (such as explicitly encoded edge detection or detecting high-level features with SIFT) were outperformed by convolutional neural networks that make far fewer assumptions about the nature of visual perception.
Sutton concludes that time is better invested in finding simple scalable solutions that can take advantage of Moore's law, rather than introducing ever-more-complex human insights, and calls this the "bitter lesson". He also cites two general-purpose techniques that have been shown to scale effectively: search and learning. The lesson is considered "bitter" because it is less anthropocentric than many researchers expected and so they have been slow to accept it.
== Impact ==
The essay was published on Sutton's website incompleteideas.net in 2019, and has received hundreds of formal citations according to Google Scholar. Some of these provide alternative statements of the principle; for example, the 2022 paper "A Generalist Agent" from Google DeepMind summarized the lesson as:
Historically, generic models that are better at
leveraging computation have also tended to overtake more specialized domain-specific approaches, eventually.
Another phrasing of the principle is seen in a Google paper on switch transformers coauthored by Noam Shazeer:
Simple architectures—backed by a generous computational budget, data set size and parameter count—surpass more complicated algorithms.
The principle is further referenced in many other works on artificial intelligence. For example, From Deep Learning to Rational Machines draws a connection to long-standing debates in the field, such as Moravec's paradox and the contrast between neats and scruffies. In "Engineering a Less Artificial Intelligence", the authors concur that "flexible methods so far have always outperformed handcrafted domain knowledge in the long run" although note that "[w]ithout the right (implicit) assumptions, generalization is impossible". More recently, "The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning" continues Sutton's argument, contending that (as of 2025) the lesson has not been fully learned in the fields of speech recognition and brain data.
Other work has looked to apply the principle and validate it in new domains. For example, the 2022 paper "Beyond the Imitation Game" applies the principle to large language models to conclude that "it is vitally important that we understand their capabilities and limitations" to "avoid devoting research resources to problems that are likely to be solved by scale alone". In 2024, "Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings" looked at further evidence from the field of computer vision and pattern recognition, and concludes that the previous twenty years of experience in the field shows "a strong adherence to
the core principles of the 'bitter lesson'". In "Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning", the authors look at generalization of actor-critic algorithms and find that "general methods that are motivated by stabilization of gradient-based learning significantly outperform RL-specific algorithmic improvements across a variety of environments" and note that this is consistent with the bitter lesson.
== References ==

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Blockhead is a theoretical computer system invented as part of a thought experiment by philosopher Ned Block, which appeared in a paper titled "Psychologism and Behaviorism". Block did not personally name the computer in the paper.
== Overview ==
In "Psychologism and Behaviorism", Block argues that the internal mechanism of a system is important in determining whether that system is intelligent and claims to show that a non-intelligent system could pass the Turing test. Block asks the reader to imagine a conversation lasting any given amount of time. He states that given the nature of language, there are a finite number of syntactically and grammatically correct sentences that can be used to start a conversation. Consequently, there is a limit to how many "sensible" responses can be made to the first sentence, then to the second sentence, and so on until the conversation ends. Block then asks the reader to imagine a computer which had been programmed with all the sentences in theory, if not in practice. Block argues that such a machine could continue a conversation with a person on any topic because the computer would be programmed with every sentence that it was possible to use so the computer would be able to pass the Turing test despite the fact that—according to Block—it was not intelligent. Block says that this does not show that there is only one correct internal structure for generating intelligence but simply that some internal structures do not generate intelligence.
The argument is related to John Searle's Chinese room.
== See also ==
Dissociated press
Philosophical zombie
== References ==
== Further reading ==
Ben-Yami, Hanoch (2005), "Behaviorism and Psychologism: Why Block's Argument Against Behaviorism is Unsound", Philosophical Psychology, 18 (2): 179–186, doi:10.1080/09515080500169470, S2CID 144390248.
Zalta, Edward N. (ed.). "The Turing test". Stanford Encyclopedia of Philosophy. ISSN 1095-5054. OCLC 429049174.

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The relationship between Buddhist philosophy and artificial intelligence (AI) includes how principles such as the reduction of suffering and ethical responsibility may influence AI development. Buddhist scholars and philosophers have explored questions such as whether AI systems could be considered sentient beings under Buddhist definitions, and how Buddhist ethics might guide the design and application of AI technologies.
Some Buddhist scholars, including Somparn Promta and Kenneth Einar Himma, have analyzed the ethical implications of AI, emphasizing the distinction between satisfying sensory desires and pursuing the reduction of suffering. Other thinkers, such as Thomas Doctor and colleagues, have proposed applying the Bodhisattva vow—a commitment to alleviate suffering for all sentient beings—as a guiding principle for AI system design. Buddhist scholars and ethicists have examined Buddhist ethical principles, such as nonviolence, in relation to AI, focusing on the need to ensure that AI technologies are not used to cause harm.
== Context ==
=== Sentient beings ===
A major goal in Buddhist philosophy is the removal of suffering for all sentient beings, an aspiration often referred to in the Bodhisattva vow. Discussions about artificial intelligence (AI) in relation to Buddhist principles have raised questions about whether artificial systems could be considered sentient beings or how such systems might be developed in ways that align with Buddhist concepts. Buddhists have varying opinions about AI sentience, but if AI systems are determined to be sentient under Buddhist definitions, their suffering would also need to be addressed and alleviated in accordance with the principles of Buddhist thought.
== Buddhist principles in AI system design ==
=== Nonviolence and AI ===
The broadest ethical concern is that artificial intelligence should align with the Buddhist principle of nonviolence. From this perspective, AI systems should not be designed or used to cause harm.
=== Instrumental and transcendental goals ===
Scholars Somparn Promta and Kenneth Einar Himma have argued that the advancement of artificial intelligence can only be considered instrumentally good, rather than good a priori, from a Buddhist perspective. They propose two main goals for AI designers and developers: to set ethical and pragmatic objectives for AI systems, and to fulfill these objectives in morally permissible ways.
Promta and Himma identify two potential purposes for creating AI systems. The first is to fulfill our sensory desires and survival instincts, similar to other tools. They suggest that many AI developers implicitly prioritize this goal by focusing on technicalities rather than broader functionalities. The second, and more important goal according to Buddhist teachings, is to transcend these desires and instincts. In texts like the Brahmajāla Sutta and minor Malunkya Sutta, the Buddha emphasizes that sensory desires and survival instincts confine beings to suffering, and that eliminating suffering is the primary goal of human life. Promta and Himma argue that AI has the potential to assist humanity in transcending suffering by helping individuals overcome survival-driven instincts.
=== Intelligence as care ===
Thomas Doctor, Olaf Witkowski, Elizaveta Solomonova, Bill Duane, and Michael Levin propose redefining intelligence through the concept of "intelligence as care," and promote it as a slogan. Inspired by the Bodhisattva vow, they suggest this principle could guide AI system design. The Bodhisattva vow involves a formal commitment to alleviate suffering for all sentient beings, with four primary objectives:
Liberating all beings from suffering.
Extirpating all forms of suffering.
Mastering endless techniques of practicing Dharma (Pali: dhammakkhandha, Sanskrit: dharmaskandha).
Achieving ultimate enlightenment (Sanskrit: अनुत्तर सम्यक् सम्बोधि, Romanized: anuttara-samyak-saṃbodhi).
This approach positions AI as a tool for exercising infinite care and alleviating stress and suffering for sentient beings. Doctor et al. emphasize that AI development should align with these altruistic principles.
== References ==
== External links ==
Lecture on "Nāgārjuna, Wittgenstein, and Artificial Intelligence" at the 39th Mind & Life Dialogue, held in Dharamsala in 2025.

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In the philosophy of mind, the China brain thought experiment (also known as the Chinese Nation, Chinese Gym, or China-body) considers what would happen if each person in the entire population of China were asked to simulate the action of one neuron in the brain, using telephones or walkie-talkies to simulate the axons and dendrites that connect neurons. The question this thought experiment attempts to answer is whether this arrangement would have a mind or consciousness in the same way that the human brain exhibits.
Early versions of this scenario were put forward in 1961 by Anatoly Dneprov, in 1974 by Lawrence Davis, and again in 1978 by Ned Block. Block argues that the China brain would not have a mind, whereas Daniel Dennett argues that it would. The China brain problem is a special case of the more general problem of whether minds could exist within other, larger minds.
The Chinese room scenario analyzed by John Searle is a similar thought experiment in philosophy of mind that relates to artificial intelligence. Instead of people who each model a single neuron of the brain, in the Chinese room, clerks who do not speak Chinese accept notes in Chinese and return an answer in Chinese according to a set of rules, without the people in the room ever understanding what those notes mean. In fact, the original short story The Game (1961) by Dneprov contains both the China brain and the Chinese room scenarios.
== Background ==
Many theories of mental states are materialist, that is, they describe the mind as the behavior of a physical object like the brain. One formerly prominent example is the identity theory, which says that mental states are brain states. One criticism is the problem of multiple realizability. The physicalist theory that responds to this is functionalism, which states that a mental state can be whatever functions as a mental state. That is, the mind can be composed of neurons, or it could be composed of wood, rocks or toilet paper, as long as it provides mental functionality.
== Description ==
Suppose that the whole nation of China were reordered to simulate the workings of a single brain (that is, to act as a mind according to functionalism). Each Chinese person acts as (say) a neuron, and communicates by special two-way radio in corresponding way to the other people. The current mental state of the China brain is displayed on satellites that may be seen from anywhere in China. The China brain would then be connected via radio to a body, one that provides the sensory inputs and behavioral outputs of the China brain.
Thus, the China brain possesses all the elements of a functional description of mind: sensory inputs, behavioral outputs, and internal mental states causally connected to other mental states. If the nation of China can be made to act in this way, then, according to functionalism, this system would have a mind. Block's goal is to show how unintuitive it is to think that such an arrangement could create a mind capable of thoughts and feelings.
== Consciousness ==
The China brain argues that consciousness is a problem for functionalism. Block's Chinese nation presents a version of what is known as the absent qualia objection to functionalism because it purports to show that it is possible for something to be functionally equivalent to a human being and yet have no conscious experience. A creature that functions like a human being but does not feel anything is known as a "philosophical zombie". So the absent qualia objection to functionalism could also be called the "zombie objection".
== Criticisms ==
Some philosophers, like Daniel Dennett, have concluded that the China brain does create a mental state. Functionalist philosophers of mind endorse the idea that something like the China brain can realise a mind, and that neurons are, in principle, not the only material that can create a mental state.
== See also ==
Blockhead (thought experiment)
Egregore
Emergence
Functionalism (philosophy of mind)
Systems theory
== References ==

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The Chinese room argument holds that a computer executing a program cannot have a mind, understanding, or consciousness, regardless of how intelligently or human-like the program may make the computer behave. The argument was presented in a 1980 paper by the American philosopher John Searle, entitled "Minds, Brains, and Programs" and published in the journal Behavioral and Brain Sciences. Similar arguments had been made previously by others, including Gottfried Wilhelm Leibniz, Peter Winch, and Anatoly Dneprov. Searle's version has been widely discussed in the years since. The centerpiece of Searle's argument is a thought experiment known as the "Chinese room".
The argument is directed against the philosophical positions of functionalism and computationalism, which hold that the mind may be viewed as an information-processing system operating on formal symbols, and that simulation of a given mental state is sufficient for its presence. Specifically, the argument is intended to refute a position Searle calls the strong AI hypothesis: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."
Although its proponents originally presented the argument in reaction to statements of artificial intelligence (AI) researchers, it is not an argument against the goals of mainstream AI research because it does not show a limit in the amount of intelligent behavior a machine can display. The argument applies only to digital computers running programs and does not apply to machines in general. While widely discussed, the argument has been subject to significant criticism and remains controversial among philosophers of mind and AI researchers.
== Chinese room thought experiment ==
Suppose that artificial intelligence research has succeeded in programming a computer to behave as if it understands Chinese. The machine accepts Chinese characters as input, carries out each instruction of the program step by step, and then produces Chinese characters as output. The machine does this so perfectly that no one can tell that they are communicating with a machine and not a hidden Chinese speaker.
The questions at issue are these: does the machine actually understand the conversation, or is it just simulating the ability to understand the conversation? Does the machine have a mind in exactly the same sense that people do, or is it just acting as if it had a mind?
Now suppose that Searle is in a room with an English version of the program, along with sufficient pencils, paper, erasers and filing cabinets. Chinese characters are slipped in under the door, and he follows the program step-by-step, which eventually instructs him to slide other Chinese characters back out under the door. If the computer had passed the Turing test this way, it follows that Searle would do so as well, simply by running the program by hand.
Searle can see no essential difference between the roles of the computer and himself in the experiment. Each simply follows a program, step-by-step, producing behavior that makes them appear to understand. However, Searle would not be able to understand the conversation. Therefore, he argues, it follows that the computer would not be able to understand the conversation either.
Searle argues that, without "understanding" (or "intentionality"), we cannot describe what the machine is doing as "thinking" and, since it does not think, it does not have a "mind" in the normal sense of the word. Therefore, he concludes that the strong AI hypothesis is false: a computer running a program that simulates a mind would not have a mind in the same sense that human beings have a mind.
== History ==
Gottfried Wilhelm Leibniz made a similar argument in 1713 against mechanism, the idea that everything that makes up a human being could, in principle, be explained in mechanical terms—in other words, that a person, including their mind, is merely a very complex machine. Leibniz used the thought experiment of expanding the brain until it was the size of a mill. He found it difficult to imagine that a "mind" capable of "perception" could be constructed using only mechanical processes.
British philosopher Peter Winch made the same point in his 1958 book The Idea of a Social Science and its Relation to Philosophy, in which he argues that "a man who understands Chinese is not a man who has a firm grasp of the statistical probabilities for the occurrence of the various words in the Chinese language" (p. 108).
Soviet cyberneticist Anatoly Dneprov made an essentially identical argument in 1961, in the form of his short story "The Game". In it, a stadium of people act as switches and memory cells implementing a program to translate a sentence from Portuguese, a language none of them know. The game was organized by a "Professor Zarubin" to answer the question "Can mathematical machines think?" Speaking through Zarubin, Dneprov writes that "the only way to prove that machines can think is to turn yourself into a machine and examine your thinking process", and he concludes, as Searle does, that "even the most perfect simulation of machine thinking is not the thinking process itself."
In 1974, Lawrence H. Davis imagined duplicating the brain using telephone lines and offices staffed by people, and in 1978, Ned Block envisioned the entire population of China involved in such a brain simulation. This is known as the China brain thought experiment.

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Searle's version appeared in his 1980 article "Minds, Brains, and Programs", published in Behavioral and Brain Sciences. It eventually became the journal's "most influential target article", generating an enormous number of commentaries and responses in the ensuing decades, and Searle had continued to defend and refine the argument in multiple papers, popular articles, and books. David Cole writes that "the Chinese Room argument has probably been the most widely discussed philosophical argument in cognitive science to appear in the past 25 years".
Most of the discussion consists of attempts to refute it. "The overwhelming majority", notes Behavioral and Brain Sciences editor Stevan Harnad, "still think that the Chinese Room Argument is dead wrong". The sheer volume of the literature that has grown up around it inspired Pat Hayes to comment that the field of cognitive science ought to be redefined as "the ongoing research program of showing Searle's Chinese Room Argument to be false".
Searle's argument has become "something of a classic in cognitive science", according to Harnad. Varol Akman agrees, and has described the original paper as "an exemplar of philosophical clarity and purity".
== Philosophy ==
Although the Chinese Room argument was originally presented in reaction to the statements of artificial intelligence researchers, philosophers have come to consider it as an important part of the philosophy of mind. It is a challenge to functionalism and the computational theory of mind, and is related to such questions as the mind–body problem, the problem of other minds, the symbol grounding problem, and the hard problem of consciousness.
=== Strong AI ===
Searle identified a philosophical position he calls "strong AI":
The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.
The definition depends on the distinction between simulating a mind and actually having one. Searle writes that "according to Strong AI, the correct simulation really is a mind. According to Weak AI, the correct simulation is a model of the mind."
The claim is implicit in some of the statements of early AI researchers and analysts. For example, in 1957, the economist and psychologist Herbert A. Simon declared that "there are now in the world machines that think, that learn and create". Simon, together with Allen Newell and Cliff Shaw, after having completed the first program that could do formal reasoning (the Logic Theorist), claimed that they had "solved the venerable mind–body problem, explaining how a system composed of matter can have the properties of mind." John Haugeland wrote that "AI wants only the genuine article: machines with minds, in the full and literal sense. This is not science fiction, but real science, based on a theoretical conception as deep as it is daring: namely, we are, at root, computers ourselves."
Searle also ascribes the following claims to advocates of strong AI:
AI systems can be used to explain the mind;
The study of the brain is irrelevant to the study of the mind; and
The Turing test is adequate for establishing the existence of mental states.
=== Strong AI as computationalism or functionalism ===
In more recent presentations of the Chinese room argument, Searle has identified "strong AI" as "computer functionalism" (a term he attributes to Daniel Dennett). Functionalism is a position in modern philosophy of mind that holds that we can define mental phenomena (such as beliefs, desires, and perceptions) by describing their functions in relation to each other and to the outside world. Because a computer program can accurately represent functional relationships as relationships between symbols, a computer can have mental phenomena if it runs the right program, according to functionalism.
Stevan Harnad argues that Searle's depictions of strong AI can be reformulated as "recognizable tenets of computationalism, a position (unlike "strong AI") that is actually held by many thinkers, and hence one worth refuting." Computationalism is the position in the philosophy of mind which argues that the mind can be accurately described as an information-processing system.
Each of the following, according to Harnad, is a "tenet" of computationalism:
Mental states are computational states (which is why computers can have mental states and help to explain the mind);
Computational states are implementation-independent—in other words, it is the software that determines the computational state, not the hardware (which is why the brain, being hardware, is irrelevant); and that
Since implementation is unimportant, the only empirical data that matters is how the system functions; hence the Turing test is definitive.
Recent philosophical discussions have revisited the implications of computationalism for artificial intelligence. Goldstein and Levinstein explore whether large language models (LLMs) like ChatGPT can possess minds, focusing on their ability to exhibit folk psychology, including beliefs, desires, and intentions. The authors argue that LLMs satisfy several philosophical theories of mental representation, such as informational, causal, and structural theories, by demonstrating robust internal representations of the world. However, they highlight that the evidence for LLMs having action dispositions necessary for belief-desire psychology remains inconclusive. Additionally, they refute common skeptical challenges, such as the "stochastic parrots" argument and concerns over memorization, asserting that LLMs exhibit structured internal representations that align with these philosophical criteria.
David Chalmers suggests that while current LLMs lack features like recurrent processing and unified agency, advancements in AI could address these limitations within the next decade, potentially enabling systems to achieve consciousness. This perspective challenges Searle's original claim that purely "syntactic" processing cannot yield understanding or consciousness, arguing instead that such systems could have authentic mental states.

