Google fires another AI researcher who reportedly challenged findings • Hacker News Board

Re: Google fires another AI researcher who reportedly challenged findings

#191

Post by faeriechangling » Mon, May 02, 2022, 10:03 PM UTC

Manuel_D wrote: post #137

dekhn wrote: post #128

Earlier quoted context omitted.

Yes, that's not a resignation. At google, to technically resign involves filling out a form on a website, it's not something you can verbally state to your (manager's) manager. Again, Google can still say this and probably even thinks internally that's what happened, but having resigned twice from Google, that's not how it really works.

I'm pretty sure if you say to your manager "I quit" and stop showing up to work, regardless of what internet forms you fill out, it's a resignation. The fact that Gebru gave her resignation in the form of an ultimatum rather than whatever website form you're talking about is a pedantic point. "I resign if you don't do X." "We're not doing X, we accept your resignation." This is a resignation the way I understand it.

That’s not a resignation the way California understands it. If she said “I am resigning NOW unless you do X” that WOULD count as a resignation.

Let’s think of a hypothetical. A student says “I’m going to quit eventually in order to attend classes” and the manager says “I accept your resignation, you are fired”. This generally seems more like a firing because somebody was going to resign, more than it seems like a resignation no?

In the Gebru case, Gebru didn’t express WHEN she was going to resign, and that’s google’s issue. Maybe Gebru was, for all Google knew, threatening to quit in a month or a year or in 10 years. Gebru never actually made the time of her proposed resignation clear. As such, the law doesn’t consider to actually have made a legally binding resignation.

Re: Google fires another AI researcher who reportedly challenged findings

#192

Post by rg111 » Mon, May 02, 2022, 10:08 PM UTC

godelski wrote: post #140

hintymad wrote: post #105

Earlier quoted context omitted.

And it's strange no one in media questioned Gebru's paper. For instance, her calculation of carbon footprint of training BERT is ridiculous, as she assumes that companies will train BERT 24x7. I think Gebru is a disgrace to the community because she always, I mean literally always, attacks her critics by motives. You think bias is a data problem? You're a bigot (See her dispute with LeCun). You disagree with my asses…

Are we talking about the LeCun fight where they were discussing Pulse (the white Obama super-resolution paper)? Didn't Pulse update their paper and show that it was in fact a dataset bias? A lot of these datasets have bias and it is frequently hard to train an unbiased model on a biased dataset. It also isn't the goal for many, though of course there is work on this. Bias is everywhere in a ML system, from the datase…

I saw an actual Tweet by Gebru that said some loss functions are more racist than others. (I am not making this up.)

Yes, she literally said that.

I read some of her other work, and found little to no substance. She is mediocre at best.

I would personally say that she is not a real AI researcher. (Her prominence, as lent by her CV, is a result of a gamed system).

Re: Google fires another AI researcher who reportedly challenged findings

#193

Post by gwern » Mon, May 02, 2022, 10:24 PM UTC

joshuamorton wrote: post #141

gwern wrote: post #132

Earlier quoted context omitted.

> There was an attempt by the authors to respond to the internal criticism, but this wasn't met constructively. Really? Because people claiming to be the reviewers at the time said that the version they reviewed was a lot worse than the final version published much later. (And that version wasn't too great either.)

That's because the paper received additional external peer review as part of the normal academic publishing process at FAccT. The internal special topics review process that wasn't formalized at the time but was pseudo-formalized later in response to this issue also supposedly allowed revision, but in practice this authors weren't given the opportunity to respond to the feedback, they were only give the option to rem…

Well OK then, but your point was that "the original version must have been acceptable because Googlers later cited the final version, gotcha!", and the more review and improvement it undergoes by whomever, the less sense your point makes.

Re: Google fires another AI researcher who reportedly challenged findings

#194

Post by nostrademons » Mon, May 02, 2022, 10:26 PM UTC

godelski wrote: post #140

hintymad wrote: post #105

Earlier quoted context omitted.

