Go is in that weird spot that chess was for ~decades[0] where the best humans could beat some of the best engines but it was getting harder, until Rybka, Stockfish and others closed the door and continued far beyond human ability (measured by ELO). AlphaGo is barely a decade old, and it does seem like progress on games has taken a decade or more to become fully superhuman from the first challenges to human world champions.
I think it is the case that when the deep learning approach Stockfish used became superhuman it very quickly became dramatically superhuman within a few years/months despite years of earlier work and slow growth. There seems to be explosive gains in capability at ~years-long intervals.
Similarly, most capability gains in math, essay writing, and writing code have periods of explosive growth and periods of slow growth. So far none of the trends in these three at human level have more than ~5 years of history; earlier systems could provide rudimentary functionality but were significantly constrained by specially designed harnesses or environments they operated within as opposed to the generality of LLMs.
So I think the phrase “do X at all” really applies to the general way that deep learning has allowed ML to do X with significantly fewer or no harnesses. Constraint search and expert systems have been around for decades with slow improvements but deep learning is not a direct offshoot of those approaches and so not quite the same “AI” doing X to compare the progress over time.
I think it is the case that when the deep learning approach Stockfish used became superhuman it very quickly became dramatically superhuman within a few years/months despite years of earlier work and slow growth.
Are you thinking of Alpha Chess Zero? Stockfish didn’t have anything to do with deep learning until they started using NNUE evaluation (which currently uses Leela Chess Zero training data).
Go is in that weird spot that chess was for ~decades[0] where the best humans could beat some of the best engines but it was getting harder, until Rybka, Stockfish and others closed the door and continued far beyond human ability (measured by ELO). AlphaGo is barely a decade old, and it does seem like progress on games has taken a decade or more to become fully superhuman from the first challenges to human world champions.
I think it is the case that when the deep learning approach Stockfish used became superhuman it very quickly became dramatically superhuman within a few years/months despite years of earlier work and slow growth. There seems to be explosive gains in capability at ~years-long intervals.
Similarly, most capability gains in math, essay writing, and writing code have periods of explosive growth and periods of slow growth. So far none of the trends in these three at human level have more than ~5 years of history; earlier systems could provide rudimentary functionality but were significantly constrained by specially designed harnesses or environments they operated within as opposed to the generality of LLMs.
So I think the phrase “do X at all” really applies to the general way that deep learning has allowed ML to do X with significantly fewer or no harnesses. Constraint search and expert systems have been around for decades with slow improvements but deep learning is not a direct offshoot of those approaches and so not quite the same “AI” doing X to compare the progress over time.
[0] https://www.reddit.com/r/chess/comments/xtjstq/the_strongest_engines_over_time/
Are you thinking of Alpha Chess Zero? Stockfish didn’t have anything to do with deep learning until they started using NNUE evaluation (which currently uses Leela Chess Zero training data).