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Jeff Dean responds to EDA industry about AlphaChip

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Re: Jeff Dean responds to EDA industry about AlphaChip

#111
post #39
post #17

Earlier quoted context omitted.

> Google continues to peddle unsubstantiated snake oil I read your comment, but I'm not following -- or maybe I disagree with it -- I'm not sure yet. "Snake oil" is an emotionally loaded term that raises the temperature of the conversation. That usually makes having a conversation harder. From my point of view, AlphaGo, AlphaZero, AlphaFold were significant achievements. Agree? Are you claiming that AlphaChip is not?…

> From my point of view, AlphaGo, AlphaZero, AlphaFold were significant achievements. These things you mentioned had obvious benchmarks that were easily surpassed by the appropriate "AI". The evidence that they were better wasn't just significant, it was obvious . This leaves the fact that with what appears to be maximal cooking of the books, the only thing AlphaChip seems to be able to beat is human, manual placemen…

> "scientific publication"

These air quotes suggests the commenter above doesn't think the paper qualifies a scientific publication. Such a characterization is unfair.

When I read the Nature article titled "Addendum: A graph placement methodology for fast chip design" [1], I see writing that more than meets the bar for a scientific publication. For example:

> Since publication, we have open-sourced a software repository [21] to fully reproduce the methods described in our paper. External researchers can use this repository to pre-train on a variety of chip blocks and then apply the pre-trained model to new blocks, as was done in our original paper. As part of this addendum, we are also releasing a model checkpoint pre-trained on 20 TPU blocks [22]. For best results, however, we continue to recommend that developers pre-train on their own in-distribution blocks [18], and provide a tutorial on how to perform pre-training with our open-source repository [23].

[1]: https://www.nature.com/articles/s41586-024-08032-5

[18]: Yue, S. et al. Scalability and generalization of circuit training for chip floorplanning. In Proc. 2022 International Symposium on Physical Design 65–70 (2022).

[21]: Guadarrama, S. et al. Circuit Training: an open-source framework for generating chip floor plans with distributed deep reinforcement learning. GitHub https://github.com/google-research/circuit_training (2021).

[23]: Guadarrama, S. et al. Pre-training. GitHub https://github.com/google-research/circuit_training/blob/mai... (2021).

Re: Jeff Dean responds to EDA industry about AlphaChip

#112

The biggest disappointment is that these discussions are still happening on Twitter/X. Leave that platform already

Sure, we want individuals to act in a way to mitigate collective action problems. But the collective action problem exists (by definition) because individuals are trapped in some variation of a prisoner's dilemma.

So, collective action problems are nearly a statistical certainty across a wide variety of situations. And yet we still "blame" individuals? We should know better.

Re: Jeff Dean responds to EDA industry about AlphaChip

#113

Earlier quoted context omitted.

>peer reviewed research published in journals Peer review doesn't mean as much as Elsevier would like you to believe. Plenty of peer-reviewed research is absolute trash.

All of the highest impact papers authored by DeepMind and Google Brain have appeared in Nature, which is the gold standard for peer-reviewed natural science research. What exactly are you trying to claim about Google's peer-reviewed papers?

From personal experience: in Nature Communications the handling editor and editor in chief absolutely do intervene, in my example to suppress a proper lit review that would have revealed the paper under review as much less innovative than claimed.

Re: Jeff Dean responds to EDA industry about AlphaChip

#114

Curious why there's so much emotion and unpleasantness in this dispute? How did it evolve from the boring academic argument about benchmarks, significance, etc to a battle of personal attacks?

The issue is that Big Tech commercial incentives around AI have polluted the “boring academic” waters with dishonest infomercials masquerading as journal articles or arXiv preprints[1], and as a direct result contemporary AI research has a much worse “replication crisis” than the social sciences, yet with far fewer legitimate excuses.

Assuming Google isn’t lying, a lot of controversy would go away if they actually released their benchmark data for independent people to look at. They are still refusing to do so: https://cacm.acm.org/news/updates-spark-uproar/ Google thinks we should simply accept their conclusions by fiat. And don’t forget about this:

  Madden further pointed out that the “30 to 35%” advantage of RePlAce was consistent with findings reported in a leaked paper by internal Google whistleblower Satrajit Chatterjee, an engineer who Google fired in 2022 when he first tried to publish the paper that discredited the “superhuman” claims Google was making at the time for its AI approach to chip design.
It is entirely appropriate to make “personal attacks” against Jeff Dean, because the heart of the criticism is that his personality is dishonest and authoritarian: he publishes suspicious research and fires people who dissent.

