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Weak artificial intelligence (weak AI) is artificial intelligence that implements a limited part of the mind, or, as Artificial Narrow Intelligence, [1] [2] [3] is focused on one narrow task. Weak AI is contrasted with strong AI , which can be interpreted in various ways:
If a narrow AI algorithm can play checkers, it can’t play chess. Examples include algorithms that only detect NSFW images and recommendation engines designed to tell you what Amazon or Etsy ...
[a] In contrast, weak AI (or narrow AI) is able to solve one specific problem but lacks general cognitive abilities. [22] [19] Some academic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the same sense as humans. [a]
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 ...
Artificial intelligence, in its current state, inherits many of the limitations of the probabilistic paradigm. Its algorithms optimize for efficiency, prediction, and pattern recognition within ...
In the philosophy of artificial intelligence, GOFAI ("Good old fashioned artificial intelligence") is classical symbolic AI, as opposed to other approaches, such as neural networks, situated robotics, narrow symbolic AI or neuro-symbolic AI. [1] [2] The term was coined by philosopher John Haugeland in his 1985 book Artificial Intelligence: The ...
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
Artificial intelligence is used in astronomy to analyze increasing amounts of available data [160] [161] and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and distinguishing ...
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