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Going back to the original "Bitter Lesson" article, I think the analogy to chess computers could be instructive here. A lot of institutional resources were spen
by lsy 2y ago
Going back to the original "Bitter Lesson" article, I think the analogy to chess computers could be instructive here. A lot of institutional resources were spent trying to achieve "superhuman" chess performance, it was achieved, and today almost the entire TAM for computer chess is covered by good-enough Stockfish, while most of the money tied up in chess is in matching human players with each other across the world, and playing against computers is sort of what you do when you're learning, or don't have an internet connection, or you're embarrassed about your skill and don't want to get trash-talked by an Estonian teenager.
The "Second Bitter Lesson" of AI might be that "just because massive amounts of compute make something possible doesn't mean that there will be a commensurately massive market to justify that compute".
"Bitter Lesson" I think also underplays the amount of energy and structure and design that has to go into compute-intensive systems to make them succeed: Deep Blue and current engines like Stockfish take advantage of tablebases of opening and closing positions that are more like GOFAI than deep tree search. And the current crop of LLMs are not only taking advantage of expanded compute, but of the hard-won ability of companies in the 21st century to not only build and resource massive server farms, but mobilize armies of contractors in low-COL areas to hand-train models into usefulness.
- diego_sandoval 2y agoThe main useful outcome we get from chess is entertainment. Entertainment that comes from a Human vs. Human match is higher than Human vs. AI, at least for spectators. But many sectors of the economy don't gain much from it being done by humans. I don't care if my car was made by all humans or all robots, as long as it's the best car I can get for the money. I think you're extrapolating a bit too much from the specific case of chess.
- ip26 2y agoIt’s not really about how the compute-intensive resources come to bear. You can draw a parallel to Moore’s law. Node advancement is one of the most expensive and cutting edge efforts by humanity today. But it’s also simultaneously true that software companies have succeeded or failed by betting for or against computers getting faster. There are famous examples of companies in the 80’s that designed software that was simply not usable on the computers on hand when the project began, but was incredible on the (much faster) computers of launch day. The bitter lesson is very similar. In essence, when building on top of AI models, bet on the AI models getting much faster and more capable.
- immibis 2y agoAnd there is software today that is simply not usable on computers today, but will be incredible on computers in 20 years time if clock speed continue doubling every 2 years. Most of it is written in Electron.
- ip26 2y agoHah, point hilariously made. Although I might argue electron commits the sin of betting on endless increases in memory performance :)