And it's strange no one in media questioned Gebru's paper. For instance, her calculation of carbon footprint of training BERT is ridiculous, as she assumes that companies will train BERT 24x7. I think Gebru is a disgrace to the community because she always, I mean literally always, attacks her critics by motives. You think bias is a data problem? You're a bigot (See her dispute with LeCun). You disagree with my asses…

Are we talking about the LeCun fight where they were discussing Pulse (the white Obama super-resolution paper)? Didn't Pulse update their paper and show that it was in fact a dataset bias? A lot of these datasets have bias and it is frequently hard to train an unbiased model on a biased dataset. It also isn't the goal for many, though of course there is work on this. Bias is everywhere in a ML system, from the datase…

> it was our human bias of knowing Obama is black

As a side note - Obama is multiracial (white mother). As a multiracial person who makes no effort to fit into American racial categories and whose general attitude toward them is "Your social construction of race is fucked up, man", it astounds me that people would expect an AI to see what's not actually there. Biologically, Barack Obama is half-black and half-white, and probably a whole lot more complicated than that because "black" and "white" are themselves leaky abstractions that we've imposed on the immense variability of human skin tones. Why wouldn't you expect him to autocomplete to a white man 50% of the time and a black man 50% of the time?

Re: Google fires another AI researcher who reportedly challenged findings

#195

Post by sangnoir » Mon, May 02, 2022, 10:28 PM UTC

Manuel_D wrote: post #170

godelski wrote: post #140

Earlier quoted context omitted.

Are we talking about the LeCun fight where they were discussing Pulse (the white Obama super-resolution paper)? Didn't Pulse update their paper and show that it was in fact a dataset bias? A lot of these datasets have bias and it is frequently hard to train an unbiased model on a biased dataset. It also isn't the goal for many, though of course there is work on this. Bias is everywhere in a ML system, from the datase…

The "white Obama" pictures didn't even seem biased. They took a brightly lit photo of Obama and the output face has the same skin tone. Compare Obama's skin tone in this photo: https://video-images.vice.com/test-uploads/articles/5ef216e9... :* With other photos. E.g: https://www.obama.org/wp-content/uploads/ar_obama_sq.jpg

You can't can't use a use a dropper and compare color hex/HSL values on different pictures with different lighting and say "Yup - that's the same", otherwise SFX wouldn't need color grading[1]. The second picture seems to be from a very brightly-lit environment - to the extent of washing out his blue shirt (collar), but the first picture gives no such cues, and appears less well-lit (virtually).

AI models have no implicit knowledge of environmental lighting, and if the training data is exceedingly made of brightly lit images, may end up generating images that belong in well-lit environments (fairer-looking skin tones), but in low-light contexts. When looking at these generated images, the human eye does what it does best: determine relevant gamma-correction using all available cues.

[1]. Assets rendered in the same color under all scene lighting conditions.

Re: Google fires another AI researcher who reportedly challenged findings

#196

Post by godelski » Mon, May 02, 2022, 10:29 PM UTC

rg111 wrote: post #192

godelski wrote: post #140

Earlier quoted context omitted.

Are we talking about the LeCun fight where they were discussing Pulse (the white Obama super-resolution paper)? Didn't Pulse update their paper and show that it was in fact a dataset bias? A lot of these datasets have bias and it is frequently hard to train an unbiased model on a biased dataset. It also isn't the goal for many, though of course there is work on this. Bias is everywhere in a ML system, from the datase…

I saw an actual Tweet by Gebru that said some loss functions are more racist than others. (I am not making this up.) Yes, she literally said that. I read some of her other work, and found little to no substance. She is mediocre at best. I would personally say that she is not a real AI researcher. (Her prominence, as lent by her CV, is a result of a gamed system).

> I saw an actual Tweet by Gebru that said some loss functions are more racist than others. (I am not making this up.)

Do you have this source? I'm curious what she said. Is this L1 vs L2 but limiting to race?

