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> In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of chall
by mindgam3 7y ago
> In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics.
<rant>
DeepMind "superhuman" hype machine strikes again.
I mean, it's cool that computers are getting even better at chess and all (and other perfectly constrained game environments), but come on. "Superhuman" chess performance hasn't been particularly interesting since Deep Blue vs Kasparov in 1997.
The fact that the new algorithms have "no knowledge of underlying dynamics" makes it sound like an entirely new approach, and on one level it is. ML vs non-statistical methods. But on a deeper level, it's the same shit.
Unless I'm grossly mistaken, (someone please correct me if this is inaccurate), the superhuman performance is only made possible by massive compute. In other words, brute force.
But it uses less training cycles, you say! AlphaZero et all mastered the game in only 3 days! etc etc. This conveniently ignores the fact that this was 3 days of training on an array of GPUs that is way more powerful than the supercomputers of old.
Don't get me wrong. These ML algorithms have value and can solve real problems. I just really wish DeepMind's marketing department would stop beating us over the head with all of this "superhuman" marketing.
For those just tuning in, this is the same company that got the term "digital prodigy" on the cover of Science [0]. Which is again a form of cheating, because the whole prodigy aspect conveniently ignores the compute power required to achieve AlphaZero. For the record, if you took A0 and ran it on hardware from a few years ago, you would have a computer that achieves superhuman performance after a very long time, which wouldn't be making headlines.
</rant>
0. https://science.sciencemag.org/content/362/6419 https://science.sciencemag.org/content/362/6419
- sanxiyn 7y agoOkay, I will bite. It uses less training cycles. Why is that not significant? Both AlphaZero and MuZero are brute force, but MuZero is less brute force than AlphaZero, so it's heading in the right direction.
- mindgam3 7y agoSure, but both of them are more brute force than Deep Blue (unless anybody has a compelling counter argument), so we’re actually heading in the wrong direction.
- sanxiyn 7y agoIt should be pointed out AlphaZero plays better than Deep Blue. I think comparing computational resource usage is important, but direct comparison only makes sense with equivalent performance level.
- mindgam3 7y agoI did point this out at the top of my original comment. “I mean, it's cool that computers are getting even better at chess and all“ > direct comparison only makes sense with equivalent performance level This makes no sense to me. 50% increase in performance can be compared to 50% increase in processing power to evaluate level of brute force-ness.
- SonOfLilit 7y agoYes, but how is "50% better" measured? On what scale?
- mindgam3 7y agoEasy. Chess uses the Elo rating system. https://en.m.wikipedia.org/wiki/Elo_rating_system https://en.m.wikipedia.org/wiki/Elo_rating_system
- SonOfLilit 7y agoI mean that ELO is not linear and there is no obvious ratio operation for ELO scores. How much is better than 2,600? Is it 5,200? Is it the rating at which you'd have 2:1 odds of winning a match? (Wikipedia says 200 points represent 74% chance of winning, so somewhere below 2,800) There's just no commonly agreed meaning of "50% better Chess player".