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Recursive self improvement (aka seed AI) – speculative ability of strong artificial intelligence to reprogram itself to make itself even more intelligent. The more intelligent it got, the more capable it would be of further improving itself, in successively more rapid iterations, potentially resulting in an intelligence explosion leading to ...
Blue Brain Project, an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level. [1] Google Brain, a deep learning project part of Google X attempting to have intelligence similar or equal to human-level. [2] Human Brain Project, ten-year scientific research project, based on exascale ...
Generative artificial intelligence (generative AI, GenAI, [166] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 167 ] [ 168 ] [ 169 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 170 ...
In artificial intelligence, symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) [1] [2] is 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. [3]
Cyc (pronounced / ˈ s aɪ k / SYKE) is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works. Hoping to capture common sense knowledge, Cyc focuses on implicit knowledge.
A* is an informed search algorithm, or a best-first search, meaning that it is formulated in terms of weighted graphs: starting from a specific starting node of a graph, it aims to find a path to the given goal node having the smallest cost (least distance travelled, shortest time, etc.).
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
Specific applications of search algorithms include: Problems in combinatorial optimization, such as: . The vehicle routing problem, a form of shortest path problem; The knapsack problem: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as ...