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In his 1994 book The Language Instinct, he wrote: The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard. The mental abilities of a four-year-old that we take for granted – recognizing a face, lifting a pencil, walking across a room, answering a question – in fact solve some of ...
A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to solve it, one would need to implement AGI, because the solution is beyond the capabilities of a purpose-specific algorithm. [44] There are many problems that have been conjectured to require general intelligence to solve as well as humans.
Question answering systems have been extended in recent [may be outdated as of April 2023] years to encompass additional domains of knowledge [21] For example, systems have been developed to automatically answer temporal and geospatial questions, questions of definition and terminology, biographical questions, multilingual questions, and ...
On the other hand, a problem is AI-Hard if and only if there is an AI-Complete problem that is polynomial time Turing-reducible to . This also gives as a consequence the existence of AI-Easy problems, that are solvable in polynomial time by a deterministic Turing machine with an oracle for some problem.
NLU has been considered an AI-hard problem. [2] There is considerable commercial interest in the field because of its application to automated reasoning, [3] machine translation, [4] question answering, [5] news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.
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NP is the set of decision problems for which the problem instances, where the answer is "yes", have proofs verifiable in polynomial time by a deterministic Turing machine, or alternatively the set of problems that can be solved in polynomial time by a nondeterministic Turing machine. [2] [Note 1]
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