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## Main Question or Discussion Point

Here I would like to see interesting challenges for artificial intelligence, which seem very hard for AI and yet in the spirit of what AI usually does.

Here is my proposal: Let us feed the machine with a big sample (containing, say, a million numbers) of prime numbers and a big sample of non-prime numbers, without feeding it with a general definition of the prime number. The machine must learn to recognize (not necessarily with 100% accuracy) prime numbers from non-prime numbers, for cases which were not present in the initial sample.

The challenge looks hard because the distribution of prime numbers looks rather random, yet not prohibitively hard because there is a strict simple rule which defines what is and what isn't a prime number.

Here is my proposal: Let us feed the machine with a big sample (containing, say, a million numbers) of prime numbers and a big sample of non-prime numbers, without feeding it with a general definition of the prime number. The machine must learn to recognize (not necessarily with 100% accuracy) prime numbers from non-prime numbers, for cases which were not present in the initial sample.

The challenge looks hard because the distribution of prime numbers looks rather random, yet not prohibitively hard because there is a strict simple rule which defines what is and what isn't a prime number.

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