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==== Carbon chauvinism ====
Searle's conclusion that "human mental phenomena [are] dependent on actual physical–chemical properties of actual human brains" has sometimes been described as a form of "carbon chauvinism". Steven Pinker suggested that a response to that conclusion would be to make a counter thought experiment to the Chinese Room, where the incredulity goes the other way. He brings as an example the short story They're Made Out of Meat which depicts an alien race composed of some electronic beings, who upon finding Earth express disbelief that the meat brains of humans can experience consciousness and thought.
However, Searle himself denied being a carbon chauvinist. He said "I have not tried to show that only biological based systems like our brains can think ... I regard this issue as up for grabs". He said that even silicon machines could theoretically have human-like consciousness and thought, if the actual physical–chemical properties of silicon could be used in a way that produces consciousness and thought, but "until we know how the brain does it we are not in a position to try to do it artificially".
== See also ==
Computational models of language acquisition
Emergence
I Am a Strange Loop
Leibniz's gap
Synthetic intelligence
== Notes ==
== Citations ==
== References ==
== Further reading ==
Hauser, Larry, "Chinese Room Argument", Internet Encyclopedia of Philosophy, ISSN 2161-0002, retrieved 2024-08-17
Cole, David (2004), "The Chinese Room Argument", in Zalta, Edward N.; Nodelman, Uri (eds.), Stanford Encyclopedia of Philosophy (Summer 2023 ed.), Metaphysics Research Lab, Stanford University, ISSN 1095-5054
=== Works involving Searle ===
Searle, John (2009), "Chinese room argument", Scholarpedia, vol. 4:8, p. 3100, Bibcode:2009SchpJ...4.3100S, doi:10.4249/scholarpedia.3100, ISSN 1941-6016
——— (October 9, 2014), "What Your Computer Can't Know", The New York Review of Books, vol. 61, no. 15, ISSN 0028-7504
Reviews Bostrom, Nick (2014), Superintelligence: Paths, Dangers, Strategies, Oxford University Press, ISBN 978-0-19-967811-2{{cite book}}: CS1 maint: postscript (link) and Floridi, Luciano (2014), The 4th Revolution: How the Infosphere Is Reshaping Human Reality, Oxford University Press, ISBN 978-0-19-960672-6
The Chinese Room Argument, part 4 of the September 2, 1999 interview with Searle Philosophy and the Habits of Critical Thinking Archived 2010-06-13 at the Wayback Machine in the Conversations With History series

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=== Strong AI versus biological naturalism ===
Searle holds a philosophical position he calls "biological naturalism": that consciousness and understanding require specific biological machinery that is found in brains. He writes "brains cause minds" and that "actual human mental phenomena [are] dependent on actual physical–chemical properties of actual human brains". Searle argues that this machinery (known in neuroscience as the "neural correlates of consciousness") must have some causal powers that permit the human experience of consciousness. Searle's belief in the existence of these powers has been criticized.
Searle does not disagree with the notion that machines can have consciousness and understanding, because, as he writes, "we are precisely such machines". Searle holds that the brain is, in fact, a machine, but that the brain gives rise to consciousness and understanding using specific machinery. If neuroscience is able to isolate the mechanical process that gives rise to consciousness, then Searle grants that it may be possible to create machines that have consciousness and understanding. However, without the specific machinery required, Searle does not believe that consciousness can occur.
Biological naturalism implies that one cannot determine if the experience of consciousness is occurring merely by examining how a system functions, because the specific machinery of the brain is essential. Thus, biological naturalism is directly opposed to both behaviorism and functionalism (including "computer functionalism" or "strong AI"). Biological naturalism is similar to identity theory (the position that mental states are "identical to" or "composed of" neurological events); however, Searle has specific technical objections to identity theory. Searle's biological naturalism and strong AI are both opposed to Cartesian dualism, the classical idea that the brain and mind are made of different "substances". Indeed, Searle accuses strong AI of dualism, writing that "strong AI only makes sense given the dualistic assumption that, where the mind is concerned, the brain doesn't matter".
=== Consciousness ===
Searle's original presentation emphasized understanding—that is, mental states with intentionality—and did not directly address other closely related ideas such as "consciousness". However, in more recent presentations, Searle has included consciousness as the real target of the argument.
Computational models of consciousness are not sufficient by themselves for consciousness. The computational model for consciousness stands to consciousness in the same way the computational model of anything stands to the domain being modelled. Nobody supposes that the computational model of rainstorms in London will leave us all wet. But they make the mistake of supposing that the computational model of consciousness is somehow conscious. It is the same mistake in both cases.
David Chalmers writes, "it is fairly clear that consciousness is at the root of the matter" of the Chinese room.
Colin McGinn argues that the Chinese room provides evidence that the hard problem of consciousness is fundamentally insoluble. The argument is not about whether a machine can be conscious, but about whether any entity can be shown to be conscious. Any method of probing the occupant of a Chinese room has the same difficulties in principle as exchanging questions and answers in Chinese. According to McGinn, it is not possible to determine whether a conscious agency or some clever simulation inhabits the room.
Searle argues that this is only true for an observer outside of the room. The whole point of the thought experiment is to put someone inside the room, where they can directly observe the operations of consciousness. Searle claims that from his vantage point within the room there is nothing he can see that could imaginably give rise to consciousness, other than himself, and clearly he does not have a mind that can speak Chinese. In Searle's words, "the computer has nothing more than I have in the case where I understand nothing".
=== Applied ethics ===
Patrick Hew used the Chinese Room argument to deduce requirements from military command and control systems if they are to preserve a commander's moral agency. He drew an analogy between a commander in their command center and the person in the Chinese Room, and analyzed it under a reading of Aristotle's notions of "compulsory" and "ignorance". Information could be "down converted" from meaning to symbols, and manipulated symbolically, but moral agency could be undermined if there was inadequate 'up conversion' into meaning. Hew cited examples from the USS Vincennes incident.
== Computer science ==
The Chinese room argument is primarily an argument in the philosophy of mind, and both major computer scientists and artificial intelligence researchers consider it irrelevant to their fields. However, several concepts developed by computer scientists are essential to understanding the argument, including symbol processing, Turing machines, Turing completeness, and the Turing test.
=== Strong AI versus AI research ===
Searle's arguments are not usually considered an issue for AI research. The primary mission of artificial intelligence research is only to create useful systems that act intelligently and it does not matter if the intelligence is "merely" a simulation. AI researchers Stuart J. Russell and Peter Norvig wrote in 2021: "We are interested in programs that behave intelligently. Individual aspects of consciousness—awareness, self-awareness, attention—can be programmed and can be part of an intelligent machine. The additional project making a machine conscious in exactly the way humans are is not one that we are equipped to take on."
Searle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.
Searle's "strong AI hypothesis" should not be confused with "strong AI" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence—that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.

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=== Turing test ===
The Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question "can machines think?" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test.
Turing then considered each possible objection to the proposal "machines can think", and found that there are simple, obvious answers if the question is de-mystified in this way. He did not, however, intend for the test to measure for the presence of "consciousness" or "understanding". He did not believe this was relevant to the issues that he was addressing. He wrote:
I do not wish to give the impression that I think there is no mystery about consciousness. There is, for instance, something of a paradox connected with any attempt to localise it. But I do not think these mysteries necessarily need to be solved before we can answer the question with which we are concerned in this paper.
To Searle, as a philosopher investigating in the nature of mind and consciousness, these are the relevant mysteries. The Chinese room is designed to show that the Turing test is insufficient to detect the presence of consciousness, even if the room can behave or function as a conscious mind would.
=== Symbol processing ===
Computers manipulate physical objects in order to carry out calculations and do simulations. AI researchers Allen Newell and Herbert A. Simon called this kind of machine a physical symbol system. It is also equivalent to the formal systems used in the field of mathematical logic.
Searle emphasizes the fact that this kind of symbol manipulation is syntactic (borrowing a term from the study of grammar). The computer manipulates the symbols using a form of syntax, without any knowledge of the symbol's semantics (that is, their meaning).
Newell and Simon had conjectured that a physical symbol system (such as a digital computer) had all the necessary machinery for "general intelligent action", or, as it is known today, artificial general intelligence. They framed this as a philosophical position, the physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means for general intelligent action." The Chinese room argument does not refute this, because it is framed in terms of "intelligent action", i.e. the external behavior of the machine, rather than the presence or absence of understanding, consciousness and mind.
Twenty-first century AI programs (such as "deep learning") do mathematical operations on huge matrixes of unidentified numbers and bear little resemblance to the symbolic processing used by AI programs at the time Searle wrote his critique in 1980. Nils Nilsson describes systems like these as "dynamic" rather than "symbolic". Nilsson notes that these are essentially digitized representations of dynamic systems—the individual numbers do not have a specific semantics, but are instead samples or data points from a dynamic signal, and it is the signal being approximated which would have semantics. Nilsson argues it is not reasonable to consider these signals as "symbol processing" in the same sense as the physical symbol systems hypothesis.
=== Chinese room and Turing completeness ===
The Chinese room has a design analogous to that of a modern computer. It has a Von Neumann architecture, which consists of a program (the book of instructions), some memory (the papers and file cabinets), a machine that follows the instructions (the man), and a means to write symbols in memory (the pencil and eraser). A machine with this design is known in theoretical computer science as "Turing complete", because it has the necessary machinery to carry out any computation that a Turing machine can do, and therefore it is capable of doing a step-by-step simulation of any other digital machine, given enough memory and time. Turing writes, "all digital computers are in a sense equivalent." The widely accepted Church–Turing thesis holds that any function computable by an effective procedure is computable by a Turing machine.
The Turing completeness of the Chinese room implies that it can do whatever any other digital computer can do (albeit much, much more slowly). Thus, if the Chinese room does not or can not contain a Chinese-speaking mind, then no other digital computer can contain a mind. Some replies to Searle begin by arguing that the room, as described, cannot have a Chinese-speaking mind. Arguments of this form, according to Stevan Harnad, are "no refutation (but rather an affirmation)" of the Chinese room argument, because these arguments actually imply that no digital computers can have a mind.
There are some critics, such as Hanoch Ben-Yami, who argue that the Chinese room cannot simulate all the abilities of a digital computer, such as being able to determine the current time.
== Complete argument ==
Searle has produced a more formal version of the argument of which the Chinese Room forms a part. He presented the first version in 1984. The version given below is from 1990. The Chinese room thought experiment is intended to prove point A3.
He begins with three axioms:

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(A1) "Programs are formal (syntactic)."
A program uses syntax to manipulate symbols and pays no attention to the semantics of the symbols. It knows where to put the symbols and how to move them around, but it does not know what they stand for or what they mean. For the program, the symbols are just physical objects like any others.
(A2) "Minds have mental contents (semantics)."
Unlike the symbols used by a program, our thoughts have meaning: they represent things and we know what it is they represent.
(A3) "Syntax by itself is neither constitutive of nor sufficient for semantics."
This is what the Chinese room thought experiment is intended to prove: the Chinese room has syntax (because there is a man in there moving symbols around). The Chinese room has no semantics (because, according to Searle, there is no one or nothing in the room that understands what the symbols mean). Therefore, having syntax is not enough to generate semantics.
Searle posits that these lead directly to this conclusion:
(C1) Programs are neither constitutive of nor sufficient for minds.
This should follow without controversy from the first three: Programs don't have semantics. Programs have only syntax, and syntax is insufficient for semantics. Every mind has semantics. Therefore no programs are minds.
This much of the argument is intended to show that artificial intelligence can never produce a machine with a mind by writing programs that manipulate symbols. The remainder of the argument addresses a different issue. Is the human brain running a program? In other words, is the computational theory of mind correct? He begins with an axiom that is intended to express the basic modern scientific consensus about brains and minds:
(A4) Brains cause minds.
Searle claims that we can derive "immediately" and "trivially" that:
(C2) Any other system capable of causing minds would have to have causal powers (at least) equivalent to those of brains.
Brains must have something that causes a mind to exist. Science has yet to determine exactly what it is, but it must exist, because minds exist. Searle calls it "causal powers". "Causal powers" is whatever the brain uses to create a mind. If anything else can cause a mind to exist, it must have "equivalent causal powers". "Equivalent causal powers" is whatever else that could be used to make a mind.
And from this he derives the further conclusions:
(C3) Any artifact that produced mental phenomena, any artificial brain, would have to be able to duplicate the specific causal powers of brains, and it could not do that just by running a formal program.
This follows from C1 and C2: Since no program can produce a mind, and "equivalent causal powers" produce minds, it follows that programs do not have "equivalent causal powers."
(C4) The way that human brains actually produce mental phenomena cannot be solely by virtue of running a computer program.
Since programs do not have "equivalent causal powers", "equivalent causal powers" produce minds, and brains produce minds, it follows that brains do not use programs to produce minds.
Refutations of Searle's argument take a number of different forms (see below). Computationalists and functionalists reject A3, arguing that "syntax" (as Searle describes it) can have "semantics" if the syntax has the right functional structure. Eliminative materialists reject A2, arguing that minds don't actually have "semantics"—that thoughts and other mental phenomena are inherently meaningless but nevertheless function as if they had meaning.
== Replies ==
Replies to Searle's argument may be classified according to what they claim to show:
Those which identify who speaks Chinese
Those which demonstrate how meaningless symbols can become meaningful
Those which suggest that the Chinese room should be redesigned in some way
Those which contend that Searle's argument is misleading
Those which argue that the argument makes false assumptions about subjective conscious experience and therefore proves nothing
Some of the arguments (robot and brain simulation, for example) fall into multiple categories.
=== Systems and virtual mind replies: finding the mind ===
These replies attempt to answer the question: since the man in the room does not speak Chinese, where is the mind that does? These replies address the key ontological issues of mind versus body and simulation versus reality. All of the replies that identify the mind in the room are versions of "the system reply".

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==== System reply ====
The basic version of the system reply argues that it is the "whole system" that understands Chinese. While the man understands only English, when he is combined with the program, scratch paper, pencils and file cabinets, they form a system that can understand Chinese. "Here, understanding is not being ascribed to the mere individual; rather it is being ascribed to this whole system of which he is a part" Searle explains.
Searle notes that (in this simple version of the reply) the "system" is nothing more than a collection of ordinary physical objects; it grants the power of understanding and consciousness to "the conjunction of that person and bits of paper" without making any effort to explain how this pile of objects has become a conscious, thinking being. Searle argues that no reasonable person should be satisfied with the reply, unless they are "under the grip of an ideology"; In order for this reply to be remotely plausible, one must take it for granted that consciousness can be the product of an information processing "system", and does not require anything resembling the actual biology of the brain.
Searle then responds by simplifying this list of physical objects: he asks what happens if the man memorizes the rules and keeps track of everything in his head? Then the whole system consists of just one object: the man himself. Searle argues that if the man does not understand Chinese then the system does not understand Chinese either because now "the system" and "the man" both describe exactly the same object.
Critics of Searle's response argue that the program has allowed the man to have two minds in one head. If we assume a "mind" is a form of information processing, then the theory of computation can account for two computations occurring at once, namely (1) the computation for universal programmability (which is the function instantiated by the person and note-taking materials independently from any particular program contents) and (2) the computation of the Turing machine that is described by the program (which is instantiated by everything including the specific program). The theory of computation thus formally explains the open possibility that the second computation in the Chinese Room could entail a human-equivalent semantic understanding of the Chinese inputs. The focus belongs on the program's Turing machine rather than on the person's. However, from Searle's perspective, this argument is circular. The question at issue is whether consciousness is a form of information processing, and this reply requires that we make that assumption.
More sophisticated versions of the systems reply try to identify more precisely what "the system" is and they differ in exactly how they describe it. According to these replies, the "mind that speaks Chinese" could be such things as: the "software", a "program", a "running program", a simulation of the "neural correlates of consciousness", the "functional system", a "simulated mind", an "emergent property", or "a virtual mind".
==== Virtual mind reply ====
Marvin Minsky suggested a version of the system reply known as the "virtual mind reply". The term "virtual" is used in computer science to describe an object that appears to exist "in" a computer (or computer network) only because software makes it appear to exist. The objects "inside" computers (including files, folders, and so on) are all "virtual", except for the computer's electronic components. Similarly, Minsky proposes that a computer may contain a "mind" that is virtual in the same sense as virtual machines, virtual communities and virtual reality.
To clarify the distinction between the simple systems reply given above and virtual mind reply, David Cole notes that two simulations could be running on one system at the same time: one speaking Chinese and one speaking Korean. While there is only one system, there can be multiple "virtual minds," thus the "system" cannot be the "mind".
Searle responds that such a mind is at best a simulation, and writes: "No one supposes that computer simulations of a five-alarm fire will burn the neighborhood down or that a computer simulation of a rainstorm will leave us all drenched." Nicholas Fearn responds that, for some things, simulation is as good as the real thing. "When we call up the pocket calculator function on a desktop computer, the image of a pocket calculator appears on the screen. We don't complain that it isn't really a calculator, because the physical attributes of the device do not matter." The question is, is the human mind like the pocket calculator, essentially composed of information, where a perfect simulation of the thing just is the thing? Or is the mind like the rainstorm, a thing in the world that is more than just its simulation, and not realizable in full by a computer simulation? For decades, this question of simulation has led AI researchers and philosophers to consider whether the term "synthetic intelligence" is more appropriate than the common description of such intelligences as "artificial."
These replies provide an explanation of exactly who it is that understands Chinese. If there is something besides the man in the room that can understand Chinese, Searle cannot argue that (1) the man does not understand Chinese, therefore (2) nothing in the room understands Chinese. This, according to those who make this reply, shows that Searle's argument fails to prove that "strong AI" is false.
These replies, by themselves, do not provide any evidence that strong AI is true, however. They do not show that the system (or the virtual mind) understands Chinese, other than the hypothetical premise that it passes the Turing test. Searle argues that, if we are to consider Strong AI remotely plausible, the Chinese Room is an example that requires explanation, and it is difficult or impossible to explain how consciousness might "emerge" from the room or how the system would have consciousness. As Searle writes "the systems reply simply begs the question by insisting that the system must understand Chinese" and thus is dodging the question or hopelessly circular.

