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AI-completeness
The most difficult problems are informally known as AI-complete or AI-hard, by analogy with NP-complete and NP-hard in complexity theory, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI. Since many AI problems have no formalisation yet, conventional complexity theory does not allow the definition of AI-completeness. To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm. They include:
• Computer vision (and subproblems such as object recognition)
• Natural language understanding (and subproblems such as text mining, machine translation, and word sense disambiguation)
• Dealing with unexpected circumstances while solving any real world problem, whether it's navigation or planning or even the kind of reasoning done by expert systems.
• Peer Review
Bongard problems
• Automatic speech recognition
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📚 Index of Bongard Problems
by Mikhail Bongard, Douglas Hofstadter, Harry Foundalis, ...

http://www.foundalis.com/res/bps/bpidx.htm