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Comment by causi | Hacker News Reader

This dispute has certainly had consequences, most of them negative.

There's an enormous amount of relevant details missing from this article and I don't know why. Why is there no response from Kahng to G&M's rebuttal of his experiments? What did G&M say when told their Github posting wasn't reproducible? Did they agree it was a problem or not? Why did G&M leave Google if the person who was personally undermining them was fired? What exactly did Chatterjee do to personally undermine G&M, and why?

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Also why emphasis that G&M are women? This sounds like creating an unnecessary protectional bias for them.

I was immediately thrown into the weeds on that, too.

ieee.org's editors are not doing a service to their members by making a chip design argument about AI a gender issue.

In 2023 all issues are gender issues.

The tangents people, even here at HN, take viz-a-viz these topics, are often mind-boggling. And HN generally has less bot protection than places like reddit, too.

> why

Some background is EE professional and academic culture has, for more than a half century been ... less than fine with having women colleagues. Lots of ugly. Decades of improvement too, reflecting a whole lot of years of work from lots of people and leadership. But... CS changed faster.

So an IEEE pub is pointing out that an IEEE-affiliated conference reportedly had... an excursion from professionalism baseline.

The only reasonable explanation I can think of is that Chatterjee's motives and subsequent termination (which is not elaborated on) was related to gender discrimination. Otherwise I can't see what relevance anyone's genders has on the issue at hand.

It's explained in the Wired article that is linked that this was indeed the cause.

The independent reproduction that the IEEE article talks about also tested the method from the leaked paper from Chatterjee and concluded that his method indeed beats CT in almost all cases at a fraction of the computational cost.

The response from G&M to the independent reproduction emphasizes their method was used for production hardware at Google, was in a peer reviewed paper, and that their GitHub repo had a lot of forks and stars (I'm not making this up). Near the end it then raises several technical objections.

It doesn't look like they didn't train CT to convergence, so idk why anyone would care about those results.

What is even the point of benchmarking against a learning based method of you don't let it finish learning? Quality of output matters way more than compute cost.

EDIT (parity error): "didn't train" -> "trained"

That's how you get attention in modern times. Marginalized groups are inherently heroic and non-marginalized groups are inherently villainous.

This seems like the relevant context?

https://www.wired.com/story/google-brain-ai-researcher-fired...

Why is there no response from Kahng to G&M's rebuttal of his experiments?

The rebuttal was published less than 2 weeks ago, so that seems a bit early, and it is public, so reading it would answer several of your questions.

https://www.annagoldie.com/home/statement

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