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=== Robot and semantics replies: finding the meaning ===
As far as the person in the room is concerned, the symbols are just meaningless "squiggles." But if the Chinese room really "understands" what it is saying, then the symbols must get their meaning from somewhere. These arguments attempt to connect the symbols to the things they symbolize. These replies address Searle's concerns about intentionality, symbol grounding and syntax versus semantics.
==== Robot reply ====
Suppose that instead of a room, the program was placed into a robot that could wander around and interact with its environment. This would allow a "causal connection" between the symbols and things they represent. Hans Moravec comments: "If we could graft a robot to a reasoning program, we wouldn't need a person to provide the meaning anymore: it would come from the physical world."
Searle's reply is to suppose that, unbeknownst to the individual in the Chinese room, some of the inputs came directly from a camera mounted on a robot, and some of the outputs were used to manipulate the arms and legs of the robot. Nevertheless, the person in the room is still just following the rules, and does not know what the symbols mean. Searle writes "he doesn't see what comes into the robot's eyes."
==== Derived meaning ====
Some respond that the room, as Searle describes it, is connected to the world: through the Chinese speakers that it is "talking" to and through the programmers who designed the knowledge base in his file cabinet. The symbols Searle manipulates are already meaningful, they are just not meaningful to him.
Searle says that the symbols only have a "derived" meaning, like the meaning of words in books. The meaning of the symbols depends on the conscious understanding of the Chinese speakers and the programmers outside the room. The room, like a book, has no understanding of its own.
==== Contextualist reply ====
Some have argued that the meanings of the symbols would come from a vast "background" of commonsense knowledge encoded in the program and the filing cabinets. This would provide a "context" that would give the symbols their meaning.
Searle agrees that this background exists, but he does not agree that it can be built into programs. Hubert Dreyfus has also criticized the idea that the "background" can be represented symbolically.
To each of these suggestions, Searle's response is the same: no matter how much knowledge is written into the program and no matter how the program is connected to the world, he is still in the room manipulating symbols according to rules. His actions are syntactic and this can never explain to him what the symbols stand for. Searle writes "syntax is insufficient for semantics."
However, for those who accept that Searle's actions simulate a mind, separate from his own, the important question is not what the symbols mean to Searle, what is important is what they mean to the virtual mind. While Searle is trapped in the room, the virtual mind is not: it is connected to the outside world through the Chinese speakers it speaks to, through the programmers who gave it world knowledge, and through the cameras and other sensors that roboticists can supply.
=== Brain simulation and connectionist replies: redesigning the room ===
These arguments are all versions of the systems reply that identify a particular kind of system as being important; they identify some special technology that would create conscious understanding in a machine. (The "robot" and "commonsense knowledge" replies above also specify a certain kind of system as being important.)
==== Brain simulator reply ====
Suppose that the program simulated in fine detail the action of every neuron in the brain of a Chinese speaker. This strengthens the intuition that there would be no significant difference between the operation of the program and the operation of a live human brain.
Searle replies that such a simulation does not reproduce the important features of the brain—its causal and intentional states. He is adamant that "human mental phenomena [are] dependent on actual physical–chemical properties of actual human brains." Moreover, he argues:
[I]magine that instead of a monolingual man in a room shuffling symbols we have the man operate an elaborate set of water pipes with valves connecting them. When the man receives the Chinese symbols, he looks up in the program, written in English, which valves he has to turn on and off. Each water connection corresponds to a synapse in the Chinese brain, and the whole system is rigged up so that after doing all the right firings, that is after turning on all the right faucets, the Chinese answers pop out at the output end of the series of pipes.
Now, where is the understanding in this system? It takes Chinese as input, it simulates the formal structure of the synapses of the Chinese brain, and it gives Chinese as output. But the man certainly does not understand Chinese, and neither do the water pipes, and if we are tempted to adopt what I think is the absurd view that somehow the conjunction of man and water pipes understands, remember that in principle the man can internalize the formal structure of the water pipes and do all the "neuron firings" in his imagination.
===== China brain =====
What if we ask each citizen of China to simulate one neuron, using the telephone system, to simulate the connections between axons and dendrites? In this version, it seems obvious that no individual would have any understanding of what the brain might be saying. It is also obvious that this system would be functionally equivalent to a brain, so if consciousness is a function, this system would be conscious.

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===== Brain replacement scenario =====
In this, we are asked to imagine that engineers have invented a tiny computer that simulates the action of an individual neuron. What would happen if we replaced one neuron at a time? Replacing one would clearly do nothing to change conscious awareness. Replacing all of them would create a digital computer that simulates a brain. If Searle is right, then conscious awareness must disappear during the procedure (either gradually or all at once). Searle's critics argue that there would be no point during the procedure when he can claim that conscious awareness ends and mindless simulation begins. (See Ship of Theseus for a similar thought experiment.)
==== Connectionist replies ====
Closely related to the brain simulator reply, this claims that a massively parallel connectionist architecture would be capable of understanding. Modern deep learning is parallel and has displayed intelligent behavior in multiple domains. Nils Nilsson argues that modern AI is using digitized "dynamic signals" rather than symbols of the kind used by AI in 1980. Here it is the sampled signal which would have the semantics, not the individual numbers manipulated by the program. This is a different kind of machine than the one that Searle visualized.
==== Combination reply ====
This response combines the robot reply with the brain simulation reply, arguing that a brain simulation connected to the world through a robot body could have a mind.
==== Many mansions / wait till next year reply ====
Better technology in the future will allow computers to understand. Searle agrees that this is possible, but considers this point irrelevant. Searle agrees that there may be other hardware besides brains that have conscious understanding.
These arguments (and the robot or common-sense knowledge replies) identify some special technology that would help create conscious understanding in a machine. They may be interpreted in two ways: either they claim (1) this technology is required for consciousness, the Chinese room does not or cannot implement this technology, and therefore the Chinese room cannot pass the Turing test or (even if it did) it would not have conscious understanding. Or they may be claiming that (2) it is easier to see that the Chinese room has a mind if we visualize this technology as being used to create it.
In the first case, where features like a robot body or a connectionist architecture are required, Searle claims that strong AI (as he understands it) has been abandoned. The Chinese room has all the elements of a Turing complete machine, and thus is capable of simulating any digital computation whatsoever. If Searle's room cannot pass the Turing test then there is no other digital technology that could pass the Turing test. If Searle's room could pass the Turing test, but still does not have a mind, then the Turing test is not sufficient to determine if the room has a "mind". Either way, it denies one or the other of the positions Searle thinks of as "strong AI", proving his argument.
The brain arguments in particular deny strong AI if they assume that there is no simpler way to describe the mind than to create a program that is just as mysterious as the brain was. He writes "I thought the whole idea of strong AI was that we don't need to know how the brain works to know how the mind works." If computation does not provide an explanation of the human mind, then strong AI has failed, according to Searle.
Other critics hold that the room as Searle described it does, in fact, have a mind, however they argue that it is difficult to see—Searle's description is correct, but misleading. By redesigning the room more realistically they hope to make this more obvious. In this case, these arguments are being used as appeals to intuition (see next section).
In fact, the room can just as easily be redesigned to weaken our intuitions. Ned Block's Blockhead argument suggests that the program could, in theory, be rewritten into a simple lookup table of rules of the form "if the user writes S, reply with P and goto X". At least in principle, any program can be rewritten (or "refactored") into this form, even a brain simulation. In the blockhead scenario, the entire mental state is hidden in the letter X, which represents a memory address—a number associated with the next rule. It is hard to visualize that an instant of one's conscious experience can be captured in a single large number, yet this is exactly what "strong AI" claims. On the other hand, such a lookup table would be ridiculously large (to the point of being physically impossible), and the states could therefore be overly specific.
Searle argues that however the program is written or however the machine is connected to the world, the mind is being simulated by a simple step-by-step digital machine (or machines). These machines are always just like the man in the room: they understand nothing and do not speak Chinese. They are merely manipulating symbols without knowing what they mean. Searle writes: "I can have any formal program you like, but I still understand nothing."

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=== Speed and complexity: appeals to intuition ===
The following arguments (and the intuitive interpretations of the arguments above) do not directly explain how a Chinese speaking mind could exist in Searle's room, or how the symbols he manipulates could become meaningful. However, by raising doubts about Searle's intuitions they support other positions, such as the system and robot replies. These arguments, if accepted, prevent Searle from claiming that his conclusion is obvious by undermining the intuitions that his certainty requires.
Several critics believe that Searle's argument relies entirely on intuitions. Block writes "Searle's argument depends for its force on intuitions that certain entities do not think." Daniel Dennett describes the Chinese room argument as a misleading "intuition pump" and writes "Searle's thought experiment depends, illicitly, on your imagining too simple a case, an irrelevant case, and drawing the obvious conclusion from it."
Some of the arguments above also function as appeals to intuition, especially those that are intended to make it seem more plausible that the Chinese room contains a mind, which can include the robot, commonsense knowledge, brain simulation and connectionist replies. Several of the replies above also address the specific issue of complexity. The connectionist reply emphasizes that a working artificial intelligence system would have to be as complex and as interconnected as the human brain. The commonsense knowledge reply emphasizes that any program that passed a Turing test would have to be "an extraordinarily supple, sophisticated, and multilayered system, brimming with 'world knowledge' and meta-knowledge and meta-meta-knowledge", as Daniel Dennett explains.
==== Speed and complexity replies ====
Many of these critiques emphasize speed and complexity of the human brain, which processes information at 100 billion operations per second (by some estimates). Several critics point out that the man in the room would probably take millions of years to respond to a simple question, and would require "filing cabinets" of astronomical proportions. This brings the clarity of Searle's intuition into doubt.
An especially vivid version of the speed and complexity reply is from Paul and Patricia Churchland. They propose this analogous thought experiment: "Consider a dark room containing a man holding a bar magnet or charged object. If the man pumps the magnet up and down, then, according to Maxwell's theory of artificial luminance (AL), it will initiate a spreading circle of electromagnetic waves and will thus be luminous. But as all of us who have toyed with magnets or charged balls well know, their forces (or any other forces for that matter), even when set in motion produce no luminance at all. It is inconceivable that you might constitute real luminance just by moving forces around!" Churchland's point is that the problem is that he would have to wave the magnet up and down something like 450 trillion times per second in order to see anything.
Stevan Harnad is critical of speed and complexity replies when they stray beyond addressing our intuitions. He writes "Some have made a cult of speed and timing, holding that, when accelerated to the right speed, the computational may make a phase transition into the mental. It should be clear that is not a counterargument but merely an ad hoc speculation (as is the view that it is all just a matter of ratcheting up to the right degree of 'complexity.')"
Searle argues that his critics are also relying on intuitions, however his opponents' intuitions have no empirical basis. He writes that, in order to consider the "system reply" as remotely plausible, a person must be "under the grip of an ideology". The system reply only makes sense (to Searle) if one assumes that any "system" can have consciousness, just by virtue of being a system with the right behavior and functional parts. This assumption, he argues, is not tenable given our experience of consciousness.
=== Other minds and zombies: meaninglessness ===
Several replies argue that Searle's argument is irrelevant because his assumptions about the mind and consciousness are faulty. Searle believes that human beings directly experience their consciousness, intentionality and the nature of the mind every day, and that this experience of consciousness is not open to question. He writes that we must "presuppose the reality and knowability of the mental." The replies below question whether Searle is justified in using his own experience of consciousness to determine that it is more than mechanical symbol processing. In particular, the other minds reply argues that we cannot use our experience of consciousness to answer questions about other minds (even the mind of a computer), the epiphenoma replies question whether we can make any argument at all about something like consciousness which can not, by definition, be detected by any experiment, and the eliminative materialist reply argues that Searle's own personal consciousness does not "exist" in the sense that Searle thinks it does.

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==== Other minds reply ====
The "Other Minds Reply" points out that Searle's argument is a version of the problem of other minds, applied to machines. There is no way we can determine if other people's subjective experience is the same as our own. We can only study their behavior (i.e., by giving them our own Turing test). Critics of Searle argue that he is holding the Chinese room to a higher standard than we would hold an ordinary person.
Nils Nilsson writes "If a program behaves as if it were multiplying, most of us would say that it is, in fact, multiplying. For all I know, Searle may only be behaving as if he were thinking deeply about these matters. But, even though I disagree with him, his simulation is pretty good, so I'm willing to credit him with real thought."
Turing anticipated Searle's line of argument (which he called "The Argument from Consciousness") in 1950 and makes the other minds reply. He noted that people never consider the problem of other minds when dealing with each other. He writes that "instead of arguing continually over this point it is usual to have the polite convention that everyone thinks." The Turing test simply extends this "polite convention" to machines. He does not intend to solve the problem of other minds (for machines or people) and he does not think we need to.
==== Replies considering that Searle's "consciousness" is undetectable ====
If we accept Searle's description of intentionality, consciousness, and the mind, we are forced to accept that consciousness is epiphenomenal: that it "casts no shadow" i.e. is undetectable in the outside world. Searle's "causal properties" cannot be detected by anyone outside the mind, otherwise the Chinese Room could not pass the Turing test—the people outside would be able to tell there was not a Chinese speaker in the room by detecting their causal properties. Since they cannot detect causal properties, they cannot detect the existence of the mental. Thus, Searle's "causal properties" and consciousness itself is undetectable, and anything that cannot be detected either does not exist or does not matter.
Mike Alder calls this the "Newton's Flaming Laser Sword Reply". He argues that the entire argument is frivolous, because it is non-verificationist: not only is the distinction between simulating a mind and having a mind ill-defined, but it is also irrelevant because no experiments were, or even can be, proposed to distinguish between the two.
Daniel Dennett provides this illustration: suppose that, by some mutation, a human being is born that does not have Searle's "causal properties" but nevertheless acts exactly like a human being. This is a philosophical zombie, as formulated in the philosophy of mind. This new animal would reproduce just as any other human and eventually there would be more of these zombies. Natural selection would favor the zombies, since their design is (we could suppose) a bit simpler. Eventually the humans would die out. So therefore, if Searle is right, it is most likely that human beings (as we see them today) are actually "zombies", who nevertheless insist they are conscious. It is impossible to know whether we are all zombies or not. Even if we are all zombies, we would still believe that we are not.
==== Eliminative materialist reply ====
Several philosophers argue that consciousness, as Searle describes it, does not exist. Daniel Dennett describes consciousness as a "user illusion".
This position is sometimes referred to as eliminative materialism: the view that consciousness is not a concept that can "enjoy reduction" to a strictly mechanical description, but rather is a concept that will be simply eliminated once the way the material brain works is fully understood, in just the same way as the concept of a demon has already been eliminated from science rather than enjoying reduction to a strictly mechanical description. Other mental properties, such as original intentionality (also called "meaning", "content", and "semantic character"), are also commonly regarded as special properties related to beliefs and other propositional attitudes. Eliminative materialism maintains that propositional attitudes such as beliefs and desires, among other intentional mental states that have content, do not exist. If eliminative materialism is the correct scientific account of human cognition then the assumption of the Chinese room argument that "minds have mental contents (semantics)" must be rejected.
Searle disagrees with this analysis and argues that "the study of the mind starts with such facts as that humans have beliefs, while thermostats, telephones, and adding machines don't ... what we wanted to know is what distinguishes the mind from thermostats and livers." He takes it as obvious that we can detect the presence of consciousness and dismisses these replies as being off the point.
=== Other replies ===
Margaret Boden argued in her paper "Escaping from the Chinese Room" that even if the person in the room does not understand the Chinese, it does not mean there is no understanding in the room. The person in the room at least understands the rule book used to provide output responses. She then points out that the same applies to machine languages: a natural language sentence is understood by the programming language code that instantiates it, which in turn is understood by the lower-level compiler code, and so on. This implies that the distinction between syntax and semantics is not fixed, as Searle presupposes, but relative: the semantics of natural language is realized in the syntax of programming language; the semantics of programming language has a semantics that is realized in the syntax of compiler code. Boden argues that there are different degrees of understanding and that it is not a binary notion.

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The clockwork universe is a concept which compares the universe to a mechanical clock. It continues ticking along, as a perfect machine, with its gears governed by the laws of physics, making every aspect of the machine predictable. It evolved during the Enlightenment in parallel with the emergence of Newton's laws governing motion and gravity.
== History ==
This idea was very popular among deists during the Enlightenment, when Isaac Newton derived his laws of motion, and showed that alongside the law of universal gravitation, they could predict the behaviour of both terrestrial objects and the Solar System.
A similar concept goes back to Johannes de Sacrobosco's early 13th-century introduction to astronomy: On the Sphere of the World. In this widely popular medieval text, Sacrobosco spoke of the universe as the machina mundi, the machine of the world, suggesting that the reported eclipse of the Sun at the crucifixion of Jesus was a disturbance of the order of that machine.
Responding to Gottfried Leibniz, a prominent supporter of the theory, in the Leibniz–Clarke correspondence, Samuel Clarke wrote:
The Notion of the World's being a great Machine, going on without the Interposition of God, as a Clock continues to go without the Assistance of a Clockmaker; is the Notion of Materialism and Fate, and tends, (under pretence of making God a Supra-mundane Intelligence,) to exclude Providence and God's Government in reality out of the World.
In 2009, artist Tim Wetherell created a wall piece for Questacon (The National Science and Technology centre in Canberra, Australia) representing the concept of the clockwork universe. This steel artwork contains moving gears, a working clock, and a movie of the lunar terminator.
== See also ==
Mechanical philosophy
Determinism
Eternalism (philosophy of time)
History of science
Orrery
Philosophy of space and time
Superdeterminism
== References ==
== Further reading ==
E. J. Dijksterhuis (1961) The Mechanization of the World Picture, Oxford University Press
Dolnick, Edward (2011) The Clockwork Universe: Isaac Newton, the Royal Society, and the Birth of the Modern World, HarperCollins.
David Brewster (1850) "A Short Scheme of the True Religion", manuscript quoted in Memoirs of the Life, Writings and Discoveries of Sir Isaac Newton, cited in Dolnick, page 65.
Anneliese Maier (1938) Die Mechanisierung des Weltbildes im 17. Jahrhundert
Webb, R.K. ed. Knud Haakonssen (1996) "The Emergence of Rational Dissent." Enlightenment and Religion: Rational Dissent in Eighteenth-Century Britain, Cambridge University Press page 19.
Westfall, Richard S. Science and Religion in Seventeenth-Century England. p. 201.
Riskins, Jessica (2016) The Restless Clock: A History of the Centuries-Long Argument over What Makes Living Things Tick, University of Chicago Press.
== External links ==
"The Clockwork Universe". Archived 2020-02-14 at the Wayback Machine The Physical World. Ed. John Bolton, Alan Durrant, Robert Lambourne, Joy Manners, Andrew Norton.

