| I have read and own Genetic Programming III by John Koza https://www.amazon.com/Genetic-Programming-III-Darwinian-Inv... and the best part about it was that it revisited problems that had been only superficially explored with GP a decade before. The increased computing power available allowed for multiple runs and provided insights into what parameters to tune and gave hard numbers on how much computation was needed to solve various classes of problems. In the end, it doesn't matter that much which approach is taken because it's all classification problems. We just need the solution matrix, and ideally what computation went into solving it. I feel that this simple fact is lost amidst the complexity of how ML is taught today. ML isn’t accelerating because of better code or research breakthroughs either. It’s happening because the big CPU manufacturers didn’t do anything for 20 years and GPU manufactures had their lunch. ML is straightforward, even trivial in some cases with effectively unlimited cores and bandwidth. We’re just rediscovering parallelization algorithms that were well known in functional programming generations ago. These discoveries are inevitable in a suitable playground. I used to have this fantasy that I would get ahead of the curve enough to be able to dabble in the last human endeavor but I'm beginning to realize that that's probably never going to happen. Machines will soon beat humans in pretty much every category, and not because someone figures out how to make it all work, but because there simply isn't enough time to stop it now. There are a dozen teams around the world racing to solve any problem and anyone’s odds of being first are perhaps 10% at best. Compounded with darwinian capitalism, the risk/reward equation is headed towards infinity so fast that it’s looking like the smartest move is not to play. Barring a dystopian future or cataclysm, I give us 10 years, certainly no more than 20, before computers can do anything people can do, at least economically. And the really eerie thing is that that won’t be the most impressive thing happening, because kids will know it’s all just hill climbing and throwing hardware at problems. It will be all the other associated technologies that come about as people abandon the old hard ways of doing things. |