4 ms·

The "success of Alpha Zero" was largery a PR stunt and the field has moved on long since then.

singularity2001 4y ago
huh? what could op mean by 'The "success of Alpha Zero" was largery a PR stunt and the field has moved on long since then.' The commenter is suggesting that the success of Alpha Zero was largely exaggerated for public relations purposes, and that advancements in the field have since progressed beyond it. This likely implies that the commenter considers Alpha Zero to be less significant than it was initially made out to be. I still don't get the phrasing "PR stunt" in this context
- mtlmtlmtlmtl 4y ago
  The match between alpha zero and stockfish was a PR stunt specifically because the match was partially fixed; Stockfish was run with unoptimal settings without an opening book. AlphaGo and AlphaZero Go were a significant achievement. AlphaZero was just applying the same training process to western chess with a huge amount of compute, running it against stockfish on a handful CPUs with suspicious settings and making a big fuzz about the result. There are good reasons to believe that the searching method and size of neural net employed in AlphaZero, while very strong for go, is just overkill for a game like chess, and that the conventional techniques are just a far better fit. The best reason is the almost total dominance of Stockfish since the PR stunt. Another is that chess is in a different complexity class than go, so it's only reasonable to expect that different algorithms will be superior in chess versus go.
  - singularity2001 4y ago
    very insightful, thanks
    - mtlmtlmtlmtl 4y ago
      Also of note is Leela Chess Zero, which is essentially the open source, weights available reimplementation of AlphaZero, trained using decentralised training among other things, I believe. A lot of the work Deepmind did with AlphaZero had to be painstakingly redone by the community and it took years. I'd feel bad if I didn't mention them too, they've done a great job. I'm working on my own weird ideas within this space that are quite different from both stockfish and leela zero(but partially taking inspiration from both) but that's still very much under construction...
kzrdude 4y ago
I don't think it can be so easily dismissed. AlphaZero contributed lots, for example reproducing opening theory from a blank slate. For chess that's significant. But we don't care about chess for this discussion about GPT. Self-learning and self-play would be interesting because it's an amplification factor. Some searching suggests some researchers have been thinking along these lines. Maybe this? https://arxiv.org/abs/2210.11610 https://arxiv.org/abs/2210.11610 Going for methods that don't require supervision
- mtlmtlmtlmtl 4y ago
  Yes, AlphaZero was a nice innovation in training methods, employing iterated distillation and amplification. Of course a game is the perfect application for it because you get the fitness function built in: just play the game. There does seem to be some research into applying the same methods to alignment problems, which is interesting. Unfortunately Google's naming scheme is kind of confusing. AlphaGo is the go engine that has some human knowledge built in. Then supposedly AlphaZero is the generalised thing that can be trained on go or chess or shogi etc. But AlphaZero trained on chess is also referred to as just AlphaZero? Whereas AlphaZero trained on Go is called AlphaZero Go... I should have been clearer on which I meant I guess, my response to your sibling should clear up what I meant though.

singularity2001 4y ago

huh? what could op mean by 'The "success of Alpha Zero" was largery a PR stunt and the field has moved on long since then.' The commenter is suggesting that the success of Alpha Zero was largely exaggerated for public relations purposes, and that advancements in the field have since progressed beyond it. This likely implies that the commenter considers Alpha Zero to be less significant than it was initially made out to be. I still don't get the phrasing "PR stunt" in this context

mtlmtlmtlmtl 4y ago
The match between alpha zero and stockfish was a PR stunt specifically because the match was partially fixed; Stockfish was run with unoptimal settings without an opening book. AlphaGo and AlphaZero Go were a significant achievement. AlphaZero was just applying the same training process to western chess with a huge amount of compute, running it against stockfish on a handful CPUs with suspicious settings and making a big fuzz about the result. There are good reasons to believe that the searching method and size of neural net employed in AlphaZero, while very strong for go, is just overkill for a game like chess, and that the conventional techniques are just a far better fit. The best reason is the almost total dominance of Stockfish since the PR stunt. Another is that chess is in a different complexity class than go, so it's only reasonable to expect that different algorithms will be superior in chess versus go.
- singularity2001 4y ago
  very insightful, thanks
  - mtlmtlmtlmtl 4y ago
    Also of note is Leela Chess Zero, which is essentially the open source, weights available reimplementation of AlphaZero, trained using decentralised training among other things, I believe. A lot of the work Deepmind did with AlphaZero had to be painstakingly redone by the community and it took years. I'd feel bad if I didn't mention them too, they've done a great job. I'm working on my own weird ideas within this space that are quite different from both stockfish and leela zero(but partially taking inspiration from both) but that's still very much under construction...

kzrdude 4y ago

I don't think it can be so easily dismissed. AlphaZero contributed lots, for example reproducing opening theory from a blank slate. For chess that's significant. But we don't care about chess for this discussion about GPT. Self-learning and self-play would be interesting because it's an amplification factor. Some searching suggests some researchers have been thinking along these lines. Maybe this? https://arxiv.org/abs/2210.11610 https://arxiv.org/abs/2210.11610 Going for methods that don't require supervision

mtlmtlmtlmtl 4y ago
Yes, AlphaZero was a nice innovation in training methods, employing iterated distillation and amplification. Of course a game is the perfect application for it because you get the fitness function built in: just play the game. There does seem to be some research into applying the same methods to alignment problems, which is interesting. Unfortunately Google's naming scheme is kind of confusing. AlphaGo is the go engine that has some human knowledge built in. Then supposedly AlphaZero is the generalised thing that can be trained on go or chess or shogi etc. But AlphaZero trained on chess is also referred to as just AlphaZero? Whereas AlphaZero trained on Go is called AlphaZero Go... I should have been clearer on which I meant I guess, my response to your sibling should clear up what I meant though.