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On the dangers of stochastic parrots: Can language models be too big? (2021)

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Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#91

Post by LarryMullins »

wizzwizz4 wrote: post #81
LarryMullins wrote: post #60

Earlier quoted context omitted.

Why would that be bad? A submarine salesman convincing you that his submarine "swims" doesn't change the set of missions a submarine might be suitable for. It makes no practical difference. There's no point where you get the submarine and it meets all the advertised specs, does everything you needed a submarine for, but you're unsatisfied with it anyway because you now realize that the word "swim" is reserved for liv…

> but you're unsatisfied with it anyway because you now realize that the word "swim" is reserved for living creatures. There are swimming robots.[0][1] Swimming is qualitatively different to what submarines do. The exception is helical flagella, not robots. [0]: https://robot.cfp.co.ir/en/robots/swimming [1]: https://www.robotswim.com/?lang=English

> The exception is helical flagella, not robots.

Don't you think they're an exemption because they're alive? If seals had propellers we'd still say they swim. Squids use jet propulsion and we still say they swim; do jetskis also swim? Somehow not.

Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#92

Post by YeGoblynQueenne »

tlringer wrote: post #67
freyr wrote: post #20

Earlier quoted context omitted.

I suspect it was Timnit’s behavior after the paper didn’t pass internal review that actually got her fired (issuing an ultimatum and threatening to resign unless the company met her demands; telling her coworkers to stop writing documents because their work didn’t matter; insinuations of racist/misogynistic treatment from leadership when she didn’t get her way).

It was Megan Kacholia, who had put Timnit Gebru and others close to her down for a long time constantly within Google, always talking down and being condescending and rude, failing to respect Timnit in how she confronted Timnit about the paper (which she was ordered to retract by way of not Google's normal paper review process, but by a then-newly-implemented and since retracted secondary "sensitive topics review" pr…

Cheers. That's way much more information than I ever wanted to know about that sorry affair. If it can quell the torrent of ad-hominems, it's worth it, but I doubt it. All those hardcore soft. engineers here on HN who spend 99.99999% of uptime close to the bare metal think that people like Gebru who work on ethics are useless hangers-on without any "real contributions" (probably because none of them has bothered to check her background on wikipedia).

Nevertheless, hoping to check your sources I clicked through your profile and I have a question, totally unrelated to all this. Can you say something about the state of the art in "neural proof synthesis"? To clarify, I'm scare-quoting because I didn't even know that's a thing. For context, my background is in the European tradition of Resolution-based automated theorem proving (Prolog and all that) but also statistical machine learning, so don't worry about simplifying terminology too much.

Btw, the "proof engineering" link in your profile gives me a security alert on firefox.

Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#93

Post by tlringer »

YeGoblynQueenne wrote: post #92
tlringer wrote: post #67

Earlier quoted context omitted.

It was Megan Kacholia, who had put Timnit Gebru and others close to her down for a long time constantly within Google, always talking down and being condescending and rude, failing to respect Timnit in how she confronted Timnit about the paper (which she was ordered to retract by way of not Google's normal paper review process, but by a then-newly-implemented and since retracted secondary "sensitive topics review" pr…

Cheers. That's way much more information than I ever wanted to know about that sorry affair. If it can quell the torrent of ad-hominems, it's worth it, but I doubt it. All those hardcore soft. engineers here on HN who spend 99.99999% of uptime close to the bare metal think that people like Gebru who work on ethics are useless hangers-on without any "real contributions" (probably because none of them has bothered to c…

ML folks often call it "neural theorem proving." SOTA results are still from combinations of tactic prediction models with specialized tree search processes. They do OK on some interesting benchmarks, but still can handle mostly only fairly simple proofs. So far, they seem strictly complementary to symbolic methods. Interest is growing dramatically, though, and progress is accelerating, so I'm excited about the near future.

Language models are showing a lot of promise for autoformalization: automatically converting natural language mathematics to formal definitions, specifications, and proofs. This is a task where symbolic methods do not seem particularly promising in general, and one that meshes nicely with synthesis.

A good conference to look at is AI for Theorem Proving (AITP). It's small but has a lot of relevant work. All of the talks from this past year are recorded and on the website. MATH-AI at NeurIPS had some good work this year, too.

There is a bit of a culture and citation gap dividing the work in the AI community from the work in the PL/SE communities; in PL/SE I'd recommend work by Emily First and Alex Sanchez-Stern. They are undercited in AI work despite having SOTA results on meaningful Coq benchmarks. In AI, I'm particularly psyched about work by Yuhuai (Tony) Wu, Markus Rabe, Christian Szegedy, Sean Welleck, Albert Jiang, and many others. Tony's papers are a good gateway into other AI papers since the AI papers tend to cite each other.

Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#94

Post by YeGoblynQueenne »

tlringer wrote: post #93
YeGoblynQueenne wrote: post #92

Earlier quoted context omitted.

Cheers. That's way much more information than I ever wanted to know about that sorry affair. If it can quell the torrent of ad-hominems, it's worth it, but I doubt it. All those hardcore soft. engineers here on HN who spend 99.99999% of uptime close to the bare metal think that people like Gebru who work on ethics are useless hangers-on without any "real contributions" (probably because none of them has bothered to c…

ML folks often call it "neural theorem proving." SOTA results are still from combinations of tactic prediction models with specialized tree search processes. They do OK on some interesting benchmarks, but still can handle mostly only fairly simple proofs. So far, they seem strictly complementary to symbolic methods. Interest is growing dramatically, though, and progress is accelerating, so I'm excited about the near…

Thanks. I'm a bit more familiar with neural theorem proving. It's an interesting area. For example, if I could train me a model to speed up (NP-complete) θ-subsumption for very long terms that would be a worthy addition to the purely symbolic toolbox I'm more at home with.