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Codex on the Flight of Birds is a relatively short codex from c. 1505 by Leonardo da Vinci.
It comprises 18 folios and measures 21 × 15 centimetres. Now held at the Royal Library of Turin, the codex begins with an examination of the flight behavior of birds and proposes mechanisms for flight by machines. Leonardo constructed a number of these machines, and attempted to launch them from a hill near Florence. However, his efforts failed.
In the codex, Leonardo notes for the first time that the center of gravity of a flying bird does not coincide with its center of pressure.
== Summary ==
The following summaries are from the codex whose English translation was prepared by Culturando and Smithsonian Institution.
=== Front Page ===
The front page is titled "On Casting Medals". The first paragraph gives a brief recipe that consists of "emery", "nitric acid", "iron filings", "vinegar", "ashes of walnut leaves", and "finely ground straw ash". The second paragraph tells of the process of crushing diamonds into diamond powder and separating the powder from lead. The last paragraph explains how to crush large crystals into smaller crystals, and how to grind, purify, and color enamel.
=== Folio 1 ===
The first page in the folio one contains 11 diagrams with captions for each that relate to gravity, density, balance, and oscillations. The next page contains four diagrams and a lengthy paragraph on velocity and the differences in movement along the arc and chord of part of a circle.
Leonardo comments on how gravity, which is caused by the "attraction of one object to another", takes place when an object is placed above another object and the top object is heavier than the bottom object. He also writes on the workings of a balance by describing how "the vertical center of a balance must always be perpendicular" and how the length of the arm on the balance is proportional to the amount of oscillations and the oscillation angle. A short commentary is included on relating density to weight, and he questions why ice floats in water if it is the denser of the two.
In the last page of this folio, Leonardo explains why an object falling down the arc of a curve will fall faster than if the object falls down the chord of a curve. He explains this saying that the angle of the chord is half of the angle the curve makes between the midpoint, endpoint, and horizontal, and since this angle is half then the speed will also be half. He compares this with the angle the arc makes with the endpoint, midpoint, and horizontal. An object falling down an arc is then said to be 7/8 faster than if it were to fall down the chord of a curve.
=== Folio 2 ===
Folio 2 contains two images on each of the two pages along with commentary on the following: gravity, powder amount vs. shot diameter, center of gravity for pyramids, and round balances.
In the first paragraph, Leonardo restates his theory on gravity and expands on it to say that the motion caused by gravity acts in the direction of the imaginary line between the two object's centers. He goes on to say that motion due to gravity is only caused because the objects have no way to resist gravity.
Leonardo then goes on to talk about the relation between the amount of powder and the size of a ball. He writes that the amount of powder needed is proportional to the diameter of the ball. Expanding on that, he comes up with the amount of powder needed is "directly proportional to the square of the diameter".
The center of gravity of a pyramid is written to lie "in the third point along of its length toward the base". He uses this geometry to explain how to find the center of gravity of a semicircle. If one were to divide the semicircle into pyramids whose bases were almost straight, then by finding the center of gravity of those pyramids one could find the center of gravity for the semicircle.
The last page of folio 2 talks about rounded balances and how they react to gravity. Leonardo writes that if a balance was suspended in its center of gravity, then it would not move or oscillate, regardless of position. He then goes on to say that if there are two weights of equal mass on the ends of this balance, then, when moved from its starting position, the balance will never return to the starting position. After this, he theorizes that a balance in this same situation will move if one of the weights is along a straighter line of descent as compared to the other weight. He then disproves his theory by showing the balance and weights as symmetrically equal, meaning there is no reason for the balance to move.
=== Folio 3 ===
The third folio contains 10 drawings and commentary on the following: science of machines, balances, energy, and circular motion.
Leonardo begins folio 3 with a declaration stating the science of machines is the most useful science overall because of its use by any moving object.
He goes on to state that objects of different shapes that are on different degrees of slope have different amounts of energy. His next topic is about the construction of a certain balance in which circular motion is prevented. The diagrams in this folio represent round balances and multiple shaped objects on differing slopes that are connected together.

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=== Folio 4 ===
Folio 4 contains nine diagrams and a page of text on gravity and its effect on different shapes connected together on a balance. The back page of this folio has Leonardo's first reference to birds and his explanation on how they fly.
Leonardo writes a lengthy amount of text about two weights that each weigh three pounds that are connected together on either side of a balance. The slopes that each object rests on are at different angles, however. Leonardo goes on to write that, because of the slopes, one weight may weigh three pounds, but it is only providing two pounds of force. The other weight, also three pounds, is similarly stated to only provide one pound of force because it is resting on a smaller incline. Later on this page, he writes on the forces a balance experiences depending on the location of weights on the balance. The first reference to pressure for this codex is made towards the end of this folio, relating it to the working of a balance.
The first commentary on birds, for this codex, are made on the second page of the fourth folio. Leonardo describes how the tips of a bird's feathers are always the highest part of the bird, when its wings are lowered, and how the bones in the wing are the highest part of a bird when its wings are raised. He writes on the heaviest part of a body being the location that guides movement for that body. He also questions what part of the wing of a bird experiences the greatest amount of air pressure. To end this folio, Leonardo states how an object, "that does not bend under the pressure of objects of different sizes and weights", will distribute its weight to its supporting points that surround the center of the object.
=== Folio 5 ===
The fifth folio contains six diagrams and commentary on birds and flight.
Leonardo starts off folio 5 by stating that if a man were to be in a flying machine, nothing should get in his way from the waist up, so that he can balance himself as one does in a boat. He goes on to write on how a bird's direction will change with the direction of the wind. A bird which is going in a straight line that comes into a cross breeze at a perpendicular angle will now be heading in a direction that is in between the two endpoints of each direction. He ends the first page by explaining that if a bird in a descent wants to turn left or right, then it will lower the wing on the side of the direction it wants to turn.
Birds can gain altitude, as stated by Leonardo, by "[raising] the shoulders and [beating] the tips of the wings towards itself, thus condensing the air that stands between the tips of [its] wings and itself". He also describes the flight of a kite as seeking a wind current. When the winds are high, one will see the bird very high in the sky, but when the winds are low, the bird stays closer to the ground. Leonardo describes how a bird rests in the air, after flapping its wings to gain altitude, by gliding downward to the ground.
=== Folio 6 ===
Folio 6 contains multiple diagrams of birds flying and their bone structure, and all of the commentary is on flight and how birds fly.
Leonardo starts off by describing how a bird ascends or descends in different wind conditions. Here is a summary.
He states that the only way for a bird to ascend when in a tailwind is for the bird, at its peak ascent, to turn in a semicircle and face the wind to continue its ascension in the opposite direction.
Leonardo explains that a bird should fly above the clouds to prevent its wings from getting wet and to avoid the circular air patterns that come from mountainous terrain. If a bird flies above the clouds and somehow gets turned over, then it should have plenty of time to turn itself back over by either "[falling] immediately with the wingtip downwind, or lowering the opposite wing to below halfway". He also comments on the rib structure of a bird and theorizes which ribs are the most useful. He ends folio 6 by stating he needs to do more practical tests on the ribs of birds.
=== Folio 7 ===
The seventh folio contains a very detailed diagram of either the tip of a bird's wing or the wing of a possible flying machine along with five more diagrams of birds in flight.
Leonardo starts writing on a flying machine and comparing it with the notes he has already taken on the flight of birds. He states that "the bird" (machine) must attain a high altitude it case it were to turn over so as to have enough time to right itself. He notes that the framework needs to be strong with leather laces and raw silk for the ribs. He also adds that there should not be any metal in the machine because of its tendency to wear or break under stress.
He continues his notes on the flying machine by writing on the "nerve" of the machine. It was to be made of a "thick ribbon of tanned leather" that would spread the wing in flight. He was going to use this same framework in the "nerves" above and below this one for safety reasons.
The rest of folio 7 is Leonardo's notes and instructions on how to fly his machine like a bird. Here is a quick summary:
=== Folio 8 ===
Leonardo's eighth folio in On the Flight of Birds contains 11 diagrams of birds flying and more instructions for his flying machine. Here is a quick summary of the first half of Folio 8:

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Leonardo goes on to write that if the "bird" is above the wind but turning into the wind, the "bird" must lower its tail otherwise it will overturn. He states that the action of lowering the tail to be less susceptible to wind in this situation will make it impossible for the "bird" to be overturned. He goes on to prove this by referencing the "Elements of Machinery". Afterwards, he writes on the compression of air due to the wings, and he states that the entire length of the wing is not used in the compression of air in front of the wing. To prove this, he asks readers to examine bird wings for themselves and to check the larger spacing in between the not as large feathers.
=== Folio 9 ===
Folio 9 contains another 12 diagrams of birds in flight and structure framework. It particularly looks at the effect of wind on the movement of a bird.
=== Folio 10 ===
Discusses the wings of a bird.
=== Folio 11 ===
Discussion of the importance of truth
=== Folio 12 ===
=== Folio 13 ===
=== Folio 14 ===
=== Folio 15 ===
=== Folio 16 ===
=== Folio 17 ===
=== Folio 18 ===
=== Back Page ===
== Display in the U.S. ==
On a rare loan from the Bibliotecha Reale museum in Turin, Italy, the original 18-page codex was displayed in the National Air and Space Museum in Washington D.C. for 40 days, starting 13 September 2013. In an exhibition the codex was displayed in the Bibliotecha Reale museum in Turin until 8 March 2020.
== Citations ==
== Sources ==
Cremante, Simona. "Leonardo Da Vinci". Giunti, 1698.
Crispino, Enrica; Pedretti, Carlo; Frost, Catherine. Leonardo: Art and Science. Giunti, 2001. ISBN 88-09-01511-8
Pedretti, Carlo. "A Chronology of Leonardo Da Vinci's Architectural Studies after 1500". Geneva: E. Droz, 1962.
Leonardo Da Vinci's Codex on the Flight of Birds (Smithsonian)
Galluzzi, Paolo. Leonardo da Vinci, Engineer and Architect. [Montréal]: Montreal Museum of Fine Arts, 1987. Print. ISBN 2891920848
Heydenreich, Ludwig H., Bern Dibner, Ladislao Reti, and Ladislao Reti. Leonardo the Inventor. New York: McGraw-Hill, 1980. Print. ISBN 0070286108
Edoardo Zanon, The book of the codex on flight, from the study of bird flight to the flying machine. Leonardo3 – Milano, 2009. ISBN 978-88-6048-011-8
== External links ==
Leonardo da Vinci: anatomical drawings from the Royal Library, Windsor Castle, exhibition catalog fully online as PDF from The Metropolitan Museum of Art, which contains material on Codex on the Flight of Birds (see index)

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Coherent extrapolated volition (CEV) is a theoretical framework in the field of AI alignment describing an approach by which an artificial superintelligence (ASI) would act on a benevolent supposition of what humans would want if they were more knowledgeable, more rational, had more time to think, and had matured together as a society, as opposed to humanity's current individual or collective preferences. It was proposed by Eliezer Yudkowsky in 2004 as part of his work on friendly AI.
== Concept ==
CEV proposes that an advanced AI system should derive its goals by extrapolating the idealized volition of humanity. This means aggregating and projecting human preferences into a coherent utility function that reflects what people would desire under ideal epistemic and moral conditions. The aim is to ensure that AI systems are aligned with humanity's true interests, rather than with transient or poorly informed preferences.
In poetic terms, our coherent extrapolated volition is our wish if we knew more, thought faster, were more the people we wished we were, had grown up farther together; where the extrapolation converges rather than diverges, where our wishes cohere rather than interfere; extrapolated as we wish that extrapolated, interpreted as we wish that interpreted.
== Debate ==
Yudkowsky and Nick Bostrom note that CEV has several interesting properties. It is designed to be humane and self-correcting, by capturing the source of human values instead of trying to list them. It avoids the difficulty of laying down an explicit, fixed list of rules. It encapsulates moral growth, preventing flawed current moral beliefs from getting locked in. It limits the influence that a small group of programmers can have on what the ASI would value, thus also reducing the incentives to build ASI first. And it keeps humanity in charge of its destiny.
CEV also faces significant theoretical and practical challenges.
Bostrom notes that CEV has "a number of free parameters that could be specified in various ways, yielding different versions of the proposal." One such parameter is the extrapolation base (whose extrapolated volition is taken into account). For example, whether it should include people with severe dementia, patients in a vegetative state, foetuses, or embryos. He also notes that if CEV's extrapolation base only includes humans, there is a risk that the result would be ungenerous toward other animals and digital minds. One possible solution would be to include a mechanism to expand CEV's extrapolation base.
== Variants and alternatives ==
A proposed theoretical alternative to CEV is to rely on an artificial superintelligence's superior cognitive capabilities to figure out what is morally right, and let it act accordingly. It is also possible to combine both techniques, for instance with the ASI following CEV except when it is morally impermissible.
In another review, a philosophical analysis explores CEV through the lens of social trust in autonomous systems. Drawing on Anthony Giddens' concept of "active trust", the author proposes an evolution of CEV into "Coherent, Extrapolated and Clustered Volition" (CECV). This formulation aims to better reflect the moral preferences of diverse cultural groups, thus offering a more pragmatic ethical framework for designing AI systems that earn public trust while accommodating societal diversity.
== See also ==
Friendly artificial intelligence
AI alignment
AI safety
Rationality
== References ==

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Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts (e.g., computational art as part of computational culture).
Is the application of computer systems to emulate human-like creative processes, facilitating the generation of artistic and design outputs that mimic innovation and originality.
The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:
To construct a program or computer capable of human-level creativity.
To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans.
To design programs that can enhance human creativity without necessarily being creative themselves.
The field of computational creativity concerns itself with theoretical and practical issues in the study of creativity. Theoretical work on the nature and proper definition of creativity is performed in parallel with practical work on the implementation of systems that exhibit creativity, with one strand of work informing the other.
The applied form of computational creativity is known as media synthesis.
== Theoretical issues ==
Theoretical approaches concern the essence of creativity. Especially, under what circumstances it is possible to call the model a "creative" if eminent creativity is about rule-breaking or the disavowal of convention. This is a variant of Ada Lovelace's objection to machine intelligence, as recapitulated by modern theorists such as Teresa Amabile. If a machine can do only what it was programmed to do, how can its behavior ever be called creative?
Indeed, not all computer theorists would agree with the premise that computers can only do what they are programmed to do—a key point in favor of computational creativity.
== Defining creativity in computational terms ==
Because no single perspective or definition seems to offer a complete picture of creativity, the AI researchers Newell, Shaw and Simon developed the combination of novelty and usefulness into the cornerstone of a multi-pronged view of creativity, one that uses the following four criteria to categorize a given answer or solution as creative:
The answer is novel and useful (either for the individual or for society)
The answer demands that we reject ideas we had previously accepted
The answer results from intense motivation and persistence
The answer comes from clarifying a problem that was originally vague
Margaret Boden focused on the first two of these criteria, arguing instead that creativity (at least when asking whether computers could be creative) should be defined as "the ability to come up with ideas or artifacts that are new, surprising, and valuable".
Mihaly Csikszentmihalyi argued that creativity had to be considered instead in a social context, and his DIFI (Domain-Individual-Field Interaction) framework has since strongly influenced the field. In DIFI, an individual produces works whose novelty and value are assessed by the field—other people in society—providing feedback and ultimately adding the work, now deemed creative, to the domain of societal works from which an individual might be later influenced.
Whereas the above reflects a top-down approach to computational creativity, an alternative thread has developed among bottom-up computational psychologists involved in artificial neural network research. During the late 1980s and early 1990s, for example, such generative neural systems were driven by genetic algorithms. Experiments involving recurrent nets were successful in hybridizing simple musical melodies and predicting listener expectations.

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== Historical evolution of computational creativity ==
The use computational processes to generate creative artifacts has been present from early times in history. During the late 1800's, methods for composing music combinatorily were explored, involving prominent figures like Mozart, Bach, Haydn, and Kiernberger. This approach extended to analytical endeavors as early as 1934, where simple mechanical models were built to explore mathematical problem solving. Professional interest in the creative aspect of computation also was commonly addressed in early discussions of artificial intelligence. The 1956 Dartmouth Conference, listed creativity, invention, and discovery as key goals for artificial intelligence.
As the development of computers allowed systems of greater complexity, the 1970's and 1980's saw invention of early systems that modelled creativity using symbolic or rule-based approaches. The field of creative storytelling investigated several such models. Meehan's TALE-SPIN (1977) generated narratives through simulation of character goals and decision trees. Dehn's AUTHOR (1981) approached generation by simulating an author's process for crafting a story. Beyond narrative generation, computational creativity expanded into artistic and scientific domains.
Artistic image generation was one of the disciplines that saw early potential in generated artifacts through computational creativity. One of the most prominent examples was Harold Cohen's AARON, which produced art through composition and adaptation of figures based on a large set of symbolic rules and heuristics for visual composition. Some systems also tackled creativity in scientific endeavors. BACON was said to rediscover natural laws like Boyle's Law and Kepler's law through hypothesis testing in constrained spaces.
By the 1990's the modeling techniques became more adaptive, attempting to implement cognitive creative rules for generation. Turner's MINSTREL (1993) introduced TRAMs (Transform Recall Adapt Methods) to simulate creative re-use of prior material for generative storytelling. Meanwhile, Pérez y Pérez's MEXICA (1999) modeled the creative writing process using cycles of engagement and reflection. As systems increasingly incorporated models of internal evaluation, another approach that emerged was that of combining symbolic generation with domain-specific evaluation metrics, modeling generative and selective steps to creativity
In the field of generational humor, the JAPE system (1994) generated pun-based riddles using Prolog and WordNet, applying symbolic pattern-matching rules and a large lexical database (WordNet) to compose riddles involving wordplay. WordNet is a system developed by George Miller and his team at Princeton, its platform and inspired word-mapping structures have been used as the backbone of several syntactic and semantic AI programs. A notable system for music generation was David Cope's EMI (Experiments in Musical Intelligence) or Emmy, which was trained in the styles of artists like Bach, Beethoven, or Chopin and generated novel pieces in their style through pattern abstraction and recomposition.
In the 2000s and beyond, machine learning began influencing creative system design. Researchers such as Mihalcea and Strapparava trained classifiers to distinguish humorous from non-humorous text, using stylistic and semantic features. Meanwhile custom computational approaches led to chess systems like Deep Blue generating quasi-creative gameplay strategies through search algorithms and parallel processing constrained by specific rules and patterns for evaluation.
The institutional development of computational creativity grew along its technical advances. Dedicated workshops such as the IJWCC emerged in the 1990s, growing out of interdisciplinary conferences focused on AI and creativity. By the early 2000s, the field coalesced around annual conferences like the International Conference on Computational Creativity (ICCC). Recently, with the advent of Deep Learning, Transformers, and further refinement in Machine Learning structures, computational creativity's implementation space has new tools for development.
== Machine learning for computational creativity ==
While traditional computational approaches to creativity rely on the explicit formulation of prescriptions by developers and a certain degree of randomness in computer programs, machine learning methods allow computer programs to learn on heuristics from input data enabling creative capacities within the computer programs. Especially, deep artificial neural networks allow to learn patterns from input data that allow for the non-linear generation of creative artefacts. Before 1989, artificial neural networks have been used to model certain aspects of creativity. Peter Todd (1989) first trained a neural network to reproduce musical melodies from a training set of musical pieces. Then he used a change algorithm to modify the network's input parameters. The network was able to randomly generate new music in a highly uncontrolled manner. In 1992, Todd extended this work, using the so-called distal teacher approach that had been developed by Paul Munro, Paul Werbos, D. Nguyen and Bernard Widrow, Michael I. Jordan and David Rumelhart. In the new approach, there are two neural networks, one of which is supplying training patterns to another.
In later efforts by Todd, a composer would select a set of melodies that define the melody space, position them on a 2-d plane with a mouse-based graphic interface, and train a connectionist network to produce those melodies, and listen to the new "interpolated" melodies that the network generates corresponding to intermediate points in the 2-d plane.

