191–200 of 221 posts
Earlier quoted context omitted.
That's an appeal to authority, and not an effective one. Jeff Dean doesn't have a good track record in chip design.
What are you even talking about? Jeff had a hand in TPU, which is so successful that all other AI companies are trying to clone this project and spin up their own efforts to make custom AI chips.
Earlier quoted context omitted.
What did the TPU physical design team say about this publicly? Can you also point to a statement from MediaTek? (I've seen a quote in Google blog, but was unable to confirm it). Who in the TF-agents team has serious physical design background?
Are you really suggesting that the TPU team does not stand behind the graphs in Google's own blog post? And that MediaTek does not stand behind their quoted statement?
Someone mentioned here before that Google folks have been using "hyperbolae". So, if MediaTek can clarify how they are using AlphaChip, everyone wins.
In the tweet Jeff Dean says that Cheng at al. failed to follow the steps required to replicate the work of the Google researchers. Specifically: > In particular the authors did no pre-training (despite pre-training being mentioned 37 times in our Nature article), robbing our learning-based method of its ability to learn from other chip designs But in the Circuit Training Google repo[1] they specifically say: > Our re…
> Did they mess up when they did not pre-train or they followed the "steps" described in the original repo and tried to get a fair reproduction? The Circuit Training repo was just going through an example. It is common for an open-source repo to describe simple examples for testing / validating your setup --- that does not mean this is how you should get optimal results in general. The confusion may stem from their s…
Earlier quoted context omitted.
I love how he’s claiming bias due to his critic’s employer. As though working for Google has no conflicts? A company that is desperately hyping each and every “me too” AI development to juice the stock price? Jeff drank so much kool aid he forget what water is.
He's criticizing Markov for not disclosing the conflict, not for the conflict itself. Hiding your affiliation in a scientific publication is far outside the norms of science, and they should be criticized for that. The publication we are discussing — "That Chip Has Sailed" — dismisses Markov in a few paragraph and spends the bulk its arguments on Cheng.
I’ve not followed this story at all, and have no idea what is true or not, but generally when people use a boatload of adjectives which serve no purpose but to skew opinion, I assume they are not being honest. Using certain words to describe a situation does not make the situation what the author is saying, and if it is as they say, then the actual content should speak for itself. For instance: > Much of this unfound…
Earlier quoted context omitted.
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.
I think the paper was probably done honestly, but also very poorly. They claimed synthesis of 36 new materials. When reviewed, for 24/36 "the predicted structure has ordered cations but there is no evidence for order, and a known, disordered version of the compound exists". In fact, with other errors, 36/36 claims were doubtful. This reflects badly for authors and worse for peer review process of Nature. https://x.co…
Every scientist will tell you that "peer reviewed" is not a mark of quality, correctness, impact, value, accuracy, whatever.
Scientists care about replication. More correctly, they care that your work can be built upon. THAT is evidence of good science.
Earlier quoted context omitted.
> it might have a harder time with a chip designed by a third party (further from its pre-training). Then they could pre-train on chips that are in-distribution for that task. See also section 3.1 of their response paper, where they describe a comparison against commercial autoplacers: https://arxiv.org/pdf/2411.10053
The comparison in that paper was very much not fair to Google's method. Google's original published comparison to simulated annealing is not fair to simulated annealing methods. That is, unfortunately, part of the game of publication when you want to publish a marginal result. It is possible that the pre-training step may overfit to a particular class of chips or may fail to converge given a general sample of chip de…
Earlier quoted context omitted.
I understand and have read the article. Running 80 experiments with a crude form of simulated annealing is at most 0.0000000001% of the effort that has been spent on making that kind of hill climb work well by traditional EDA vendors. That is also an in-sample comparison, where I would believe the Google thing pre-trained on Google chips would do well, while it might have a harder time with a chip designed by a third…
> it might have a harder time with a chip designed by a third party (further from its pre-training). Then they could pre-train on chips that are in-distribution for that task. See also section 3.1 of their response paper, where they describe a comparison against commercial autoplacers: https://arxiv.org/pdf/2411.10053
Earlier quoted context omitted.
> Did they mess up when they did not pre-train or they followed the "steps" described in the original repo and tried to get a fair reproduction? The Circuit Training repo was just going through an example. It is common for an open-source repo to describe simple examples for testing / validating your setup --- that does not mean this is how you should get optimal results in general. The confusion may stem from their s…
You must be kidding. They did reach out and documented their interactions, with names of engineers, dates, and all. Someone is ghosting someone here.
[8] Prior to publication of Cheng et al., our last correspondence with any of its authors was in August of 2022 when we reached out to share our new contact information.
[9] In contrast, prior to publishing in Nature, we corresponded extensively with Andrew Kahng, senior author of Cheng et al. and of the prior state of the art (RePlAce), to ensure that we were using the appropriate settings for RePlAce."
Earlier quoted context omitted.
And do you believe that that is what's happening in this case? If you have personal experience with Jeff Dean et al that you're willing to share, I'd be interested in hearing about it. 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." It's not even clea…
When Google published the Nature article, Nature included a rosy intro article by a leading expert in chip design. His name was Andrew Kahng, and he apparently liked Google at the time. But when he dug into Google code (released way after publication), he retracted his intro and co-authored the Cheng et al article. You see how your theory breaks down here.
As I said, the truth will out. I was just unhappy with the case previously being offered.