[1] Jeff Dean hypocritically sneering about the critique being a conference paper is especially galling. What an unbelievable asshole.

Re: Jeff Dean responds to EDA industry about AlphaChip

#115
post #112

The biggest disappointment is that these discussions are still happening on Twitter/X. Leave that platform already

Sure, we want individuals to act in a way to mitigate collective action problems. But the collective action problem exists (by definition) because individuals are trapped in some variation of a prisoner's dilemma. So, collective action problems are nearly a statistical certainty across a wide variety of situations. And yet we still "blame" individuals? We should know better.

So you're saying Head of AI of Google of Jeff can't choose a better venue?

He's not the first Jeffery with a lot of power who doesn't care.

Re: Jeff Dean responds to EDA industry about AlphaChip

#116
post #115
post #112

Earlier quoted context omitted.

Sure, we want individuals to act in a way to mitigate collective action problems. But the collective action problem exists (by definition) because individuals are trapped in some variation of a prisoner's dilemma. So, collective action problems are nearly a statistical certainty across a wide variety of situations. And yet we still "blame" individuals? We should know better.

So you're saying Head of AI of Google of Jeff can't choose a better venue? He's not the first Jeffery with a lot of power who doesn't care.

> So you're saying Head of AI of Google of Jeff can't choose a better venue?

Phrasing it this way isn't useful. Talking about choice in the abstract doesn't help with a game-theoretic analysis. You need costs and benefits too.

There are many people who face something like a prisoner's dilemma (on Twitter, for example). We could assess the cost-benefit of a particular person leaving Twitter. We could even judge them according to some standards (ethical, rational, and so on). But why bother?...

...Think about major collective action failures. How often are they the result of just one person's decisions? How does "blaming" or "judging" an individual help make a situation better? This effort on blaming could be better spent elsewhere; such as understanding the system and finding leverage points.

There are cases where blaming/guilt can help, but only in the prospective sense: if a person knows they will be blamed and face consequences for an action, it will make that action more costly. This might be enough to deter than decision. But do you think this applies in the context of the "do I leave Twitter?" decision? I'd say very little, if at all.

Re: Jeff Dean responds to EDA industry about AlphaChip

#118
post #54

Earlier quoted context omitted.

Not true, H100s cost $2-3/GPU/hr on the open market.

Yes, they even do at $1/GPU/hr. However, 8xH100 cluster at full utilization is ~8kWh of electricity and costs almost ~0.5M$. 16xH100 cluster is probably 2x of that. How many years before you break-even at ~24$/GPU/day income?

Who cares? That's someone else's problem. I just pay 2-3$/hr and the H100s are usable

Re: Jeff Dean responds to EDA industry about AlphaChip

#119
Related. Others?

AI Alone Isn't Ready for Chip Design - https://news.ycombinator.com/item?id=42207373 - Nov 2024 (2 comments)

That Chip Has Sailed: Critique of Unfounded Skepticism Around AI for Chip Design - https://news.ycombinator.com/item?id=42172967 - Nov 2024 (9 comments)

Reevaluating Google's Reinforcement Learning for IC Macro Placement (AlphaChip) - https://news.ycombinator.com/item?id=42042046 - Nov 2024 (1 comment)

How AlphaChip transformed computer chip design - https://news.ycombinator.com/item?id=41672110 - Sept 2024 (194 comments)

Tension Inside Google over a Fired AI Researcher’s Conduct - https://news.ycombinator.com/item?id=31576301 - May 2022 (23 comments)

Google is using AI to design chips that will accelerate AI - https://news.ycombinator.com/item?id=22717983 - March 2020 (1 comment)

Re: Jeff Dean responds to EDA industry about AlphaChip

#120
post #37

Earlier quoted context omitted.

Their material discovery paper turned out to have negligible significance.

If so, does this qualify as “snake oil”? What do you mean? Snake oil requires exaggeration and deception. Fair? If a paper / experiment is done with intellectual honesty, great! If it doesn’t make a big splash, fine.

The paper is more or less a dead end. If there is another name you want to call it, by all means.
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