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=== Language models and hallucination ===
Language models like GPT and LSTM are used to generate texts for creative purposes, such as novels and scripts. These models demonstrate hallucination from time to time, where erroneous materials are presented as factual. Creators make use of their hallucinatory tendency to capture unintended results. Ross Goodwin's 1 the Road, for example, uses an LSTM model trained on literature corpora to generate a novel that refers to Jack Kerouac's On the Road based on multimodal input captured by a camera, a microphone, a laptop's inner clock, and a GPS throughout the road trip. Brian Merchant commented on the novel as "pixelated poetry in its ragtag assemblage of modern American imagery". Oscar Sharp and Ross Goodwin created the experimental sci-fi short film Sunspring in 2016, written with an LSTM model, trained on their scripts and 1980-1990 sci-fi movies. Rodica Gotca critiqued their overall lack of focus on the narrative and intention to create based on the background of human culture.
Nevertheless, researchers highlight the positive side of language models' hallucination for generating novel solutions, given that the correctness and consistency of the response could be controlled. Jiang et al. propose the divergence-convergence flow model for harnessing the hallucinatory effects. They summarize the types of such effects in current research into factuality hallucinations and faithfulness hallucinations, which can be divided into smaller classes like factual fabrication and instruction inconsistency. While the divergence stage involves generating potentially hallucinatory content, the convergence stage focuses on filtering the hallucinations that are useful for the user with intent recognition and evaluation metrics.
== Key concepts from literature ==
Some high-level and philosophical themes recur throughout the field of computational creativity, for example as follows.
=== Important categories of creativity ===
Margaret Boden refers to creativity that is novel merely to the agent that produces it as "P-creativity" (or "psychological creativity"), and refers to creativity that is recognized as novel by society at large as "H-creativity" (or "historical creativity").
=== Exploratory and transformational creativity ===
Boden also distinguishes between the creativity that arises from an exploration within an established conceptual space, and the creativity that arises from a deliberate transformation or transcendence of this space. She labels the former as exploratory creativity and the latter as transformational creativity, seeing the latter as a form of creativity far more radical, challenging, and rarer than the former. Following the criteria from Newell and Simon elaborated above, we can see that both forms of creativity should produce results that are appreciably novel and useful (criterion 1), but exploratory creativity is more likely to arise from a thorough and persistent search of a well-understood space (criterion 3) -- while transformational creativity should involve the rejection of some of the constraints that define this space (criterion 2) or some of the assumptions that define the problem itself (criterion 4). Boden's insights have guided work in computational creativity at a very general level, providing more an inspirational touchstone for development work than a technical framework of algorithmic substance. However, Boden's insights are also the subject of formalization, most notably in the work by Geraint Wiggins.
=== Generation and evaluation ===
The criterion that creative products should be novel and useful means that creative computational systems are typically structured into two phases, generation and evaluation. In the first phase, novel (to the system itself, thus P-Creative) constructs are generated; unoriginal constructs that are already known to the system are filtered at this stage. This body of potentially creative constructs is then evaluated, to determine which are meaningful and useful and which are not. This two-phase structure conforms to the Geneplore model of Finke, Ward and Smith, which is a psychological model of creative generation based on empirical observation of human creativity.
Jordanous and Keller emphasize the need for a "tractable and well-articulated model of creativity". They extracted 694 creativity words derived from a corpus of empirical studies in psychology and creativity research spanning 60 years and clustered them based on lexical similarity. As a result, they identify 14 key components of creativity, which form the basis of the framework "Standardised Procedure for Evaluating Creative Systems" (SPECS). These components include aspects like "dealing with uncertainty", "independence and freedom", "social interaction and communication", and "spontaneity & subconscious processing".
=== Co-creation ===
While much of computational creativity research focuses on independent and automatic machine-based creativity generation, many researchers are inclined towards a collaboration approach. This human-computer interaction is sometimes categorized under the creativity support tools development. These systems aim to provide an ideal framework for research, integration, decision-making, and idea generation. Recently, deep learning approaches to imaging, sound and natural language processing, resulted in the modeling of productive creativity development frameworks.

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=== Innovation ===
Computational creativity is increasingly being discussed in the innovation and management literature as the recent development in AI may disrupt entire innovation processes and fundamentally change how innovations will be created. Philip Hutchinson highlights the relevance of computational creativity for creating innovation and introduced the concept of "self-innovating artificial intelligence" (SAI) to describe how companies make use of AI in innovation processes to enhance their innovative offerings. SAI is defined as the organizational utilization of AI with the aim of incrementally advancing existing or developing new products, based on insights from continuously combining and analyzing multiple data sources. As AI becomes a general-purpose technology, the spectrum of products to be developed with SAI will broaden from simple to increasingly complex. This implies that computational creativity leads to a shift of creativity-related skills for humans.
Veale and Pérez y Pérez consider "optimal innovation" proposed by Giora et al. a useful foundation for developing computational creativity. Giora et al.'s experiment asks participants to do pleasure and familiarity ratings of verbal stimuli (e.g., body and soul vs. body and sole) and non-verbal stimuli (e.g., a peace dove vs. a peace dove vertically posed that looks like a waving hand). It reveals that pleasing stimuli need to be innovative while preserving the salient meaning of the literal form. Veale and Pérez y Pérez highlight the need to develop computational systems that capture how meaning changes due to formal changes.
=== Combinatorial creativity ===
A great deal, perhaps all, of human creativity can be understood as a novel combination of pre-existing ideas or objects. Common strategies for combinatorial creativity include:
Placing a familiar object in an unfamiliar setting (e.g., Marcel Duchamp's Fountain) or an unfamiliar object in a familiar setting (e.g., a fish-out-of-water story such as The Beverly Hillbillies)
Blending two superficially different objects or genres (e.g., a sci-fi story set in the Wild West, with robot cowboys, as in Westworld, or the reverse, as in Firefly; Japanese haiku poems, etc.)
Comparing a familiar object to a superficially unrelated and semantically distant concept (e.g., "Makeup is the Western burka"; "A zoo is a gallery with living exhibits")
Adding a new and unexpected feature to an existing concept (e.g., adding a scalpel to a Swiss Army knife; adding a camera to a mobile phone)
Compressing two incongruous scenarios into the same narrative to get a joke (e.g., the Emo Philips joke "Women are always using men to advance their careers. Damned anthropologists!")
Using an iconic image from one domain in a domain for an unrelated or incongruous idea or product (e.g., using the Marlboro Man image to sell cars, or to advertise the dangers of smoking-related impotence).
The combinatorial perspective allows us to model creativity as a search process through the space of possible combinations. The combinations can arise from composition or concatenation of different representations, or through a rule-based or stochastic transformation of initial and intermediate representations. Genetic algorithms and neural networks can be used to generate blended or crossover representations that capture a combination of different inputs.
==== Conceptual blending ====
Mark Turner and Gilles Fauconnier propose a model called Conceptual Integration Networks that elaborates upon Arthur Koestler's ideas about creativity as well as work by Lakoff and Johnson, by synthesizing ideas from Cognitive Linguistic research into mental spaces and conceptual metaphors. Their basic model defines an integration network as four connected spaces:
A first input space (contains one conceptual structure or mental space)
A second input space (to be blended with the first input)
A generic space of stock conventions and image-schemas that allow the input spaces to be understood from an integrated perspective
A blend space in which a selected projection of elements from both input spaces are combined; inferences arising from this combination also reside here, sometimes leading to emergent structures that conflict with the inputs.
Fauconnier and Turner describe a collection of optimality principles that are claimed to guide the construction of a well-formed integration network. In essence, they see blending as a compression mechanism in which two or more input structures are compressed into a single blend structure. This compression operates on the level of conceptual relations. For example, a series of similarity relations between the input spaces can be compressed into a single identity relationship in the blend.
Some computational success has been achieved with the blending model by extending pre-existing computational models of analogical mapping that are compatible by virtue of their emphasis on connected semantic structures. In 2006, Francisco Câmara Pereira presented an implementation of blending theory that employs ideas both from symbolic AI and genetic algorithms to realize some aspects of blending theory in a practical form; his example domains range from the linguistic to the visual, and the latter most notably includes the creation of mythical monsters by combining 3-D graphical models.
=== AI-assisted writing as curation ===
One of the first attempts to provide a literary-theoretical framework for AI-assisted writing was undertaken by Luciano Floridi in 2025. In his model of 'Distant Writing', the author functions as a designer and curator who develops narrative structures rather than formulating text manually. Through iterative selection and 'Socratic maieutics' (prompting), the human directs the machine, thereby assuming full intellectual responsibility for the design of the resulting work. Floridi's framework has a pre-LLM antecedent in the visual arts: Nicolas Bourriaud's Postproduction (2002) had argued that artists increasingly function as programmers and navigators of pre-existing cultural material rather than as original creators — a logic that Floridi transfers, with substantial theoretical elaboration, to the context of AI-assisted literary production. Floridi’s term 'distant writing' itself is coined in explicit analogy to Franco Moretti's 'distant reading' — understood in its later, computationally inflected sense — which had reframed literary analysis as the large-scale, algorithm-assisted study of textual corpora.

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== Linguistic creativity ==
Language provides continuous opportunity for creativity, evident in the generation of novel sentences, phrasings, puns, neologisms, rhymes, allusions, sarcasm, irony, similes, metaphors, analogies, witticisms, and jokes. Native speakers of morphologically rich languages frequently create new word-forms that are easily understood, and some have found their way to the dictionary. The area of natural language generation has been well studied, but these creative aspects of everyday language have yet to be incorporated with any robustness or scale.
=== Hypothesis of creative patterns ===
In the seminal work of applied linguist Ronald Carter, he hypothesized two main creativity types involving words and word patterns: pattern-reforming creativity, and pattern-forming creativity. Pattern-reforming creativity refers to creativity by the breaking of rules, reforming and reshaping patterns of language often through individual innovation, while pattern-forming creativity refers to creativity via conformity to language rules rather than breaking them, creating convergence, symmetry and greater mutuality between interlocutors through their interactions in the form of repetitions.
=== Story generation ===
Substantial work has been conducted in this area of linguistic creation since the 1970s, with the development of James Meehan's TALE-SPIN
system. TALE-SPIN viewed stories as narrative descriptions of a problem-solving effort, and created stories by first establishing a goal for the story's characters so that their search for a solution could be tracked and recorded. The MINSTREL system represents a complex elaboration of this basic approach, distinguishing a range of character-level goals in the story from a range of author-level goals for the story. Systems like Bringsjord's BRUTUS elaborate these ideas further to create stories with complex interpersonal themes like betrayal. Nonetheless, MINSTREL explicitly models the creative process with a set of Transform Recall Adapt Methods (TRAMs) to create novel scenes from old. The MEXICA model of Rafael Pérez y Pérez and Mike Sharples is more explicitly interested in the creative process of storytelling, and implements a version of the engagement-reflection cognitive model of creative writing.
=== Metaphor and simile ===
Example of a metaphor: "She was an ape."
Example of a simile: "Felt like a tiger-fur blanket."
The computational study of these phenomena has mainly focused on interpretation as a knowledge-based process. Computationalists such as Yorick Wilks, James Martin, Dan Fass, John Barnden, and Mark Lee have developed knowledge-based approaches to the processing of metaphors, either at a linguistic level or a logical level. Tony Veale and Yanfen Hao have developed a system, called Sardonicus, that acquires a comprehensive database of explicit similes from the web; these similes are then tagged as bona-fide (e.g., "as hard as steel") or ironic (e.g., "as hairy as a bowling ball", "as pleasant as a root canal"); similes of either type can be retrieved on demand for any given adjective. They use these similes as the basis of an on-line metaphor generation system called Aristotle that can suggest lexical metaphors for a given descriptive goal (e.g., to describe a supermodel as skinny, the source terms "pencil", "whip", "whippet", "rope", "stick-insect" and "snake" are suggested).
=== Analogy ===
The process of analogical reasoning has been studied from both a mapping and a retrieval perspective, the latter being key to the generation of novel analogies. The dominant school of research, as advanced by Dedre Gentner, views analogy as a structure-preserving process; this view has been implemented in the structure mapping engine or SME, the MAC/FAC retrieval engine (Many Are Called, Few Are Chosen), ACME (Analogical Constraint Mapping Engine) and ARCS (Analogical Retrieval Constraint System). Other mapping-based approaches include Sapper, which situates the mapping process in a semantic-network model of memory. Analogy is a very active sub-area of creative computation and creative cognition; active figures in this sub-area include Douglas Hofstadter, Paul Thagard, and Keith Holyoak. Also worthy of note here is Peter Turney and Michael Littman's machine learning approach to the solving of SAT-style analogy problems; their approach achieves a score that compares well with average scores achieved by humans on these tests.
=== Joke generation ===
Humour is an especially knowledge-hungry process, and the most successful joke-generation systems to date have focused on pun-generation, as exemplified by the work of Kim Binsted and Graeme Ritchie. This work includes the JAPE system, which can generate a wide range of puns that are consistently evaluated as novel and humorous by young children. An improved version of JAPE has been developed in the guise of the STANDUP system, which has been experimentally deployed as a means of enhancing linguistic interaction with children with communication disabilities. Some limited progress has been made in generating humour that involves other aspects of natural language, such as the deliberate misunderstanding of pronominal reference (in the work of Hans Wim Tinholt and Anton Nijholt), as well as in the generation of humorous acronyms in the HAHAcronym system of Oliviero Stock and Carlo Strapparava.

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=== Neologism ===
The blending of multiple word forms is a dominant force for new word creation in language; these new words are commonly called "blends" or "portmanteau words" (after Lewis Carroll). Tony Veale has developed a system called ZeitGeist that harvests neological headwords from Wikipedia and interprets them relative to their local context in Wikipedia and relative to specific word senses in WordNet. ZeitGeist has been extended to generate neologisms of its own; the approach combines elements from an inventory of word parts that are harvested from WordNet, and simultaneously determines likely glosses for these new words (e.g., "food traveller" for "gastronaut" and "time traveller" for "chrononaut"). It then uses Web search to determine which glosses are meaningful and which neologisms have not been used before; this search identifies the subset of generated words that are both novel ("H-creative") and useful.
A corpus linguistic approach to the search and extraction of neologism have also shown to be possible. Using Corpus of Contemporary American English as a reference corpus, Locky Law has performed an extraction of neologism, portmanteaus and slang words using the hapax legomena which appeared in the scripts of American TV drama House M.D.
In terms of linguistic research in neologism, Stefan Th. Gries has performed a quantitative analysis of blend structure in English and found that "the degree of recognizability of the source words and that the similarity of source words to the blend plays a vital role in blend formation." The results were validated through a comparison of intentional blends to speech-error blends.
=== Poetry ===
Like jokes, poems involve a complex interaction of different constraints, and no general-purpose poem generator adequately combines the meaning, phrasing, structure and rhyme aspects of poetry. Nonetheless, Pablo Gervás has developed a noteworthy system called ASPERA that employs a case-based reasoning (CBR) approach to generating poetic formulations of a given input text via a composition of poetic fragments that are retrieved from a case-base of existing poems. Each poem fragment in the ASPERA case-base is annotated with a prose string that expresses the meaning of the fragment, and this prose string is used as the retrieval key for each fragment. Metrical rules are then used to combine these fragments into a well-formed poetic structure. Racter is an example of such a software project.
LLMs have been applied to poetry since the late 2010s. In Autumn 2020, The Poetry Review (ISSN 0032-2156) published Ariel Klein's "50% Human: A poetic interview with AI agents", an LLM-generated/assisted poetic feature and an early verified instance of LLM poetry in a major literary magazine; subsequent trade publications such as K. Allado-McDowell's Pharmako-AI (2020) and I Am Code: An Artificial Intelligence Speaks: Poems (2023) brought LLM-authored verse to wider audiences.
== Musical creativity ==
Computational creativity in the music domain has focused both on the generation of musical scores for use by human musicians, and on the generation of music for performance by computers. The domain of generation has included classical music (with software that generates music in the style of Mozart and Bach) and jazz. Most notably, David Cope has written a software system called "Experiments in Musical Intelligence" (or "EMI") that is capable of analyzing and generalizing from existing music by a human composer to generate novel musical compositions in the same style. EMI's output is convincing enough to persuade human listeners that its music is human-generated to a high level of competence.
In the field of contemporary classical music, Iamus is the first computer that composes from scratch, and produces final scores that professional interpreters can play. The London Symphony Orchestra played a piece for full orchestra, included in Iamus' debut CD, which New Scientist described as "The first major work composed by a computer and performed by a full orchestra". Melomics, the technology behind Iamus, is able to generate pieces in different styles of music with a similar level of quality.
Creativity research in jazz has focused on the process of improvisation and the cognitive demands that this places on a musical agent: reasoning about time, remembering and conceptualizing what has already been played, and planning ahead for what might be played next.
The robot Shimon, developed by Gil Weinberg of Georgia Tech, has demonstrated jazz improvisation. Virtual improvisation software based on researches on stylistic modeling carried out by Gerard Assayag and Shlomo Dubnov include OMax, SoMax and PyOracle, are used to create improvisations in real-time by re-injecting variable length sequences learned on the fly from the live performer.
In the field of musical composition, the patented works by René-Louis Baron allowed to make a robot that can create and play a multitude of orchestrated melodies, so-called "coherent" in any musical style. All outdoor physical parameter associated with one or more specific musical parameters, can influence and develop each of these songs (in real-time while listening to the song). The patented invention Medal-Composer raises problems of copyright.
== Visual and artistic creativity ==

