AI is just applied statistics
Current “AI” is nothing but a fancy name for what used to be called “applied statistics”. Everything about “AI” was already known 40 years ago. What’s new today is that we now have GPU’s (ironically, invented for playing video games) and statistics can be applied at a truly massive scale. Where before statistics operated with thousands of data points we can now crunch millions and hundreds of millions.
Even more ironically, from the point of view of actual science, “AI research” is a regression, not progress. Inferring useful conclusions from hundreds of data points requires complex and nuanced math. When you have millions of data points, you can just throw plain old logistic regressions at the problem (the dumbest tool in the statistician toolbox) and get satisfactory results.
Modern “AI” “research” consists of low-skilled math graduates cleaning up data manually. (A.k.a. “feature selection”.) It’s creative work, but certainly not science. 90% of the time it’s just blindly trying random features until you hit on something that gives slightly better results. “Deep learning”, a.k.a “neural networks” is just the snakeoil name for logistic regression, which dates back to the 1930′s.
a) The programs aren’t “self-adapting”. The only practical self-adapting code is found in malware. So-called “AI” is code with self-adapting coefficients, which is exactly what regression analysis is — adapting coefficients based on changing inputs.
b) A neural network doesn’t “learn” anything. A so-called neural network is mathematically exactly equivalent to a logistic regression, and cannot “learn” something any more than a logistic regression can.
c) The fact that said coefficients aren’t directly interpretable means nothing, please don’t ascribe some sort of mystical value to this fact. Any change of basis can give you uninterpretable vectors. (E.g., a plain old SVD.)
There’s no such thing as “AI”. AI is just the snakeoil salesman name for applied statistics. The whole field is snakes and oil all the way down. (Not surprising when there’s so much easy money to be made here.) Even with a lot "collective intelligence", there is still a need for high-intellect individuals to generate the unique insights that push things forward. A roomful of well-intentioned people is no match for a lone genius with an IQ of say 160.