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[–] 4 points 3 months ago (2 children)

When a simpler, search-based approach with special hardware and software proved vastly more effective, these human-knowledge-based chess researchers were not good losers. They said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess. These researchers wanted methods based on human input to win and were disappointed when they did not.

careful; lemmings HATE ai and and pro-ai stance is going to get you blacklisted. lol

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  • [–] 2 points 3 months ago

    Don't over-focus on chess. The Lesson is that if you take two identical machine-learning labs which currently have a working model today, and ask them to come back and show you how much they improved in a year or so, the lab which forward-ports the model to newer hardware will tend to outperform the lab which tries to tweak the model to fit the problem domain more precisely. Sheer amounts of compute dominate any sort of domain-specific (here, chess-specific) knowledge. Further, there's a suggested mechanism, Moore's Law, which is tapering off now but used to be exponential; how could a polynomial amount of effort ever compete with exponentially-better new hardware?

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