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My thoughts, not being in the field, are parallel to the parent post. "It's nice and all that we're achieving better and better computer performance at things that used to require the human brain, but it seems we're doing so by building larger and larger computers."Not to detract from that achievement, I love large computers in their own right! I'm a dabbler in Go, and "somewhere below professional" at the game of poker. I've followed the advances in the latter for more than a decade, eagerly reading every paper the CPRG publishes. They use a LOT of compute power! I know from experience that "The specific settings matter a lot.". For several years, I made my living "implementing papers for hire". It's real work, no argument there. Sometimes the settings are the solution, and heck, sometimes the published algorithm is outright wrong, and you only discover so when trying to implement it. But the second part of your point, that it's not simply achieving more performance by throwing more transistors at it, I don't have experience with, and I sorta don't believe you. :) Your comment is quite well written, making me (irrationally?) predisposed to suspect you're correct on factual matters, or at least more of a domain expert than I. Can you cite sources, or simply elaborate more? |
Yes, and in the case of deep RL, the ability to to get "lucky" random initialization seems to (still) matter a lot.
I work in real time control systems, which are roughly decision making under uncertainty problems. A lot of the RL research has become noise buoyed with large marketing budgets.