Re: Google fires another AI researcher who reportedly challenged findings

#197

Post by godelski » Mon, May 02, 2022, 10:31 PM UTC

nostrademons wrote: post #194

godelski wrote: post #140

Earlier quoted context omitted.

Are we talking about the LeCun fight where they were discussing Pulse (the white Obama super-resolution paper)? Didn't Pulse update their paper and show that it was in fact a dataset bias? A lot of these datasets have bias and it is frequently hard to train an unbiased model on a biased dataset. It also isn't the goal for many, though of course there is work on this. Bias is everywhere in a ML system, from the datase…

> it was our human bias of knowing Obama is black As a side note - Obama is multiracial (white mother). As a multiracial person who makes no effort to fit into American racial categories and whose general attitude toward them is "Your social construction of race is fucked up, man", it astounds me that people would expect an AI to see what's not actually there . Biologically, Barack Obama is half-black and half-white,…

Well the goal is that the reconstruction would be "him". Obviously the reconstruction isn't Obama. Limiting the discussion to race isn't a productive means to resolve the errors. There are more than race and they are likely coupled.

Re: Google fires another AI researcher who reportedly challenged findings

#198

Post by [deleted] » Mon, May 02, 2022, 10:48 PM UTC

yablak wrote: post #118

The TF-Agents team replicated the RL training (with the corresponding teams' very deep collaboration) and open-sourced it here: https://github.com/google-research/circuit_training It pretty much gets the same results as found in the Nature paper. The original codebase was heavily research-focused, used TF1, was impossible to run distributed training outside of Google's infra, and made it hard to try algorithms other…

[deleted]

Re: Google fires another AI researcher who reportedly challenged findings

#199

Post by Throw4w5y » Mon, May 02, 2022, 10:59 PM UTC

yablak wrote: post #118

The TF-Agents team replicated the RL training (with the corresponding teams' very deep collaboration) and open-sourced it here: https://github.com/google-research/circuit_training It pretty much gets the same results as found in the Nature paper. The original codebase was heavily research-focused, used TF1, was impossible to run distributed training outside of Google's infra, and made it hard to try algorithms other…

> the RL training

Is RL necessary in this context or would other, simpler methods work as well to within statistical error? RL can be a heck of a random number generator if misapplied.

Re: Google fires another AI researcher who reportedly challenged findings

#200

Post by SpicyLemonZest » Mon, May 02, 2022, 11:08 PM UTC

brian_cloutier wrote: post #184

SpicyLemonZest wrote: post #144

Earlier quoted context omitted.

The core thing I (and I suspect the original commenter) struggle to get past is the invetiable twist that in this post happens halfway through this post's part II. > If it’s a very smart mesa-optimizer, it might think “If I throw the strawberry at the streetlight, I will be caught and trained to have different goals." It seems to me that this is a category error, like having the very smart mesa-optimizer start thinki…

I don't think I understand your objection because it doesn't seem like a category error to talk about optimizers having goals. I think you would agree that thermostats have goals? They try to minimize the error between the desired and the actual temperature. And you would also agree that gradient descent has a goal? It tweaks parameters in the search for models which minimize error in the training set. The system per…

I would say that talking about a thermostat's goals is an even stronger example of anthropomorphism. Broadly goal-like behavior, sure, so I hesitate to flatly say they don't have goals. But if someone told me that we have to be careful about engineering better thermostats, because a sufficiently high quality thermostat wants to keep its current set temperature and won't let you change it, I don't think that'd make a ton of sense.

I don't want to sound unfair here, because I do agree that proper alignment of ML models is an important challenge. I can easily imagine an ML engagement algorithm that starts to get everyone hooked on pornography, or an ML drug discovery program where half the drugs have permanent side effects that only manifest after 10 years, and I don't think there's any guarantee that these problems will be obvious to find or easy to fix. What I don't follow is the scenario where the drug discovery program "wants" to show you bad drugs but shows you good ones instead because it thinks you'll eventually put it in charge of the FDA.