5 ms·

I apologize in advance for the elitist-sounding tone. In my defense the people I’m calling elite I have nothing to do with, I’m certainly not talking about myse

I apologize in advance for the elitist-sounding tone. In my defense the people I’m calling elite I have nothing to do with, I’m certainly not talking about myself. Without a fairly deep grounding in this stuff it’s hard to appreciate how far ahead Brain and DM are. Neither OpenAI nor FAIR ever has the top score on anything unless Google delays publication. And short of FAIR? D2 lacrosse. There are exceptions to such a brash generalization, NVIDIA’s group comes to mind, but it’s a very good rule of thumb. Or your whole face the next time you are tempted to doze behind the wheel of a Tesla. There are two big reasons for this: - the talent wants to work with the other talent, and through a combination of foresight and deep pockets Google got that exponent on their side right around the time NVIDIA cards started breaking ImageNet. Winning the Hinton bidding war clinched it. - the current approach of “how many Falcon Heavy launches worth of TPU can I throw at the same basic masked attention with residual feedback and a cute Fourier coloring” inherently favors deep pockets, and obviously MSFT, sorry OpenAI has that, but deep pockets also non-linearly scale outcomes when you’ve got in-house hardware for multiply-mixed precision. Now clearly we’re nowhere close to Maxwell’s Demon on this stuff, and sooner or later some bright spark is going to break the logjam of needing 10-100MM in compute to squeeze a few points out of a language benchmark. But the incentives are weird here: who, exactly, does it serve for us plebs to be able to train these things from scratch?