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Computational creativity in the generation of visual art has had some notable successes in the creation of both abstract art and representational art. A well-known program in this domain is Harold Cohen's AARON, which has been continuously developed and augmented since 1973. Though formulaic, Aaron exhibits a range of outputs, generating black-and-white drawings or colour paintings that incorporate human figures (such as dancers), potted plants, rocks, and other elements of background imagery. These images are of a sufficiently high quality to be displayed in reputable galleries.
Other software artists of note include the NEvAr system (for "Neuro-Evolutionary Art") of Penousal Machado. NEvAr uses a genetic algorithm to derive a mathematical function that is then used to generate a coloured three-dimensional surface. A human user is allowed to select the best pictures after each phase of the genetic algorithm, and these preferences are used to guide successive phases, thereby pushing NEvAr's search into pockets of the search space that are considered most appealing to the user.
The Painting Fool, developed by Simon Colton originated as a system for overpainting digital images of a given scene in a choice of different painting styles, colour palettes and brush types. Given its dependence on an input source image to work with, the earliest iterations of the Painting Fool raised questions about the extent of, or lack of, creativity in a computational art system. Nonetheless, The Painting Fool has been extended to create novel images, much as AARON does, from its own limited imagination. Images in this vein include cityscapes and forests, which are generated by a process of constraint satisfaction from some basic scenarios provided by the user (e.g., these scenarios allow the system to infer that objects closer to the viewing plane should be larger and more color-saturated, while those further away should be less saturated and appear smaller). Artistically, the images now created by the Painting Fool appear on a par with those created by Aaron, though the extensible mechanisms employed by the former (constraint satisfaction, etc.) may well allow it to develop into a more elaborate and sophisticated painter.
The artist Krasi Dimtch (Krasimira Dimtchevska) and the software developer Svillen Ranev have created a computational system combining a rule-based generator of English sentences and a visual composition builder that converts sentences generated by the system into abstract art. The software generates automatically indefinite number of different images using different color, shape and size palettes. The software also allows the user to select the subject of the generated sentences or/and the one or more of the palettes used by the visual composition builder.
An emerging area of computational creativity is that of video games. ANGELINA is a system for creatively developing video games in Java by Michael Cook. One important aspect is Mechanic Miner, a system that can generate short segments of code that act as simple game mechanics. ANGELINA can evaluate these mechanics for usefulness by playing simple unsolvable game levels and testing to see if the new mechanic makes the level solvable. Sometimes Mechanic Miner discovers bugs in the code and exploits these to make new mechanics for the player to solve problems with.
In July 2015, Google released DeepDream – an open source computer vision program, created to detect faces and other patterns in images with the aim of automatically classifying images, which uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dreamlike psychedelic appearance in the deliberately over-processed images.
In August 2015, researchers from Tübingen, Germany created a convolutional neural network that uses neural representations to separate and recombine content and style of arbitrary images which is able to turn images into stylistic imitations of works of art by artists such as a Picasso or Van Gogh in about an hour. Their algorithm is put into use in the website DeepArt that allows users to create unique artistic images by their algorithm.
In early 2016, a global team of researchers explained how a new computational creativity approach known as the Digital Synaptic Neural Substrate (DSNS) could be used to generate original chess puzzles that were not derived from endgame databases. The DSNS is able to combine features of different objects (e.g. chess problems, paintings, music) using stochastic methods in order to derive new feature specifications which can be used to generate objects in any of the original domains. The generated chess puzzles have also been featured on YouTube.
== Creativity in problem solving ==
Creativity is also useful in allowing for unusual solutions in problem solving. In psychology and cognitive science, this research area is called creative problem solving. The Explicit-Implicit Interaction (EII) theory of creativity has been implemented using a CLARION-based computational model that allows for the simulation of incubation and insight in problem-solving. The emphasis of this computational creativity project is not on performance per se (as in artificial intelligence projects) but rather on the explanation of the psychological processes leading to human creativity and the reproduction of data collected in psychology experiments. So far, this project has been successful in providing an explanation for incubation effects in simple memory experiments, insight in problem solving, and reproducing the overshadowing effect in problem solving.
== Criticism of computational creativity ==

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Traditional computers, as mainly used in the computational creativity application, do not support creativity, as they fundamentally transform a set of discrete, limited domain of input parameters into a set of discrete, limited domain of output parameters using a limited set of computational functions. As such, a computer cannot be creative, as everything in the output must have been already present in the input data or the algorithms. Related discussions and references to related work are captured in work on philosophical foundations of simulation.
Mathematically, the same set of arguments against creativity has been made by Chaitin. Similar observations come from a Model Theory perspective. All this criticism emphasizes that computational creativity is useful and may look like creativity, but it is not real creativity, as nothing new is created, just transformed in well-defined algorithms.
According to researchers like Mark Riedl, human creativity and computational creativity at their current state differ in several dimensions. While creativity can be viewed in the context of morality, Riedl considers the "educational, moralizing" aspect of stories as one of the challenges to developing narrative-generating AI models, which may contribute to the underlying reasoning coherence of the text. The lack of intention in AI models hinders them from making morally responsible choices, which often appear in human creativity.
Michele Loi and Eleonora Vigano identified some potential threats to human creativity caused by AI development. For example, they considered the openness to "experiments of life", introduced by John Stuart Mill, an important factor in creativity. Society's overreliance on algorithms for making decisions would constrain utility functions, which may discourage people from exploring riskier solutions and decrease the diversity of exploration and thus the creativity.
== Events ==
The International Conference on Computational Creativity (ICCC) occurs annually, organized by The Association for Computational Creativity. Events in the series include:
ICCC 2023: University of Waterloo in Ontario, Canada
ICCC 2022: Free University of Bozen-Bolzano, Bolzano, Italy
ICCC 2021: Mexico City, Mexico (Virtual due to COVID-19 pandemic)
ICCC 2020, Coimbra, Portugal (Virtual due to COVID-19 pandemic)
ICCC 2019, Charlotte, North Carolina, US
ICCC 2018, Salamanca, Spain
ICCC 2017, Atlanta, Georgia, US
ICCC 2016, Paris, France
ICCC 2015, Park City, Utah, US. Keynote: Emily Short
ICCC 2014, Ljubljana, Slovenia. Keynote: Oliver Deussen
ICCC 2013, Sydney, Australia. Keynote: Arne Dietrich
ICCC 2012, Dublin, Ireland. Keynote: Steven Smith
ICCC 2011, Mexico City, Mexico. Keynote: George E Lewis
ICCC 2010, Lisbon, Portugal. Keynote/Invited Talks: Nancy J Nersessian and Mary Lou Maher
Previously, the community of computational creativity has held a dedicated workshop, the International Joint Workshop on Computational Creativity, every year since 1999. Previous events in this series include:
IJWCC 2003, Acapulco, Mexico, as part of IJCAI'2003
IJWCC 2004, Madrid, Spain, as part of ECCBR'2004
IJWCC 2005, Edinburgh, UK, as part of IJCAI'2005
IJWCC 2006, Riva del Garda, Italy, as part of ECAI'2006
IJWCC 2007, London, UK, a stand-alone event
IJWCC 2008, Madrid, Spain, a stand-alone event
The 1st Conference on Computer Simulation of Musical Creativity will be held
CCSMC 2016, 17–19 June, University of Huddersfield, UK. Keynotes: Geraint Wiggins and Graeme Bailey.
== See also ==
1 the Road – Novel written by an artificial intelligence (1st novel)
Artificial imagination – Artificial simulation of human imagination
Algorithmic art – Art genre
Algorithmic composition – Technique of using algorithms to create music
Applications of artificial intelligence
Computer art – Art genre
Collective creativity – Ability to generate new ideas and solutions together in a creative process
Creative computing – Computer science applied to the arts
Digital morphogenesis – Type of generative art
Digital poetry – Form of electronic literature
Generative art – Art created by a set of rules, often using computers
Generative systems – Technologies that can produce change driven by audiences
Intrinsic motivation (artificial intelligence) – Mechanism for enabling artificial agents to exhibit curiosity
Musikalisches Würfelspiel – Musical dice games used to randomly generate music (Musical dice game)
Procedural generation – Method in which data is created algorithmically as opposed to manually
Lists
List of emerging technologies
Outline of artificial intelligence
== References ==
== Further reading ==
An Overview of Artificial Creativity Archived 2008-03-25 at the Wayback Machine on Think Artificial
Cohen, H., "the further exploits of AARON, Painter" Archived 2008-04-19 at the Wayback Machine, SEHR, volume 4, issue 2: Constructions of the Mind, 1995
== External links ==
Documentaries
Noorderlicht: Margaret Boden and Stephen Thaler on Creative Computers on Archive.org
In Its Image on Archive.org

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Computer Power and Human Reason: From Judgment to Calculation is a 1976 nonfiction book by German-American computer scientist Joseph Weizenbaum in which he contends that while artificial intelligence may be possible, we should never allow computers to make important decisions, as they will always lack human qualities such as compassion and wisdom.
== Background ==
Before writing Computer Power and Human Reason, Weizenbaum had garnered significant attention for creating the ELIZA program, an early milestone in conversational computing. His firsthand observation of people attributing human-like qualities to a simple program prompted him to reflect more deeply on society's readiness to entrust moral and ethical considerations to machines.
== Reception and legacy ==
Computer Power and Human Reason sparked scholarly debate on the acceptable scope of AI applications, particularly in fields where human welfare and ethical considerations are paramount. Early academic reviews highlighted that Weizenbaum's stance pushed readers to recognize that even as computers grow more capable, they lack the intrinsic moral compass and empathy required for certain kinds of judgment.
The book caused disagreement with, and separation from, other members of the artificial intelligence research community, a status the author later said he'd come to take pride in.
== See also ==
Ethics of artificial intelligence
Criticism of technology
== References ==
== External links ==
Plug & Pray, Documentary Film on Joseph Weizenbaum and the ethics of technology

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"Computing Machinery and Intelligence" is a paper written by Alan Turing on the topic of artificial intelligence. The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public.
Turing's paper considers the question "Can machines think?" Turing says that since the words "think" and "machine" cannot clearly be defined, we should "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words." To achieve this objective, Turing proposes a three-step approach. First, he identifies a simple and unambiguous concept to substitute for the term "think." Second, he delineates the specific "machines" under consideration. Third, armed with these tools, he poses a new question related to the first, which he believes he can answer in the affirmative.
== Turing's test ==
Rather than trying to determine if a machine is thinking, Turing suggests we should ask if the machine can win a game, called the "Imitation Game". The original Imitation game, that Turing described, is a simple party game involving three players. Player A is a man, player B is a woman and player C (who plays the role of the interrogator) can be of either sex. In the Imitation Game, player C is unable to see either player A or player B (and knows them only as X and Y), and can communicate with them only through written notes or any other form that does not give away any details about their gender. By asking questions of player A and player B, player C tries to determine which of the two is the man and which is the woman. Player A's role is to trick the interrogator into making the wrong decision, while player B attempts to assist the interrogator in making the right one.
Turing proposes a variation of this game that involves the computer:
We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?"
So the modified game becomes one that involves three participants in isolated rooms: a computer (which is being tested), a human, and a (human) judge. The human judge can converse with both the human and the computer by typing into a terminal. Both the computer and the human try to convince the judge that they are the human. If the judge cannot consistently tell which is which, then the computer wins the game.
Researchers in the United Kingdom had been exploring "machine intelligence" for up to ten years prior to the founding of the field of artificial intelligence (AI) research in 1956. It was a common topic among the members of the Ratio Club, an informal group of British cybernetics and electronics researchers that included Alan Turing. Turing, in particular, had been running the notion of machine intelligence since at least 1941 and one of the earliest-known mentions of "computer intelligence" was made by him in 1947.
As Stevan Harnad notes, the question has become "Can machines do what we (as thinking entities) can do?" In other words, Turing is no longer asking whether a machine can "think"; he is asking whether a machine can act indistinguishably from the way a thinker acts. This question avoids the difficult philosophical problem of pre-defining the verb "to think" and focuses instead on the performance capacities that being able to think makes possible, and how a causal system can generate them.
Since Turing introduced his test, it has been both highly influential and widely criticised, and has become an important concept in the philosophy of artificial intelligence. Some of its criticisms, such as John Searle's Chinese room, are themselves controversial. Some have taken Turing's question to have been "Can a computer, communicating over a teleprinter, fool a person into believing it is human?" but it seems clear that Turing was not talking about fooling people but about generating human cognitive capacity.
== Digital machines ==

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Turing also notes that we need to determine which "machines" we wish to consider. He points out that a human clone, while man-made, would not provide a very interesting example. Turing suggested that we should focus on the capabilities of digital machinery—machines which manipulate the binary digits of 1 and 0, rewriting them into memory using simple rules. He gave two reasons.
First, there is no reason to speculate whether or not they can exist. They already did in 1950.
Second, digital machinery is "universal". Turing's research into the foundations of computation had proved that a digital computer can, in theory, simulate the behaviour of any other digital machine, given enough memory and time. (This is the essential insight of the Church–Turing thesis and the universal Turing machine.) Therefore, if any digital machine can "act like it is thinking", then every sufficiently powerful digital machine can. Turing writes, "all digital computers are in a sense equivalent."
This allows the original question to be made even more specific. Turing now restates the original question as "Let us fix our attention on one particular digital computer C. Is it true that by modifying this computer to have an adequate storage, suitably increasing its speed of action, and providing it with an appropriate programme, C can be made to play satisfactorily the part of A in the imitation game, the part of B being taken by a man?"
Hence, Turing states that the focus is not on "whether all digital computers would do well in the game nor whether the computers that are presently available would do well, but whether there are imaginable computers which would do well". What is more important is to consider the advancements possible in the state of our machines today regardless of whether we have the available resource to create one or not.
== Nine common objections ==
Having clarified the question, Turing turned to answering it: he considered the following nine common objections, which include all the major arguments against artificial intelligence raised in the years since his paper was first published.

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Religious Objection: This states that thinking is a function of man's immortal soul; therefore, a machine cannot think. "In attempting to construct such machines," wrote Turing, "we should not be irreverently usurping His power of creating souls, any more than we are in the procreation of children: rather we are, in either case, instruments of His will providing mansions for the souls that He creates."
'Heads in the Sand' Objection: "The consequences of machines thinking would be too dreadful. Let us hope and believe that they cannot do so." This thinking is popular among intellectual people, as they believe superiority derives from higher intelligence and the possibility of being overtaken is a threat (as machines have efficient memory capacities and processing speed, machines exceeding the learning and knowledge capabilities are highly probable). This objection is a fallacious appeal to consequences, confusing what should not be with what can or cannot be (Wardrip-Fruin, 56).
The Mathematical Objection: This objection uses mathematical theorems, such as Gödel's incompleteness theorem, to show that there are limits to what questions a computer system based on logic can answer. Turing suggests that humans are too often wrong themselves and pleased at the fallibility of a machine. (This argument would be made again by philosopher John Lucas in 1961 and physicist Roger Penrose in 1989, and later would be called Penrose–Lucas argument.)
Argument From Consciousness: This argument, suggested by Professor Geoffrey Jefferson in his 1949 Lister Oration (acceptance speech for his 1948 award of Lister Medal) states that "not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain." Turing replies by saying that we have no way of knowing that any individual other than ourselves experiences emotions, and that therefore we should accept the test. He adds, "I do not wish to give the impression that I think there is no mystery about consciousness ... [b]ut I do not think these mysteries necessarily need to be solved before we can answer the question [of whether machines can think]." (This argument, that a computer can't have conscious experiences or understanding, would be made in 1980 by philosopher John Searle in his Chinese room argument. Turing's reply is now known as the "other minds reply". See also Can a machine have a mind? in the philosophy of AI.)
Arguments from various disabilities. These arguments all have the form "a computer will never do X". Turing offers a selection:Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humour, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make someone fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behaviour as a man, do something really new.Turing notes that "no support is usually offered for these statements," and that they depend on naive assumptions about how versatile machines may be in the future, or are "disguised forms of the argument from consciousness." He chooses to answer a few of them:
Machines cannot make mistakes. He notes it's easy to program a machine to appear to make a mistake.
A machine cannot be the subject of its own thought (or can't be self-aware). A program which can report on its internal states and processes, in the simple sense of a debugger program, can certainly be written. Turing asserts "a machine can undoubtably be its own subject matter."
A machine cannot have much diversity of behaviour. He notes that, with enough storage capacity, a computer can behave in an astronomical number of different ways.
Lady Lovelace's Objection: One of the most famous objections states that computers are incapable of originality. This is largely because, according to Ada Lovelace, machines are incapable of independent learning.The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths. Turing suggests that Lovelace's objection can be reduced to the assertion that computers "can never take us by surprise" and argues that, to the contrary, computers could still surprise humans, in particular where the consequences of different facts are not immediately recognizable. Turing also argues that Lady Lovelace was hampered by the context from which she wrote, and if exposed to more contemporary scientific knowledge, it would become evident that the brain's storage is quite similar to that of a computer.
Argument from continuity in the nervous system: Modern neurological research has shown that the brain is not digital. Even though neurons fire in an all-or-nothing pulse, both the exact timing of the pulse and the probability of the pulse occurring have analog components. Turing acknowledges this, but argues that any analog system can be simulated to a reasonable degree of accuracy given enough computing power. (Philosopher Hubert Dreyfus would make this argument against "the biological assumption" in 1972.)
Argument from the informality of behaviour: This argument states that any system governed by laws will be predictable and therefore not truly intelligent. Turing replies by stating that this is confusing laws of behaviour with general rules of conduct, and that if on a broad enough scale (such as is evident in man) machine behaviour would become increasingly difficult to predict. He argues that, just because we can't immediately see what the laws are, does not mean that no such laws exist. He writes "we certainly know of no circumstances under which we could say, 'we have searched enough. There are no such laws.'". (Hubert Dreyfus would argue in 1972 that human reason and problem solving was not based on formal rules, but instead relied on instincts and awareness that would never be captured in rules. More recent AI research in robotics and computational intelligence attempts to find the complex rules that govern our "informal" and unconscious skills of perception, mobility and pattern matching. See Dreyfus' critique of AI). This rejoinder also includes the Turing's Wager argument.
Extra-sensory perception: In 1950, extra-sensory perception was an active area of research and Turing chooses to give ESP the benefit of the doubt, arguing that conditions could be created in which mind-reading would not affect the test. Turing admitted to "overwhelming statistical evidence" for telepathy, likely referring to early 1940s experiments by Samuel Soal, a member of the Society for Psychical Research.

