23 lines
5.2 KiB
Markdown
23 lines
5.2 KiB
Markdown
---
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title: "Artificial general intelligence"
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chunk: 3/8
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source: "https://en.wikipedia.org/wiki/Artificial_general_intelligence"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T11:01:56.982213+00:00"
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instance: "kb-cron"
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---
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At the turn of the century, many mainstream AI researchers hoped that strong AI could be developed by combining programs that solve various sub-problems. Hans Moravec wrote in 1988: I am confident that this bottom-up route to artificial intelligence will one day meet the traditional top-down route more than halfway, ready to provide the real-world competence and the commonsense knowledge that has been so frustratingly elusive in reasoning programs. Fully intelligent machines will result when the metaphorical golden spike is driven, uniting the two efforts.
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However, even at the time, this was disputed. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the symbol grounding hypothesis by stating: The expectation has often been voiced that "top-down" (symbolic) approaches to modeling cognition will somehow meet "bottom-up" (sensory) approaches somewhere in between. If the grounding considerations in this paper are valid, then this expectation is hopelessly modular and there is really only one viable route from sense to symbols: from the ground up. A free-floating symbolic level like the software level of a computer will never be reached by this route (or vice versa) – nor is it clear why we should even try to reach such a level, since it looks as if getting there would just amount to uprooting our symbols from their intrinsic meanings (thereby merely reducing ourselves to the functional equivalent of a programmable computer).
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=== Feasibility ===
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As of 2023, the development and potential achievement of AGI remains a subject of intense debate within the AI community. While traditional consensus held that AGI was a distant goal, recent advancements have led some researchers and industry figures to claim that early forms of AGI may already exist. AI pioneer Herbert A. Simon speculated in 1965 that "machines will be capable, within twenty years, of doing any work a man can do". This prediction failed to come true. Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century because it would require "unforeseeable and fundamentally unpredictable breakthroughs" and a "scientifically deep understanding of cognition". Writing in The Guardian, roboticist Alan Winfield claimed in 2014 that the gulf between modern computing and human-level artificial intelligence is as wide as the gulf between current space flight and practical faster-than-light spaceflight.
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An additional challenge is the lack of clarity in defining what intelligence entails. Does it require consciousness? Must it display the ability to set goals as well as pursue them? Is it purely a matter of scale such that if model sizes increase sufficiently, intelligence will emerge? Are facilities such as planning, reasoning, and causal understanding required? Does intelligence require explicitly replicating the brain and its specific faculties? Does it require emotions?
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Most AI researchers believe strong AI can be achieved in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of achieving strong AI. John McCarthy is among those who believe human-level AI will be accomplished, but that the present level of progress is such that a date cannot accurately be predicted. AI experts' views on the feasibility of AGI wax and wane. Four polls conducted in 2012 and 2013 suggested that the median estimate among experts for when they would be 50% confident AGI would arrive was 2040 to 2050, depending on the poll, with the mean being 2081. Of the experts, 16.5% answered with "never" when asked the same question, but with a 90% confidence instead. Further current AGI progress considerations can be found above Tests for confirming human-level AGI.
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A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that "over [a] 60-year time frame there is a strong bias towards predicting the arrival of human-level AI as between 15 and 25 years from the time the prediction was made". They analyzed 95 predictions made between 1950 and 2012 on when human-level AI will come about.
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In 2023, Microsoft researchers published a detailed evaluation of GPT-4. They concluded: "Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system." Another study in 2023 reported that GPT-4 outperforms 99% of humans on the Torrance tests of creative thinking.
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Blaise Agüera y Arcas and Peter Norvig wrote in 2023 the article "Artificial General Intelligence Is Already Here", arguing that frontier models had already achieved a significant level of general intelligence. They wrote that reluctance to this view comes from four main reasons: a "healthy skepticism about metrics for AGI", an "ideological commitment to alternative AI theories or techniques", a "devotion to human (or biological) exceptionalism", or a "concern about the economic implications of AGI".
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=== Timescales === |