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The paper was submitted on time, other papers at Google were submitted for review after they were released. By Jeff Dean's own admission the paper was approved

The paper was submitted on time, other papers at Google were submitted for review after they were released. By Jeff Dean's own admission the paper was approved for release. The demand for retraction was initially given while refusing to state what the reason(s) for the demand were. Only after the meeting where they demanded she retract the paper did they belatedly agree to read her the feedback that they stated as the reason. They wouldn't even let her read it first hand. Ultimately it seems like Google could have plainly stated their issues with the paper from the start, and that said issues were not so serious that they merited a demand for retraction. There are a number of things that just don't add up. Even if you take it at face value that Gebru's ultimatum demands were unreasonable, there are several problems with how the situation got to that point in the first place.

uh_uh 6y ago
Excerpt from Jeff Dean's email: > Timnit co-authored a paper with four fellow Googlers as well as some external collaborators that needed to go through our review process (as is the case with all externally submitted papers). We’ve approved dozens of papers that Timnit and/or the other Googlers have authored and then published, but as you know, papers often require changes during the internal review process (or are even deemed unsuitable for submission). Unfortunately, this particular paper was only shared with a day’s notice before its deadline — we require two weeks for this sort of review — and then instead of awaiting reviewer feedback, it was approved for submission and submitted. > A cross functional team then reviewed the paper as part of our regular process and the authors were informed that it didn’t meet our bar for publication and were given feedback about why. It ignored too much relevant research — for example, it talked about the environmental impact of large models, but disregarded subsequent research showing much greater efficiencies. Similarly, it raised concerns about bias in language models, but didn’t take into account recent research to mitigate these issues. We acknowledge that the authors were extremely disappointed with the decision that Megan and I ultimately made, especially as they’d already submitted the paper. I'm curious: why is ignoring too much relevant research not a good reason to demand retraction? Isn't this one of the most common embarrassing mistakes that inexperienced researchers tend to make? I can fully understand if Google does not want to associate itself with work that might be viewed by the wider research community as shoddy.
- tomdell 6y ago
  That claim by Jeff Dean has been more or less debunked. The real reason that Google demanded the paper not be published is that it is critical of models underlying the company’s fundamental business - the whole message from Jeff Dean is very misleading and states a bunch of other, unrelated things as the “real reason”.
  - zaroth 6y ago
    It would be very helpful to point to sources for the claim that Jeff Dean's explanation is debunked, rather than just claiming it has been. The timing of the submission for review, and the deadline for submission to publication seems like a simple factual statement that should be easily demonstrated as true or false.
    - pseudalopex 6y ago
      Several Google employees said it wasn't the regular process.[1][2] [1] https://news.ycombinator.com/item?id=25309488 https://news.ycombinator.com/item?id=25309488 [2] https://news.ycombinator.com/item?id=25309379 https://news.ycombinator.com/item?id=25309379
  - uh_uh 6y ago
    Debunked by whom?
  - hnuser123456 6y ago
    AI is far more energy efficient than creating the incalcuable number of human programmers required to directly program the same solutions. Writing NLP code with non-ML methods, to the same level of quality, would be essentially impossible without an operation on the scale of warfare. Even with the efficiency benefits that it already has, we all know ML still stands to be further optimized, and a lot of people are working on it. Comparing the exponential increase of "intelligence" of computers versus our own, computers will be as smart as humans with similar energy efficiency in around 50 years, and they require no cars, food, etc. It will be hard to criticize the efficiency of ML when it is already moreso than our biological selves, but it will be done, and we will be wholly outsmarted.

uh_uh 6y ago

Excerpt from Jeff Dean's email: > Timnit co-authored a paper with four fellow Googlers as well as some external collaborators that needed to go through our review process (as is the case with all externally submitted papers). We’ve approved dozens of papers that Timnit and/or the other Googlers have authored and then published, but as you know, papers often require changes during the internal review process (or are even deemed unsuitable for submission). Unfortunately, this particular paper was only shared with a day’s notice before its deadline — we require two weeks for this sort of review — and then instead of awaiting reviewer feedback, it was approved for submission and submitted. > A cross functional team then reviewed the paper as part of our regular process and the authors were informed that it didn’t meet our bar for publication and were given feedback about why. It ignored too much relevant research — for example, it talked about the environmental impact of large models, but disregarded subsequent research showing much greater efficiencies. Similarly, it raised concerns about bias in language models, but didn’t take into account recent research to mitigate these issues. We acknowledge that the authors were extremely disappointed with the decision that Megan and I ultimately made, especially as they’d already submitted the paper. I'm curious: why is ignoring too much relevant research not a good reason to demand retraction? Isn't this one of the most common embarrassing mistakes that inexperienced researchers tend to make? I can fully understand if Google does not want to associate itself with work that might be viewed by the wider research community as shoddy.

tomdell 6y ago
That claim by Jeff Dean has been more or less debunked. The real reason that Google demanded the paper not be published is that it is critical of models underlying the company’s fundamental business - the whole message from Jeff Dean is very misleading and states a bunch of other, unrelated things as the “real reason”.
- zaroth 6y ago
  It would be very helpful to point to sources for the claim that Jeff Dean's explanation is debunked, rather than just claiming it has been. The timing of the submission for review, and the deadline for submission to publication seems like a simple factual statement that should be easily demonstrated as true or false.
  - pseudalopex 6y ago
    Several Google employees said it wasn't the regular process.[1][2] [1] https://news.ycombinator.com/item?id=25309488 https://news.ycombinator.com/item?id=25309488 [2] https://news.ycombinator.com/item?id=25309379 https://news.ycombinator.com/item?id=25309379
- uh_uh 6y ago
  Debunked by whom?
- hnuser123456 6y ago
  AI is far more energy efficient than creating the incalcuable number of human programmers required to directly program the same solutions. Writing NLP code with non-ML methods, to the same level of quality, would be essentially impossible without an operation on the scale of warfare. Even with the efficiency benefits that it already has, we all know ML still stands to be further optimized, and a lot of people are working on it. Comparing the exponential increase of "intelligence" of computers versus our own, computers will be as smart as humans with similar energy efficiency in around 50 years, and they require no cars, food, etc. It will be hard to criticize the efficiency of ML when it is already moreso than our biological selves, but it will be done, and we will be wholly outsmarted.