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> From where I'm sitting it looks like, "Google spent a fortune on deep learning, and got a small but real win. People who don't like Google failed to follow Go

by bsder 2y ago

> From where I'm sitting it looks like, "Google spent a fortune on deep learning, and got a small but real win. People who don't like Google failed to follow Google's recipe and got a large and easily replicated loss." From where I'm sitting it looks like Google cooked the books maximally, barely beat humans let alone state of the art algorithms, published a crappy article in Nature because it would never have passed editorial muster at something like DAC or an IEEE journal and now have to browbeat other people who are calling them out on it. And that's the best interpretation we can cough up. I'll go further, we don't even have any raw data that says that they actually did beat the humans. Some of the humans I know who run P&R are REALLY good at what they do. The data could be completely made up. Given how much scientific fraud has come out lately, I'm amazed at the number of people defending Google on this. Where I'm from, we call what Google is doing both "lying" and "bullying". Look, Google can easily defuse this in all manner of ways. Publish their raw data. Run things on testbenches and benchmarks that the EDA tools vendors have been running on for years. Run things on the open source VLSI designs that they sponsored. What I suspect happened is that Google's AI group has gotten used to being able to make hyperbolic marketing claims which are difficult to verify. They then poked at place and route, failed, and published an article anyway because someone's promotion is tied to this. They expected that everybody would swallow their glop just like every other time, be mostly ignored and the people involved can get their promotions and move on. Unfortunately, Google is shoveling bullshit around something that has objective answers; real money is at stake; and they're getting rightfully excoriated for it. Whoops.

btilly 2y ago
Look, either the follow-up article did pretraining or not. Jeff Dean is claiming that the importance of pretraining was mentioned 37 times and the follow-up didn't do it. That sounds easy to verify. Likewise the importance of spending 20x as much money on the training portion seems easy to verify, and significant. That they would fail to properly test against industry standard workbenches seems reasonable to me. This is a bunch of ML specialists who know nothing about chip design. Their background is beating everyone at Go and setting a new state of the art for protein folding, and not chip design. If you dismiss those particular past accomplishments as hyperbolic marketing, that's your decision. But you aren't going to find a lot of people in these parts who agree with you. If you think that those were real, but that a bunch of more recent accomplishments are BS, I haven't been following closely enough to have an opinion. The stuff that crossed my radar since AlphaFold is mostly done at places like OpenAI, and not Google. Regardless, the truth will out. And what Google is claiming for itself here really isn't all that impressive.
- dogleg77 2y ago
  Reading those papers and looking at the code, it doesn't look easy. However, let's imagine that the Cheng et al team comes back with results for pretraining a few months from now, and they support the conclusions of their earlier paper. What should they do to help everyone reach a conclusion?
  - wholehog 2y ago
    Pre-training is just training on multiple chips. "If Cheng et al. had reached out to the corresponding authors of the Nature paper, we would have gladly helped them to correct these issues prior to publication" (https://arxiv.org/pdf/2411.10053 https://arxiv.org/pdf/2411.10053) That's how you actually do a reproduction study - you reach out to the corresponding authors and make sure you do everything exactly the same. But at this point, it's hard to imagine the AlphaChip folks having much patience with them.
wholehog 2y ago
> published a crappy article in Nature because it would never have passed editorial muster at something like DAC or an IEEE journal and now have to browbeat other people who are calling them out on it. I don't think it's easier to get into DAC / an IEEE journal than Nature. Their human baseline was the TPU physical design team, with access to the best available tools: rdcu.be/cmedX and this is still the baseline to beat in order to get used in production, which has happened for multiple generations of TPU. TPU is export controlled and super confidential -- multi-billion dollar IP! -- so I don't see raw data coming out anytime soon.
- dogleg77 2y ago
  Nature papers get retracted every year. I have not heard of DAC papers retracted. If the Nature paper made it clear that RL is not seriously expected to work on non-TPU chips, it would have have probably been rejected. If RL works on many other chips, then evidence should be easy to publish.

by bsder 2y ago

btilly 2y ago

Look, either the follow-up article did pretraining or not. Jeff Dean is claiming that the importance of pretraining was mentioned 37 times and the follow-up didn't do it. That sounds easy to verify. Likewise the importance of spending 20x as much money on the training portion seems easy to verify, and significant. That they would fail to properly test against industry standard workbenches seems reasonable to me. This is a bunch of ML specialists who know nothing about chip design. Their background is beating everyone at Go and setting a new state of the art for protein folding, and not chip design. If you dismiss those particular past accomplishments as hyperbolic marketing, that's your decision. But you aren't going to find a lot of people in these parts who agree with you. If you think that those were real, but that a bunch of more recent accomplishments are BS, I haven't been following closely enough to have an opinion. The stuff that crossed my radar since AlphaFold is mostly done at places like OpenAI, and not Google. Regardless, the truth will out. And what Google is claiming for itself here really isn't all that impressive.

dogleg77 2y ago
Reading those papers and looking at the code, it doesn't look easy. However, let's imagine that the Cheng et al team comes back with results for pretraining a few months from now, and they support the conclusions of their earlier paper. What should they do to help everyone reach a conclusion?
- wholehog 2y ago
  Pre-training is just training on multiple chips. "If Cheng et al. had reached out to the corresponding authors of the Nature paper, we would have gladly helped them to correct these issues prior to publication" (https://arxiv.org/pdf/2411.10053 https://arxiv.org/pdf/2411.10053) That's how you actually do a reproduction study - you reach out to the corresponding authors and make sure you do everything exactly the same. But at this point, it's hard to imagine the AlphaChip folks having much patience with them.

wholehog 2y ago

> published a crappy article in Nature because it would never have passed editorial muster at something like DAC or an IEEE journal and now have to browbeat other people who are calling them out on it. I don't think it's easier to get into DAC / an IEEE journal than Nature. Their human baseline was the TPU physical design team, with access to the best available tools: rdcu.be/cmedX and this is still the baseline to beat in order to get used in production, which has happened for multiple generations of TPU. TPU is export controlled and super confidential -- multi-billion dollar IP! -- so I don't see raw data coming out anytime soon.

dogleg77 2y ago
Nature papers get retracted every year. I have not heard of DAC papers retracted. If the Nature paper made it clear that RL is not seriously expected to work on non-TPU chips, it would have have probably been rejected. If RL works on many other chips, then evidence should be easy to publish.