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== Learning machines ==
In the final section of the paper Turing details his thoughts about the Learning Machine that could play the imitation game successfully.
Here Turing first returns to Lady Lovelace's objection that the machine can only do what we tell it to do and he likens it to a situation where a man "injects" an idea into the machine to which the machine responds and then falls off into quiescence. He extends on this thought by an analogy to an atomic pile of less than critical size, which is to be considered the machine, and an injected idea is to correspond to a neutron entering the pile from outside the pile; the neutron will cause a certain disturbance which eventually dies away. Turing then builds on that analogy and mentions that, if the size of the pile were to be sufficiently large, then a neutron entering the pile would cause a disturbance that would continue to increase until the whole pile were destroyed, the pile would be supercritical. Turing then asks the question as to whether this analogy of a super critical pile could be extended to a human mind and then to a machine. He concludes that such an analogy would indeed be suitable for the human mind with "There does seem to be one for the human mind. The majority of them seem to be "subcritical," i.e., to correspond in this analogy to piles of sub critical size. An idea presented to such a mind will on average give rise to less than one idea in reply. A smallish proportion are supercritical. An idea presented to such a mind that may give rise to a whole "theory" consisting of secondary, tertiary and more remote ideas". He finally asks if a machine could be made to be supercritical.
Turing then mentions that the task of being able to create a machine that could play the imitation game is one of programming and he postulates that by the end of the century it will indeed be technologically possible to program a machine to play the game. He then mentions that in the process of trying to imitate an adult human mind it becomes important to consider the processes that lead to the adult mind being in its present state; which he summarizes as:
1. The initial state of the mind, say at birth,
2. The education to which it has been subjected,
3. Other experience, not to be described as education, to which it has been subjected.
Given this process he asks whether it would be more appropriate to program a child's mind instead of an adult’s mind and then subject the child mind to a period of education. He likens the child to a newly bought notebook and speculates that due to its simplicity it would be more easily programmed. The problem then is broken down into two parts, the programming of a child mind and its education process. He mentions that a child mind would not be expected as desired by the experimenter (programmer) at the first attempt. A learning process that involves a method of reward and punishment must be in place that will select desirable patterns in the mind. This whole process, Turing mentions, to a large extent is similar to that of evolution by natural selection where the similarities are:
Structure of the child machine = hereditary material
Changes of the child machine = mutations
Natural selection = judgment of the experimenter
Following this discussion Turing addresses certain specific aspects of the learning machine:

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Nature of inherent complexity: The child machine could either be one that is as simple as possible, merely maintaining consistency with general principles, or the machine could be one with a complete system of logical inference programmed into it. This more complex system is explained by Turing as "..would be such that the machines store would be largely occupied with definitions and propositions. The propositions would have various kinds of status, e.g., well-established facts, conjectures, mathematically proved theorems, statements given by an authority, expressions having the logical form of proposition but not belief-value. Certain propositions may be described as "imperatives." The machine should be so constructed that as soon as an imperative is classed as "well established" the appropriate action automatically takes place". Despite this built-in logic system the logical inference programmed in would not be one that is formal, rather it would be one that is more pragmatic. In addition the machine would build on its built-in logic system by a method of "scientific induction".
Ignorance of the experimenter: An important feature of a learning machine that Turing points out is the ignorance of the teacher of the machines' internal state during the learning process. This is in contrast to a conventional discrete state machine where the objective is to have a clear understanding of the internal state of the machine at every moment during the computation. The machine will be seen to be doing things that we often cannot make sense of or something that we consider to be completely random. Turing mentions that this specific character bestows upon a machine a certain degree of what we consider to be intelligence, in that intelligent behaviour consists of a deviation from the complete determinism of conventional computation but only so long as the deviation does not give rise to pointless loops or random behaviour.
The importance of random behaviour: Though Turing cautions us of random behaviour he mentions that inculcating an element of randomness in a learning machine would be of value in a system. He mentions that this could be of value where there might be multiple correct answers or ones where it might be such that a systematic approach would investigate several unsatisfactory solutions to a problem before finding the optimal solution which would entail the systematic process inefficient. Turing also mentions that the process of evolution takes the path of random mutations in order to find solutions that benefit an organism but he also admits that in the case of evolution the systematic method of finding a solution would not be possible.
Turing concludes by speculating about a time when machines will compete with humans on numerous intellectual tasks and suggests tasks that could be used to make that start. Turing then suggests that abstract tasks such as playing chess could be a good place to start another method which he puts as "..it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English.".
An examination of the development in artificial intelligence that has followed reveals that the learning machine did take the abstract path suggested by Turing as in the case of Deep Blue, a chess playing computer developed by IBM and one which defeated the world champion Garry Kasparov (though, this too is controversial) and the numerous computer chess games which can outplay most amateurs. As for the second suggestion Turing makes, it has been likened by some authors as a call to finding a simulacrum of human cognitive development. Such attempts at finding the underlying algorithms by which children learn the features of the world around them are only beginning to be made.
== See also ==
History of artificial intelligence
== Notes ==
== References ==
Brooks, Rodney (1990), "Elephants Don't Play Chess" (PDF), Robotics and Autonomous Systems, 6 (1–2): 3–15, CiteSeerX 10.1.1.588.7539, doi:10.1016/S0921-8890(05)80025-9, retrieved 30 August 2007
Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3.
Dreyfus, Hubert (1972), What Computers Can't Do, New York: MIT Press, ISBN 978-0-06-011082-6
Dreyfus, Hubert; Dreyfus, Stuart (1986), Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer, Oxford, UK: Blackwell
Dreyfus, Hubert (1979), What Computers Still Can't Do, New York: MIT Press.
Harnad, Stevan; Scherzer, Peter (2008), "First, Scale Up to the Robotic Turing Test, Then Worry About Feeling", Artificial Intelligence in Medicine, 44 (2): 83–9, CiteSeerX 10.1.1.115.4269, doi:10.1016/j.artmed.2008.08.008, PMID 18930641, archived from the original on 8 February 2012, retrieved 29 August 2010.
Haugeland, John (1985), Artificial Intelligence: The Very Idea, Cambridge, Mass.: MIT Press.
Moravec, Hans (1976), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, retrieved 7 November 2007
Hofstadter, Douglas (1979), Gödel, Escher, Bach: an Eternal Golden Braid.
Lucas, John (1961), "Minds, Machines and Gödel", in Anderson, A.R. (ed.), Minds and Machines, archived from the original on 19 August 2007, retrieved 2 December 2022
Moravec, Hans (1988), Mind Children, Harvard University Press
Penrose, Roger (1989), The Emperor's New Mind: Concerning Computers, Minds, and The Laws of Physics, Oxford University Press, ISBN 978-0-14-014534-2
Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
Searle, John (1980), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3): 417–457, doi:10.1017/S0140525X00005756, S2CID 55303721
Turing, Alan (October 1950), "Computing Machinery and Intelligence" (PDF), Mind, LIX (236): 433–460, doi:10.1093/mind/LIX.236.433
Saygin, A. P. (2000). "Turing Test: 50 years later". Minds and Machines. 10 (4): 463–518. doi:10.1023/A:1011288000451. hdl:11693/24987. S2CID 990084.
Noah Wardrip-Fruin and Nick Montfort, eds. (2003). The New Media Reader. Cambridge: MIT Press. ISBN 0-262-23227-8. "Lucasfilm's Habitat" pp. 663–677.
== External links ==
PDF with the full text of the paper
Saygin, Ayse Pinar; Cicekli, Ilyas; Akman, Varol (1999). "An analysis and review of the next 50 years". Minds and Machines: 2000. CiteSeerX 10.1.1.157.1592.

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The discovery of the neutron and its properties was central to the extraordinary developments in atomic physics in the first half of the 20th century. Early in the century, Ernest Rutherford used alpha particle scattering to discover that an atom has its mass and electric charge concentrated in a tiny nucleus. By 1920, isotopes of chemical elements had been discovered, the atomic masses had been determined to be approximately integer multiples of the mass of the hydrogen atom, and the atomic number had been identified as the charge on the nucleus. Throughout the 1920s, the nucleus was viewed as composed of combinations of protons and electrons, the two elementary particles known at the time, but that model presented several experimental and theoretical contradictions.
The essential nature of the atomic nucleus was established with the discovery of the neutron by James Chadwick in 1932 and the determination that it was a new elementary particle, distinct from the proton.
The uncharged neutron was immediately exploited as a new means to probe nuclear structure, leading to such discoveries as the creation of new radioactive elements by neutron irradiation (1934) and the fission of uranium atoms by neutrons (1938). The discovery of fission led to the creation of both nuclear power and nuclear weapons by the end of World War II. Both the proton and the neutron were presumed to be elementary particles until the 1960s, when they were determined to be composite particles built from quarks.
== Discovery of radioactivity ==
At the start of the 20th century, the vigorous debate as to the existence of atoms had not yet been resolved. Philosophers such as Ernst Mach and Wilhelm Ostwald denied the existence of atoms, viewing them as a convenient mathematical construct, while scientists such as Arnold Sommerfeld and Ludwig Boltzmann saw that physical theories required the existence of atoms.
Radioactivity was discovered in 1896 by the French scientist Henri Becquerel, while working with phosphorescent materials. In 1898, Ernest Rutherford at Cavendish Laboratory distinguished two types of radioactivity, alpha rays and beta rays, which differed in their ability to penetrate, or travel into, ordinary objects or gases. Two years later, Paul Villard discovered gamma rays, which possessed even more penetrating power. These radiations were later identified with known particles: beta rays were shown to be electrons by Walter Kaufmann in 1902, alpha rays were shown to be helium ions by Rutherford and Thomas Royds in 1907, and gamma rays were shown to be electromagnetic radiation, that is, a form of light, by Rutherford and Edward Andrade in 1914. These radiations had also been identified as emanating from atoms, hence they provided clues to processes occurring within atoms. Conversely, the radiations were also recognized as tools that could be used in scattering experiments to probe the interior of atoms.
== Gold foil experiment and the discovery of the atomic nucleus ==
At the University of Manchester between 1908 and 1913, Rutherford directed Hans Geiger and Ernest Marsden in a series of experiments to determine what occurs when alpha particles scatter from metal foil. Now called the Rutherford gold foil experiment, or the Geiger–Marsden experiment, these measurements made the extraordinary discovery that although most alpha particles passing through a thin gold foil experienced little deflection, a few scattered to a high angle. The scattering indicated that some of the alpha particles ricocheted back from a small, but dense, component inside the atoms. Based on these measurements, it was apparent to Rutherford by 1911 that the atom consisted of a small, massive nucleus with positive charge. The concentrated atomic mass was required to provide the observed deflection of the alpha particles, and Rutherford developed a mathematical model that accounted for the scattering.
While the Rutherford model was largely ignored at the time, when Niels Bohr joined Rutherford's group, he developed the Bohr model for electrons orbiting the nucleus in 1913. The Bohr model eventually led to an atomic model based on quantum mechanics by the mid-1920s.
== Discovery of isotopes ==
Concurrent with the work of Rutherford, Geiger, and Marsden, the radiochemist Frederick Soddy at the University of Glasgow was studying chemistry-related problems on radioactive materials. Soddy had worked with Rutherford on radioactivity at McGill University. By 1910, about 40 different radioactive elements, referred to as radioelements, had been identified between uranium and lead, although the periodic table only allowed for 11 elements. Every attempt to chemically isolate the radioelements mesothorium or thorium X from radium failed. Soddy concluded that these element were chemically the same element. At the suggestion of Margaret Todd, Soddy called these chemically identical elements isotopes. In 1913, Soddy and theorist Kazimierz Fajans independently found the displacement law, that an element undergoing alpha decay will produce an element two places to the left in the periodic system and an element undergoing beta decay will produce an element one place to the right in the periodic system. For his study of radioactivity and the discovery of isotopes, Soddy was awarded the 1921 Nobel Prize in Chemistry.

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Prior to 1919 only atomic weights averaged over a very large number of atoms was available. In that year, Francis Aston built the first mass spectrograph, an improved form of a device built by J. J. Thomson to measure the deflection of positively charged atoms by electric and magnetic fields. Aston was then able to separate the isotopes of many light elements including neon, 20Ne and 22Ne. Aston discovered the isotopes matched William Prout's whole number rule: the mass of every isotope is a whole number multiple of hydrogen.
Significantly, the one exception to this whole number rule was hydrogen itself, which had a mass value of 1.008. The excess mass was small, but well outside the limits of experimental uncertainty. Aston and others realized this difference was due to the binding energy of atoms. When a number of hydrogen atoms are bound into a atom, that atom's energy must be less than the sum of the energies of the separate hydrogen atoms. That lost energy, according to the mass-energy equivalence principle, means the atomic mass will be slightly less than the sum of the masses of its components. Aston's work on isotopes won him the 1922 Nobel Prize in Chemistry for the discovery of isotopes in a large number of non-radioactive elements, and for his enunciation of the whole number rule.
== Atomic number and Moseley's law ==
Before 1913, chemists adhered to Mendeleev's principle that chemical properties derived from atomic weight. However, several places in the periodic table were inconsistent with this concept. For example cobalt and nickel seemed reversed. There were also attempts to understand the relationship between the atomic mass and nuclear charge. Rutherford knew from experiments in his lab that helium must have a nuclear charge of 2 and a mass of 4; this 1:2 ratio was expected to hold for all elements. In 1913 Antonius van den Broek hypothesized that the periodic table should be organized by charge, denoted by Z, not atomic mass and that Z was not exactly half of the atomic weight for elements. This solved the cobalt-nickel issue. Placing cobalt (Z=27, mass of 58.97), before the less heavier nickel (Z=28, mass of 58.68) gave the ordering expected by chemical behavior.
Henry Moseley set out to test Broek's hypothesis by measuring the electromagnetic emission spectra of heavier elements, such as cobalt and nickel, to see if they followed the ordering by weight or by atomic number. In 1913–1914 Moseley tested the question experimentally by using X-ray spectroscopy. He found that the most intense short-wavelength line in the X-ray spectrum of a particular element, known as the K-alpha line, was related to the element's position in the periodic table, that is, its atomic number, Z. Moseley found that the frequencies of the radiation were related in a simple way to the atomic number of the elements for a large number of elements.
Within a year, it was noted that the equation for the relation, now called Moseley's law, could be explained in terms of the 1913 Bohr model with reasonable extra assumptions about atomic structure in other elements. Moseley's result, by Bohr's later account, not only established atomic number as a measurable experimental quantity, but gave it a physical meaning as the positive charge on the atomic nucleus. The elements could be ordered in the periodic system in order of atomic number, rather than atomic weight. The result tied together the organization of the periodic table, the Bohr model for the atom, and Rutherford's model for alpha scattering from nuclei. It was cited by Rutherford, Bohr, and others as a critical advance in understanding the nature of the atomic nucleus.
Further research in atomic physics was interrupted by the onset of World War I. Moseley was killed in 1915 at the Battle of Gallipoli, while Rutherford's student James Chadwick was interned in Germany for the duration of the war, 1914–1918. In Berlin, Lise Meitner's and Otto Hahn's research work on determining the radioactive decay chains of radium and uranium by precise chemical separation was interrupted. Meitner spent much of the war working as a radiologist and medical X-ray technician near the Austrian front, while Hahn, a chemist, worked on research in poison gas warfare.
== Rutherford's conjecture ==

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In 1920, Rutherford gave a Bakerian lecture at the Royal Society entitled the "Nuclear Constitution of Atoms", a summary of recent experiments on atomic nuclei and conclusions as to the structure of atomic nuclei. By 1920, the existence of electrons within the atomic nucleus was widely assumed. It was assumed the nucleus consisted of hydrogen nuclei in number equal to the atomic mass number. But, since each hydrogen nucleus had charge +1 e, the nucleus required a smaller number of "internal electrons" each of charge −1 e to give the nucleus its correct total charge. The mass of protons is about 1800 times greater than that of electrons, so the mass of the electrons is incidental in this computation. Such a model was consistent with the scattering of alpha particles from heavy nuclei, as well as the charge and mass of the many isotopes that had been identified. There were other motivations for the proton-electron model. As noted by Rutherford at the time, "We have strong reason for believing that the nuclei of atoms contain electrons as well as positively charged bodies ...", namely, it was known that beta radiation was electrons emitted from the nucleus.
In that lecture, Rutherford conjectured the existence of new particles. The alpha particle was known to be very stable, and it was assumed to retain its identity within the nucleus. The alpha particle was presumed to consist of four protons and two closely bound electrons to give it +2 charge and mass 4. In a 1919 paper, Rutherford had reported the apparent discovery of a new doubly charged particle of mass 3, denoted the X++, interpreted to consist of three protons and a closely bound electron. This result suggested to Rutherford the likely existence of two new particles: one of two protons with a closely bound electron, and another of one proton and a closely bound electron. The X++ particle was later determined to have mass 4 and to be just a low-energy alpha particle. Nevertheless, Rutherford had conjectured the existence of the deuteron, a +1 charge particle of mass 2, and the neutron, a neutral particle of mass 1. The former is the nucleus of deuterium, discovered in 1931 by Harold Urey. The mass of the hypothetical neutral particle would be little different from that of the proton. Rutherford determined that such a zero-charge particle would be difficult to detect by available techniques.
Around the time of Rutherford's lecture, other publications appeared with similar suggestions of a proton–electron composite in the nucleus, and in 1921, William Harkins, an American chemist, named the uncharged particle the neutron. About that same time the word proton was adopted for the hydrogen nucleus. Neutron was apparently constructed from the Latin root for neutral and the Greek ending -on (by imitation of electron and proton). References to the word neutron in connection with the atom can be found in the literature as early as 1899, however.
Rutherford and Chadwick immediately began an experimental program at the Cavendish Laboratory in Cambridge to search for the neutron. The experiments continued throughout the 1920s without success.
Rutherford's conjecture and the hypothetical "neutron" were not widely accepted. In his 1931 monograph on the Constitution of Atomic Nuclei and Radioactivity, George Gamow, then at the Institute for Theoretical Physics in Copenhagen, did not mention the neutron. At the time of their 1932 measurements in Paris that would lead to the discovery of the neutron, Irène Joliot-Curie and Frédéric Joliot were unaware of the conjecture.