meowface 4y ago
Not elitist at all; I highly appreciate this post. I know the basics of ML but otherwise am clueless when it comes to the true depths of this field and it's interesting to hear this perspective.
- benreesman 4y ago
  I used a lot of jargon and lingo and inside baseball in that post, it was intended for people who have deep background. But if you’re interested I’m happy to (attempt) answers to anything that was jargon: by virtue of HN my answers will be peer-reviewed in real time, and with only modest luck, a true expert might chime in.
  - blinding-streak 4y ago
    Is there a handy list of generally recognized AI advancements, and their owners, that you would recommend reviewing? Or perhaps, seminal papers published? I'm only tangentially familiar with the field but would be curious to learn about the clash of the Titans playing out. Thanks!
    - benreesman 4y ago
      That’s too big a question to even attempt an answer in an HN comment, but to try to answer a realistic subset of it: “Attention is All You Need” in like 2017 is the paper most germane to my remark, and probably the thread. The modeling style it introduced often gets called a “transformer”. The TLDR is that people had been trying for ages to capture long-distance (in the input or output, not the black box) relationships in a way that was amenable to traditional neural-network training techniques, which is non-obvious how to do because your basic NN takes an input without a distance metric, or put more plainly: it can know all the words in a sentence but struggles with what order they are in without some help. The state of the art for awhile was something called an LSTM, and those gadgets are still useful sometimes, but have mostly been obsoleted by this attention/transformer business. That paper had a number of cool things in it but two stand out: - by blinding an NN to some parts of the input (“masking”) you can incentivize/compel it to look at (“attend to”) others. That’s a gross oversimplification, but it gets the gist of it I think. People have come up with very clever ways to boost up this or that part of the input in a context-dependent way. - by playing with some trigonometry you can get a unique shape that came be expressed as a sun on something else that gives the model its “bearings” so to speak as to “where” it is in the input. such a word is closer to the beginning of a paragraph sort of a thing. people have also gotten very clever about how to do this, but the idea is the same: how do I tell a neural network that there’s structure in what would otherwise be a pile of numbers.
ttul 4y ago
In short, it’s all about money.
- benreesman 4y ago
  Yes and no. For example: the high-frequency trading industry is estimated to have made somewhere between 2-3 billion dollars in all of 2020, profit/earnings. That’s a good weekend at Google. HFT shops pay well, but not much different to top performers at FAANG. People work in HFT because without taking a pay cut they can play real ball: they want to try themselves against the best. Heavy learning people are no different in wanting both a competitive TC but maybe even more to be where the action is. That’s currently Blade Runner Industries Ltd, but that could change.
f38zf5vdt 4y ago
> But the incentives are weird here: who, exactly, does it serve for us plebs to be able to train these things from scratch? I'm not sure it matters. The history of computing shows that within the decade we will all have the ability to train and use these models.
- sinenomine 4y ago
  ... Unless the possession of capable models becomes a legal liability by that time.
  - astrange 4y ago
    This won’t happen in an interesting way. What will happen is you’ll find out training a model on copyrighted inputs causes it to memorize those inputs and the owners own your output.
dougabug 4y ago
This characterization is not really accurate. OpenAI has had almost a 2 year lead with GPT-3 dominating the discussion of LLMs (large language models). Google didn’t release its paper on the powerful PaLM-540b model until recently. Similarly, CLiP, Glide, DALL-E, and DALL-E2 have been incredibly influential in visual-language models. Imagen, while highly impressive, definitely is a catch-up piece of work (as was PaLM-540b). Google clearly demonstrates their unrivaled capability to leverage massive quantities of data and compute, but it’s premature to declare that they’ve secured victory in the AI Wars.
- benreesman 4y ago
  I agree that it’s still a jump ball in a rapidly moving field, I was saying Google is far ahead, not that they’ve won. And I don’t think whatever iteration of PaLM was cooking at the time GPT-3 started getting press would have looked to shabby. I think Google crushed OpenAI on both GPT and DALL-E in short order because OpenAI published twice and someone had had enough.
  - dougabug 4y ago
    That’s pretty speculative and dubious (the holding back part) given the heavy bias to publication culture at Google Research and DeepMind. OpenAI has hardly been “crushed” here; PaLM and Imagen are solid, incremental advances, but given what came before them, not Earth-shattering. If I were going to cite evidence for Alphabet’s “supremacy” in AI, I would’ve picked something more novel and surprising such as AlphaFold, or perhaps even Gato. It’s not clear to me that Google has anything which compares to Reality Labs, although this may simply be my own ignorance. Nvidia surely scooped Google with Instant Neural Graphics Primitives, in spite of Google publishing dozens of (often very interesting) NeRF papers. It’s not a war, all these works build on one another.
    - benreesman 4y ago
      I want to be clear, all of this stuff is fascinating, expensive, and difficult. With the possible exception of a few trailer-park weirdos like me, it basically takes a PhD to even stay on top of the field, and you clearly know your stuff. And to be equally clear, I have no inside baseball on how Brain/DM choose when to publish. I have some watercooler chat on the friendly but serious rivalry between those groups, but that’s about it. I’m looking from the outside in at OpenAI getting all the press and attention, which sounds superficial but sooner or later turns into actual hires of actual star-bound post docs, and Google laying a little low for a few years. Then we get Gato, Imagen, and PaLM in the space of like what, 2 months? Clearly I’m speculating that someone pulled the trigger, but I don’t think it’s like, absurd.
joshcryer 4y ago
Who does it serve for plebs to be shown the approach openly? I don't know that it does a disservice to anyone by showing the approach. But in general it is likely more due in part to the fact that it's going to happen anyway, if we can share our approaches and research findings, we'll just achieve it sooner.
- benreesman 4y ago
  Once upon a time you could lie only a little bit and Stanford would give you the whole ImageNet corpus. I know because, uh, a friend told me. I’ve got no interest in moralizing on this, but if any of the big actors wanted to they could put a meaningful if not overwhelming subset of the corpus on S3, put the source code on GitHub, and you could on a modest budget see an epoch or 3. I’m not holding my breath.
Herodotus38 4y ago
Is Maxwell's Demon applicable to this scenario? I'm not a physicist but I recently had to look it up after talking with someone and thought it had to do with a specific thermodynamic thought experiment with gas particles and heat differences. Is there is another application I don't understand with computing power?
- benreesman 4y ago
  You’re absolutely right that I used a sloppy analogy there. It’s Boltzmann and Szilard that did the original “kT” stuff around underlying thermodynamics governing energy dissipation in these scenarios, and Rolf Landaeur (I think that’s how you spell it) who did the really interesting work on how to apply that thermo work to lower-bounds on energy-expenditure in a given computation. I said Maxwell’s Demon because it’s the best known example of a deep connection between useful work and computation. But it was sloppy.
  - Herodotus38 4y ago
    OK thanks I figured there was a connection between computational power and thermodynamics when you get to a small enough scale but I wasn't sure how to apply it!
chillee 4y ago
> Neither OpenAI nor FAIR ever has the top score on anything unless Google delays publication. This is ... very incorrect. I am very certain (95%+) that Google had nothing even close to GPT-3 at the time of its release. It's been 2 full years since GPT-3 was released, and even longer since OpenAI actually trained it. That's not to talk about any of the other things OpenAI/FAIR has released that were SOTA at the time of release (Dall-E 1, JukeBox, Poker, Diplomacy, Codex). Google Brain and Deepmind have done a lot of great work, but to imply that they essentially have a monopoly on SOTA results and all SOTA results other labs have achieved are just due to Google delaying publication is ridiculous.
- gwern 4y ago
  Yeah, at the time, GB was still very big on mixture-of-expert models and bidirectional models like T5. (I'm not too enthusiastic about the former, but the latter has been a great model family and even if not GPT-3, still awesome.) DeepMind pivoted faster than GB, based on Gopher's reported training date, and GB followed some time after. But definitely neither had their own GPT-3-scale dense Transformer when GPT-3 was published.
  - benreesman 4y ago
    At the risk of sounding like I’m trying to defend a position that I’ve already conceded is an oversimplification, I’m frankly a little skeptical of how we can even know that. GPT is, opaque. It’s somewhere between common knowledge and conspiracy theory that it gets a helping hand from Turks when it gets in over its head. The exact details of why a BERT-style transformer, or any of the zillion other lookalikes, isn’t just over-fitting Wikipedia the more corpus and compute you feed to its insatiable maw has always seemed a little big on claims and light on reproducibility. I don’t think there are many attention skeptics in language modeling, it’s a good idea that you can demo on a gaming PC. Transformers demonstrably work, and a better beam-search (or whatever) hits the armchair Turing test harder for a given compute budget. But having seen some of this stuff play out at scale, and admittedly this is purely anecdotal, these things are basically asking the question: “if I overfit all human language on the Internet, is that a bad thing?” It’s my personal suspicion that this is the dominant term, and it’s my personal belief that Google’s ability to do both corpus and model parallelism at Jeff Dean levels while simultaneously building out hardware to the exact precision required is unique by a long way. But, to be more accurate than I was in my original comment, I don’t know most of that in the sense that would be required by peer-review, let alone a jury. It’s just an educated guess.
deleted 4y ago
[deleted]

