Machine Learning Perspective on the Twin Prime Conjecture

This article focuses on the machine learning aspects of the problem, and the use of pattern recognition techniques leading to interesting, new findings about twin primes. Twin primes are prime numbers p such that p + 2 is also prime. For instance, 3 and 5, or 29 and 31. A famous, unsolved and old mathematical conjecture states that there are infinitely many such primes, but a proof still remains elusive to this day. Twin primes are far rarer than primes: there are infinitely more primes than there are twin primes, in the same way that that there are infinitely more integers than there are prime integers.

Here I discuss the results of my experimental math research, based on big data, algorithms, machine learning, and pattern discovery. The level is accessible to all machine learning practitioners. I first discuss my experimentations in section 1, and then how it relates to the twin prime conjecture, in section 2. Mathematicians may be interested as well, as it leads to a potential new path to prove this conjecture. But machine learning readers with little time, not curious about the link to the mathematical aspects, can read section 1 and skip section 2.

I do not prove the twin prime conjecture (yet). Rather, based on data analysis, I provide compelling evidence (the strongest I have ever seen), supporting the fact that it is very likely to be true. It is not based on heuristic or probabilistic arguments (unlike this version dating back to around 1920), but on hard counts and strong patterns.

This is not different from analyzing data and finding that smoking is strongly correlated with lung cancer: the relationship may not be causal as there might be confounding factors. In order to prove causality, more than data analysis is needed (in the case of smoking, of course causality has been firmly established long ago.)

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