Autoformalization also sounds interesting. I've had some conversations about automatically turning big corpora of natural language text into Prolog with language models, for example. I don't reckon anyone is even researching how to do this with symbolic methods at the moment.

I'll check out AITP. Thanks for the pointers. I'm used to small conferences [and to underciting between disciplines] :)

Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#95

Post by [deleted] »

tlringer wrote: post #88
srvmshr wrote: post #87

Earlier quoted context omitted.

I have the same username as on Twitter. I admire your work and your journey. Thanks

Got it, thank you. On why Timnit is criticized so intensely everywhere anonymity is possible, I honestly think that is more an artifact of sexism and racism than an indictment of anything she has done. Also a matter of the target audience of a lot of the anonymous forums. FWIW, at Google in Research, many people have mentioned her to me as the only person they trusted to talk to about the things they went through whe…

[deleted]

Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#96

Post by tlringer »

YeGoblynQueenne wrote: post #94
tlringer wrote: post #93

Earlier quoted context omitted.

ML folks often call it "neural theorem proving." SOTA results are still from combinations of tactic prediction models with specialized tree search processes. They do OK on some interesting benchmarks, but still can handle mostly only fairly simple proofs. So far, they seem strictly complementary to symbolic methods. Interest is growing dramatically, though, and progress is accelerating, so I'm excited about the near…

Thanks. I'm a bit more familiar with neural theorem proving. It's an interesting area. For example, if I could train me a model to speed up (NP-complete) θ-subsumption for very long terms that would be a worthy addition to the purely symbolic toolbox I'm more at home with. Autoformalization also sounds interesting. I've had some conversations about automatically turning big corpora of natural language text into Prolo…

I went to AITP for the first time last September, and I found it an absolute pleasure. Everyone was kind and wonderful and open-minded. The venue was wonderful too. Highly recommended if you're interested at any point.

Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#97

Post by YeGoblynQueenne »

tlringer wrote: post #96
YeGoblynQueenne wrote: post #94

Earlier quoted context omitted.

Thanks. I'm a bit more familiar with neural theorem proving. It's an interesting area. For example, if I could train me a model to speed up (NP-complete) θ-subsumption for very long terms that would be a worthy addition to the purely symbolic toolbox I'm more at home with. Autoformalization also sounds interesting. I've had some conversations about automatically turning big corpora of natural language text into Prolo…

I went to AITP for the first time last September, and I found it an absolute pleasure. Everyone was kind and wonderful and open-minded. The venue was wonderful too. Highly recommended if you're interested at any point.

And the next one is easy to get to by train from my current location. That's great (I don't fly). Nice.

Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#98

Post by tlringer »

YeGoblynQueenne wrote: post #97
tlringer wrote: post #96

Earlier quoted context omitted.

I went to AITP for the first time last September, and I found it an absolute pleasure. Everyone was kind and wonderful and open-minded. The venue was wonderful too. Highly recommended if you're interested at any point.

And the next one is easy to get to by train from my current location. That's great (I don't fly). Nice.

Hopefully I'll see you there!

BTW what you say about the sadly common assumption Timnit and other AI ethics folks don't have "real contributions" is too real. It has impacted me even though my work isn't on AI ethics at all, just because I bother to talk about it online in public sometimes. Similarly for any social justice work or any work improving the work environment in research. It is like some people cannot comprehend that one can be technically proficient and still care about social justice and ethics and other "soft" issues. I love how confused those people are when they learn my expertise is in formal logic and proof haha

Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#99

Post by [deleted] »

tlringer wrote: post #67
freyr wrote: post #20

Earlier quoted context omitted.

I suspect it was Timnit’s behavior after the paper didn’t pass internal review that actually got her fired (issuing an ultimatum and threatening to resign unless the company met her demands; telling her coworkers to stop writing documents because their work didn’t matter; insinuations of racist/misogynistic treatment from leadership when she didn’t get her way).

It was Megan Kacholia, who had put Timnit Gebru and others close to her down for a long time constantly within Google, always talking down and being condescending and rude, failing to respect Timnit in how she confronted Timnit about the paper (which she was ordered to retract by way of not Google's normal paper review process, but by a then-newly-implemented and since retracted secondary "sensitive topics review" pr…

[deleted]

Re: On the dangers of stochastic parrots: Can language models be too big? (2021)

#100

Post by hardwaregeek »

hn_throwaway_99 wrote: post #73
hardwaregeek wrote: post #50

I'm still midway through the paper, but I gotta say, I'm a little surprised at the contrast between the contents of the paper and how people have described it on HN. I don't agree with everything that is said, but there are some interesting points made about the data used to train the models, such as it capturing bias (I would certainly question the methodology of using reddit as a large source of training data), and…

> I'm a little surprised at the contrast between the contents of the paper and how people have described it on HN. I haven't read the paper beyond the abstract yet, so can't comment on it's contents, but can you specify what you mean by "how people have described it on HN"? To be frank, I don't like this style of argument, because I literally don't know what you're referring to. I'm quite familiar with the hubbub tha…

Apologies, I assumed it would be clear from the other comments. When I posted this the top comment was a very visibly negative take on the paper. Similarly, Google must have considered this paper to be sufficiently outside the realm of valid academic discourse because their actions around the release of this paper lead to the termination of a couple authors and a PR snafu.
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