meowface 4y ago

Not elitist at all; I highly appreciate this post. I know the basics of ML but otherwise am clueless when it comes to the true depths of this field and it's interesting to hear this perspective.

benreesman 4y ago
I used a lot of jargon and lingo and inside baseball in that post, it was intended for people who have deep background. But if you’re interested I’m happy to (attempt) answers to anything that was jargon: by virtue of HN my answers will be peer-reviewed in real time, and with only modest luck, a true expert might chime in.
- blinding-streak 4y ago
  Is there a handy list of generally recognized AI advancements, and their owners, that you would recommend reviewing? Or perhaps, seminal papers published? I'm only tangentially familiar with the field but would be curious to learn about the clash of the Titans playing out. Thanks!
  - benreesman 4y ago
    That’s too big a question to even attempt an answer in an HN comment, but to try to answer a realistic subset of it: “Attention is All You Need” in like 2017 is the paper most germane to my remark, and probably the thread. The modeling style it introduced often gets called a “transformer”. The TLDR is that people had been trying for ages to capture long-distance (in the input or output, not the black box) relationships in a way that was amenable to traditional neural-network training techniques, which is non-obvious how to do because your basic NN takes an input without a distance metric, or put more plainly: it can know all the words in a sentence but struggles with what order they are in without some help. The state of the art for awhile was something called an LSTM, and those gadgets are still useful sometimes, but have mostly been obsoleted by this attention/transformer business. That paper had a number of cool things in it but two stand out: - by blinding an NN to some parts of the input (“masking”) you can incentivize/compel it to look at (“attend to”) others. That’s a gross oversimplification, but it gets the gist of it I think. People have come up with very clever ways to boost up this or that part of the input in a context-dependent way. - by playing with some trigonometry you can get a unique shape that came be expressed as a sun on something else that gives the model its “bearings” so to speak as to “where” it is in the input. such a word is closer to the beginning of a paragraph sort of a thing. people have also gotten very clever about how to do this, but the idea is the same: how do I tell a neural network that there’s structure in what would otherwise be a pile of numbers.