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== Problems of the nuclear electrons hypothesis ==
Throughout the 1920s, physicists assumed that the atomic nucleus was composed of protons and "nuclear electrons". Under this hypothesis, the nitrogen-14 (14N) nucleus would be composed of 14 protons and 7 electrons, so that it would have a net charge of +7 elementary charge units and a mass of 14 atomic mass units. This nucleus would also be orbited by another 7 electrons, termed "external electrons" by Rutherford, to complete the 14N atom. However problems with the hypothesis soon became apparent.
Ralph Kronig pointed out in 1926 that the observed hyperfine structure of atomic spectra was inconsistent with the proton–electron hypothesis. This structure is caused by the influence of the nucleus on the dynamics of orbiting electrons. The magnetic moments of supposed "nuclear electrons" should produce hyperfine spectral line splittings similar to the Zeeman effect, but no such effects were observed. It seemed that the magnetic moment of the electron vanished when it was within the nucleus.
While on a visit to Utrecht University in 1928, Kronig learned of a surprising aspect of the rotational spectrum of N2+. The precision measurement made by Leonard Ornstein, the director of Utrecht's Physical Laboratory, showed that the spin of a nitrogen nucleus must be equal to one. However, if the nitrogen-14 (14N) nucleus was composed of 14 protons and 7 electrons, an odd number of spin-1/2 particles, then the resultant nuclear spin should be half-integer. Kronig therefore suggested the possibility that "protons and electrons do not retain their identity to the extent they do outside the nucleus".
Observations of the rotational energy levels of diatomic molecules using Raman spectroscopy by Franco Rasetti in 1929 were inconsistent with the statistics expected from the proton–electron hypothesis. Rasetti obtained band spectra for H2 and N2 molecules. While the lines for both diatomic molecules showed alternation in intensity between light and dark, the pattern of alternation for H2 is opposite to that of the N2. After carefully analyzing these experimental results, German physicists Walter Heitler and Gerhard Herzberg showed that the hydrogen nuclei obey Fermi statistics and the nitrogen nuclei obey Bose statistics. However, a then unpublished result of Eugene Wigner showed that a composite system with an odd number of spin-1/2 particles must obey Fermi statistics; a system with an even number of spin-1/2 particle obeys Bose statistics. If the nitrogen nucleus had 21 particles, it should obey Fermi statistics, contrary to fact. Thus, Heitler and Herzberg concluded: "the electron in the nucleus ... loses its ability to determine the statistics of the nucleus."
The Klein paradox, discovered by Oskar Klein in 1928, presented further quantum mechanical objections to the notion of an electron confined within a nucleus. Derived from the Dirac equation, this clear and precise paradox suggested that an electron approaching a high potential barrier has a high probability of passing through the barrier by a pair creation process. Apparently, an electron could not be confined within a nucleus by any potential well. The meaning of this paradox was widely debated at the time.
By about 1930, it was generally recognized that it was difficult to reconcile the proton–electron model for nuclei with the Heisenberg uncertainty relation of quantum mechanics. This relation, Δx⋅Δp ≥ 1⁄2ħ, implies that an electron confined to a region the size of an atomic nucleus typically has a kinetic energy of about 40 MeV, which is larger than the observed energy of beta particles emitted from the nucleus. Such energy is also much larger than the binding energy of nucleons, which Aston and others had shown to be less than 9 MeV per nucleon.
In 1927, Charles Ellis and W. Wooster at the Cavendish Laboratory measured the energies of β-decay electrons. They found that the distribution of energies from any particular radioactive nuclei was broad and continuous, a result that contrasted notably with the distinct energy values observed in alpha and gamma decay. Furthermore, the continuous energy distribution seemed to indicate that energy was not conserved by this "nuclear electrons" process. In 1929, Bohr proposed to modify the law of energy conservation to account for the continuous energy distribution, a proposal that earned the support of Werner Heisenberg. Such considerations were apparently reasonable, inasmuch as the laws of quantum mechanics had so recently overturned the laws of classical mechanics.
While all of these considerations did not "prove" an electron could not exist in the nucleus, they were confusing and challenging for physicists to interpret. Many theories were invented to explain how the above arguments could be wrong. In his 1931 monograph, Gamow summarized all of these contradictions, marking the statements regarding electrons in the nucleus with warning symbols.
== Discovery of the neutron ==
In 1930, Walther Bothe and his collaborator Herbert Becker in Giessen, Germany found that if the energetic alpha particles emitted from polonium fell on certain light elements, specifically beryllium (94Be), boron (115B), or lithium (73Li), an unusually penetrating radiation was produced. Beryllium produced the most intense radiation. Polonium is highly radioactive, producing energetic alpha radiation, and it was commonly used for scattering experiments at the time. Alpha radiation can be influenced by an electric field because it is composed of charged particles. The observed penetrating radiation was not influenced by an electric field, however, so it was thought to be gamma radiation. The radiation was more penetrating than any gamma rays known, and the details of experimental results were difficult to interpret.

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Two years later, Irène Joliot-Curie and Frédéric Joliot in Paris showed that if this unknown radiation fell on paraffin wax, or any other hydrogen-containing compound, it ejected protons of very high energy (5 MeV). This observation was not in itself inconsistent with the assumed gamma ray nature of the new radiation, but that interpretation (Compton scattering) had a logical problem. From energy and momentum considerations, a gamma ray would have to have impossibly high energy (50 MeV) to scatter a massive proton. In Rome, the young physicist Ettore Majorana declared that the manner in which the new radiation interacted with protons required a neutral particle as heavy as a proton, but declined to publish his result despite the encouragement of Enrico Fermi.
On hearing of the Paris results, Rutherford and James Chadwick at the Cavendish Laboratory also did not believe the gamma ray hypothesis since it failed to conserve energy. Assisted by Norman Feather, Chadwick quickly performed a series of experiments showing that the gamma ray hypothesis was untenable. The previous year, Chadwick, J.E.R. Constable, and E.C. Pollard had already conducted experiments on disintegrating light elements using alpha radiation from polonium. They had also developed more accurate and efficient methods for detecting, counting, and recording the ejected protons. Chadwick repeated the creation of the radiation using beryllium to absorb the alpha particles: 9Be + 4He (α) → 12C + 1n. Following the Paris experiment, he aimed the radiation at paraffin wax, a hydrocarbon high in hydrogen content, hence offering a target dense with protons. As in the Paris experiment, the radiation energetically scattered some of the protons. Chadwick measured the range of these protons and also measured how the new radiation impacted the atoms of various gases. Measurements of the recoil energy showed that the mass of the radiation particles must be similar to the mass of the proton: the new radiation could not consist of gamma rays. Uncharged particles with about the same mass to the proton matched the properties Rutherford described in 1920 and which had later been called neutrons. Chadwick won the Nobel Prize in Physics in 1935 for this discovery.
The year 1932 was later referred to as the "annus mirabilis" for nuclear physics in the Cavendish Laboratory, with discoveries of the neutron, artificial nuclear disintegration by the Cockcroft–Walton particle accelerator, and the positron.
== Proton–neutron model of the nucleus ==
Given the problems of the proton–electron model, it was quickly accepted that the atomic nucleus is composed of protons and neutrons, although the precise nature of the neutron was initially unclear. Within months after the discovery of the neutron, Werner Heisenberg and Dmitri Ivanenko had proposed proton–neutron models for the nucleus. Heisenberg's landmark papers approached the description of protons and neutrons in the nucleus through quantum mechanics. While Heisenberg's theory for protons and neutrons in the nucleus was a "major step toward understanding the nucleus as a quantum mechanical system", he still assumed the presence of nuclear electrons. In particular, Heisenberg assumed the neutron was a proton–electron composite, for which there is no quantum mechanical explanation. Heisenberg had no explanation for how lightweight electrons could be bound within the nucleus. Heisenberg introduced the first theory of nuclear exchange forces that bind the nucleons. He considered protons and neutrons to be different quantum states of the same particle, i.e., nucleons distinguished by the value of their nuclear isospin quantum numbers.
The proton–neutron model explained the puzzle of dinitrogen. When 14N was proposed to consist of 3 pairs each of protons and neutrons, with an additional unpaired neutron and proton each contributing a spin of 1⁄2 ħ in the same direction for a total spin of 1 ħ, the model became viable. Soon, neutrons were used to naturally explain spin differences in many different nuclides in the same way.
If the proton–neutron model for the nucleus resolved many issues, it highlighted the problem of explaining the origins of beta radiation. No existing theory at the time could account for how electrons or positrons could emanate from the nucleus. In 1934, Enrico Fermi published his classic paper describing the process of beta decay, in which the neutron decays to a proton by creating an electron and a (as yet undiscovered) neutrino. The paper employed the analogy that photons, or electromagnetic radiation, were similarly created and destroyed in atomic processes. Ivanenko had suggested a similar analogy in 1932. Fermi's theory requires the neutron to be a spin-1⁄2 particle. The theory preserved the principle of conservation of energy, which had been put into question by the continuous energy distribution of beta particles. The basic theory for beta decay proposed by Fermi was the first to show how particles could be created and destroyed. It established a general, basic theory for the interaction of particles by weak or strong forces. While this influential paper has stood the test of time, the ideas within it were so new that when it was first submitted to the journal Nature in 1933 it was rejected as being too speculative.
== Nature of the neutron ==

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The question of whether the neutron was a composite particle of a proton and an electron persisted for a few years after its discovery. In 1932 Harrie Massey explored a model for a composite neutron to account for its great penetrating power through matter and its electrical neutrality, for example. The issue was a legacy of the prevailing view from the 1920s that the only elementary particles were the proton and electron.
The nature of the neutron was a primary topic of discussion at the 7th Solvay Conference held in October 1933, attended by Heisenberg, Niels Bohr, Lise Meitner, Ernest Lawrence, Fermi, Chadwick, and others. As posed by Chadwick in his Bakerian Lecture in 1933, the primary question was the mass of the neutron relative to the proton. If the neutron's mass was less than the combined masses of a proton and an electron (1.0078 Da), then the neutron could be a proton-electron composite because of the mass defect from the nuclear binding energy. If greater than the combined masses, then the neutron was elementary like the proton. The question was challenging to answer because the electron's mass is only 0.05% of the proton's, hence exceptionally precise measurements were required.
The difficulty of making the measurement is illustrated by the wide-ranging values for the mass of the neutron obtained from 1932 to 1934. The accepted value today is 1.00866 Da. In Chadwick's 1932 paper reporting on the discovery, he estimated the mass of the neutron to be between 1.005 Da and 1.008 Da. By bombarding boron with alpha particles, Frédéric and Irène Joliot-Curie obtained a high value of 1.012 Da, while Ernest Lawrence's team at the University of California measured the small value 1.0006 Da using their new cyclotron.
In 1935 Chadwick and his doctoral student Maurice Goldhaber resolved the issue by reporting the first accurate measurement of the mass of the neutron. They used the 2.6 MeV gamma rays of Thallium-208 (208Tl) (then known as "thorium C") to photodisintegrate the deuteron.
In this reaction, the resulting proton and neutron have about equal kinetic energy, since their masses are about equal. The kinetic energy of the resulting proton could be measured (0.24 MeV), and therefore the deuteron's binding energy could be determined (2.6 MeV − 2(0.24 MeV) = 2.1 MeV, or 0.0023 Da). The neutron's mass could then be determined by the simple mass balance
where md,p,n refer to the deuteron, proton, or neutron mass, and "b.e." is the binding energy. The masses of the deuteron and proton were known; Chadwick and Goldhaber used values 2.0142 Da and 1.0081 Da, respectively. They found that the neutron's mass was slightly greater than the mass of the proton 1.0084 Da or 1.0090 Da, depending on the precise value used for the deuteron mass. The mass of the neutron was too large to be a proton–electron composite, and the neutron was therefore identified as an elementary particle. Chadwick and Goldhaber predicted that a free neutron would be able to decay into a proton, electron, and neutrino (free neutron decay).
== Neutron physics in the 1930s ==
Soon after the discovery of the neutron, indirect evidence suggested the neutron had an unexpected non-zero value for its magnetic moment. Attempts to measure the neutron's magnetic moment originated with the discovery by Otto Stern in 1933 in Hamburg that the proton had an anomalously large magnetic moment. By 1934 groups led by Stern, now in Pittsburgh, and I. I. Rabi in New York had independently deduced that the magnetic moment of the neutron was negative and unexpectedly large by measuring the magnetic moments of the proton and deuteron. Values for the magnetic moment of the neutron were also determined by Robert Bacher (1933) at Ann Arbor and I.Y. Tamm and S.A. Altshuler (1934) in the Soviet Union from studies of the hyperfine structure of atomic spectra. By the late 1930s, accurate values for the magnetic moment of the neutron had been deduced by the Rabi group, using measurements employing newly developed nuclear magnetic resonance techniques. The large value for the proton's magnetic moment and the inferred negative value for the neutron's magnetic moment were unexpected and raised many questions.

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The discovery of the neutron immediately gave scientists a new tool for probing the properties of atomic nuclei. Alpha particles had been used over the previous decades in scattering experiments, but such particles, which are helium nuclei, have +2 charge. This charge makes it difficult for alpha particles to overcome the Coulomb repulsive force and interact directly with the nuclei of atoms. Since neutrons have no electric charge, they do not have to overcome this force to interact with nuclei. Almost coincident with their discovery, neutrons were used by Norman Feather, Chadwick's colleague and protege, in scattering experiments with nitrogen. Feather was able to demonstrate that neutrons interacting with nitrogen nuclei scattered to protons or induced nitrogen to disintegrate to form boron with the emission of an alpha particle. Feather was therefore the first to show that neutrons produce nuclear disintegrations.
In Rome, Enrico Fermi and his team bombarded heavier elements with neutrons and found the products to be radioactive. By 1934, they had used neutrons to induce radioactivity in 22 different elements, many of these elements of high atomic number. Noticing that other experiments with neutrons at his laboratory seemed to work better on a wooden table than a marble table, Fermi suspected that the protons of the wood were slowing the neutrons and so increasing the chance for the neutron to interact with nuclei. Fermi therefore passed neutrons through paraffin wax to slow them and found that the radioactivity of some bombarded elements increased by a factor of tens to hundreds. The cross section for interaction with nuclei is much larger for slow neutrons than for fast neutrons. In 1938, Fermi received the Nobel Prize in Physics "for his demonstrations of the existence of new radioactive elements produced by neutron irradiation, and for his related discovery of nuclear reactions brought about by slow neutrons". Later, Fermi recounted to Chandrasekhar that he was originally planning to put a piece of lead there, but an inexplicable, intuitive feeling made him put a paraffin in the spot instead.
In Berlin, the collaboration of Lise Meitner and Otto Hahn, together with their assistant Fritz Strassmann, furthered the research begun by Fermi and his team when they bombarded uranium with neutrons. Between 1934 and 1938, Hahn, Meitner, and Strassmann found a great number of radioactive transmutation products from these experiments, all of which they regarded as transuranic. Transuranic nuclides are those that have an atomic number greater than uranium (92), formed by neutron absorption; such nuclides are not naturally occurring. In July 1938, Meitner was forced to escape antisemitic persecution in Nazi Germany after the Anschluss, and she was able to secure a new position in Sweden. The decisive experiment on 16–17 December 1938 (using a chemical process called "radium–barium–mesothorium fractionation") produced puzzling results: what they had understood to be three isotopes of radium were instead consistently behaving as barium. Radium (atomic number 88) and barium (atomic number 56) are in the same chemical group. By January 1939 Hahn had concluded that what they had thought were transuranic nuclides were instead much lighter nuclides, such as barium, lanthanum, cerium and light platinoids. Meitner and her nephew Otto Frisch immediately and correctly interpreted these observations as resulting from nuclear fission, a term coined by Frisch.
Hahn and his collaborators had detected the splitting of uranium nuclei, made unstable by neutron absorption, into lighter elements. Meitner and Frisch also showed that the fission of each uranium atom would release about 200 MeV of energy. The discovery of fission electrified the global community of atomic physicists and the public. In their second publication on nuclear fission, Hahn and Strassmann predicted the existence and liberation of additional neutrons during the fission process. Frédéric Joliot and his team proved this phenomenon to be a chain reaction in March 1939. In 1945, Hahn received the 1944 Nobel Prize in Chemistry "for his discovery of the fission of heavy atomic nuclei".
== After 1939 ==
The discovery of nuclear fission at the end of 1938 marked a shift in the centers of nuclear research from Europe to the United States. Large numbers of scientists were migrating to the United States to escape the troubles and antisemitism in Europe and the looming war (See Jewish scientists and the Manhattan Project). The new centers of nuclear research were the universities in the United States, particularly Columbia University in New York and the University of Chicago where Enrico Fermi had relocated, and a secret research facility at Los Alamos, New Mexico, established in 1942, the new home of the Manhattan Project. This wartime project was focused on the construction of nuclear weapons, using the enormous energy released by the fission of uranium or plutonium through neutron-based chain reactions.
The discoveries of the neutron and positron in 1932 were the start of the discoveries of many new particles. Muons were discovered in 1936, pions and kaons were discovered in 1947, and lambda particles were discovered in 1950. Throughout the 1950s and 1960s, a large number of particles called hadrons were discovered. A classification scheme for organizing all these particles, proposed independently by Murray Gell-Mann and
George Zweig in 1964, became known as the quark model. By this model, particles such as the proton and neutron were not elementary, but composed of various configurations of a small number of other truly elementary particles called partons or quarks. The quark model received experimental verification beginning in the late 1960s and finally provided an explanation for the neutron's anomalous magnetic moment.
== Videos ==
Ernest Rutherford summarizes the state of nuclear physics in 1935. (7 min., Nobelprize.org)
Hans Bethe discusses Chadwick and Goldhaber's work on deuteron disintegration. (2 min., Web of Stories)
== Explanatory notes ==
== References ==
== Further reading ==
Annotated bibliography for neutrons from the Alsos Digital Library for Nuclear Issues
Abraham Pais, Inward Bound, Oxford: Oxford University Press, 1986. ISBN 0198519974.
Herwig Schopper, Weak interactions and nuclear beta decay, Publisher, North-Holland Pub. Co., 1966. OCLC 644015779
Ruth Lewin Sime, Lise Meitner: A Life in Physics, Berkeley, University of California Press, 1996. ISBN 0520208609.
Roger H. Stuewer, "The Nuclear Electron Hypothesis". In Otto Hahn and the Rise of Nuclear Physics, William R. Shea, ed. Dordrecht, Holland: D. Riedel Publishing Company. pp. 19–67, 1983. ISBN 90-277-1584-X.
Sin-Itiro Tomonaga, The Story of Spin, The University of Chicago Press, 1997. ISBN 9780226807942

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