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2. Turing test - Wikipedia

   en.wikipedia.org/wiki/Turing_test

   Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. Multiple tests of artificial-intelligence efficacy are needed because, "just as there is no single test of athletic prowess, there cannot be one ultimate test of intelligence." One such test, a ...

3. Weak artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Weak_artificial_intelligence

   John Searle contests the possibility of strong AI (by which he means conscious AI). He further believes that the Turing test (created by Alan Turing and originally called the "imitation game", used to assess whether a machine can converse indistinguishably from a human) is not accurate or appropriate for testing whether an AI is "strong".

4. Artificial general intelligence - Wikipedia

   en.wikipedia.org/.../Artificial_general_intelligence

   Artificial general intelligence (AGI) is a type of artificial intelligence (AI) that matches or surpasses human cognitive capabilities across a wide range of cognitive tasks. This contrasts with narrow AI , which is limited to specific tasks. [ 1 ]

5. A beginner’s guide to AI: The difference between human and ...

   www.aol.com/news/beginner-guide-ai-difference...

   The guide contains articles on (in order published) neural networks, computer vision, natural language processing, algorithms, artificial general intelligence, and the difference between video ...

6. Intelligent agent - Wikipedia

   en.wikipedia.org/wiki/Intelligent_agent

   Unlike the Turing test, it does not refer to human intelligence in any way. Thus, there is no need to discuss if it is "real" vs "simulated" intelligence (i.e., "synthetic" vs "artificial" intelligence) and does not indicate that such a machine has a mind, consciousness or true understanding. It seems not imply John Searle's "strong AI ...

7. Neats and scruffies - Wikipedia

   en.wikipedia.org/wiki/Neats_and_scruffies

   Modern AI has elements of both scruffy and neat approaches. Scruffy AI researchers in the 1990s applied mathematical rigor to their programs, as neat experts did. [5] [6] They also express the hope that there is a single paradigm (a "master algorithm") that will cause general intelligence and superintelligence to emerge. [7]