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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Glossary of artificial intelligence | 20/21 | https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence | reference | science, encyclopedia | 2026-05-05T07:50:25.401446+00:00 | kb-cron |
stochastic semantic analysis An approach used in computer science as a semantic component of natural language understanding. Stochastic models generally use the definition of segments of words as basic semantic units for the semantic models, and in some cases involve a two layered approach.
Stanford Research Institute Problem Solver (STRIPS) An automated planner developed by Richard Fikes and Nils Nilsson in 1971 at SRI International.
subject-matter expert (SME) A person who has accumulated great knowledge in a particular field or topic, demonstrated by the person's degree, licensure, and/or through years of professional experience with the subject.
superintelligence A hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. Superintelligence may also refer to a property of problem-solving systems (e.g., superintelligent language translators or engineering assistants) whether or not these high-level intellectual competencies are embodied in agents that act within the physical world. A superintelligence may or may not be created by an intelligence explosion and be associated with a technological singularity.
supervised learning The machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
support vector machines In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression.
swarm intelligence (SI) The collective behavior of decentralized, self-organized systems, either natural or artificial. The expression was introduced in the context of cellular robotic systems.
symbolic artificial intelligence The term for the collection of all methods in artificial intelligence research that are based on high-level "symbolic" (human-readable) representations of problems, logic, and search.
synthetic intelligence (SI) An alternative term for artificial intelligence which emphasizes that the intelligence of machines need not be an imitation or in any way artificial; it can be a genuine form of intelligence.
systems neuroscience A subdiscipline of neuroscience and systems biology that studies the structure and function of neural circuits and systems. It is an umbrella term, encompassing a number of areas of study concerned with how nerve cells behave when connected together to form neural pathways, neural circuits, and larger brain networks.
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technological singularity Also simply the singularity.A hypothetical point in the future when technological growth becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization. temporal difference learning A class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods.
tensor network theory A theory of brain function (particularly that of the cerebellum) that provides a mathematical model of the transformation of sensory space-time coordinates into motor coordinates and vice versa by cerebellar neuronal networks. The theory was developed as a geometrization of brain function (especially of the central nervous system) using tensors.
TensorFlow A free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.
The Age of Spiritual Machines: When Computers Exceed Human Intelligence Non-fiction book by Ray Kurzweil, published in 1999, in which he forecasts the future of AI.
theoretical computer science (TCS) A subset of general computer science and mathematics that focuses on more mathematical topics of computing and includes the theory of computation.
theory of computation In theoretical computer science and mathematics, the theory of computation is the branch that deals with how efficiently problems can be solved on a model of computation, using an algorithm. The field is divided into three major branches: automata theory and languages, computability theory, and computational complexity theory, which are linked by the question: "What are the fundamental capabilities and limitations of computers?".
Thompson sampling A heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists in choosing the action that maximizes the expected reward with respect to a randomly drawn belief.
time complexity The computational complexity that describes the amount of time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to differ by at most a constant factor.
transfer learning A machine learning technique in which knowledge learned from a task is reused in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
transformer A type of deep learning architecture that exploits a multi-head attention mechanism. Transformers address some of the limitations of long short-term memory, and became widely used in natural language processing, although it can also process other types of data such as images in the case of vision transformers.