ttul 4y ago

In short, it’s all about money.

benreesman 4y ago
Yes and no. For example: the high-frequency trading industry is estimated to have made somewhere between 2-3 billion dollars in all of 2020, profit/earnings. That’s a good weekend at Google. HFT shops pay well, but not much different to top performers at FAANG. People work in HFT because without taking a pay cut they can play real ball: they want to try themselves against the best. Heavy learning people are no different in wanting both a competitive TC but maybe even more to be where the action is. That’s currently Blade Runner Industries Ltd, but that could change.

f38zf5vdt 4y ago

> But the incentives are weird here: who, exactly, does it serve for us plebs to be able to train these things from scratch? I'm not sure it matters. The history of computing shows that within the decade we will all have the ability to train and use these models.

sinenomine 4y ago
... Unless the possession of capable models becomes a legal liability by that time.
- astrange 4y ago
  This won’t happen in an interesting way. What will happen is you’ll find out training a model on copyrighted inputs causes it to memorize those inputs and the owners own your output.

dougabug 4y ago

This characterization is not really accurate. OpenAI has had almost a 2 year lead with GPT-3 dominating the discussion of LLMs (large language models). Google didn’t release its paper on the powerful PaLM-540b model until recently. Similarly, CLiP, Glide, DALL-E, and DALL-E2 have been incredibly influential in visual-language models. Imagen, while highly impressive, definitely is a catch-up piece of work (as was PaLM-540b). Google clearly demonstrates their unrivaled capability to leverage massive quantities of data and compute, but it’s premature to declare that they’ve secured victory in the AI Wars.

benreesman 4y ago
I agree that it’s still a jump ball in a rapidly moving field, I was saying Google is far ahead, not that they’ve won. And I don’t think whatever iteration of PaLM was cooking at the time GPT-3 started getting press would have looked to shabby. I think Google crushed OpenAI on both GPT and DALL-E in short order because OpenAI published twice and someone had had enough.
- dougabug 4y ago
  That’s pretty speculative and dubious (the holding back part) given the heavy bias to publication culture at Google Research and DeepMind. OpenAI has hardly been “crushed” here; PaLM and Imagen are solid, incremental advances, but given what came before them, not Earth-shattering. If I were going to cite evidence for Alphabet’s “supremacy” in AI, I would’ve picked something more novel and surprising such as AlphaFold, or perhaps even Gato. It’s not clear to me that Google has anything which compares to Reality Labs, although this may simply be my own ignorance. Nvidia surely scooped Google with Instant Neural Graphics Primitives, in spite of Google publishing dozens of (often very interesting) NeRF papers. It’s not a war, all these works build on one another.
  - benreesman 4y ago
    I want to be clear, all of this stuff is fascinating, expensive, and difficult. With the possible exception of a few trailer-park weirdos like me, it basically takes a PhD to even stay on top of the field, and you clearly know your stuff. And to be equally clear, I have no inside baseball on how Brain/DM choose when to publish. I have some watercooler chat on the friendly but serious rivalry between those groups, but that’s about it. I’m looking from the outside in at OpenAI getting all the press and attention, which sounds superficial but sooner or later turns into actual hires of actual star-bound post docs, and Google laying a little low for a few years. Then we get Gato, Imagen, and PaLM in the space of like what, 2 months? Clearly I’m speculating that someone pulled the trigger, but I don’t think it’s like, absurd.

joshcryer 4y ago

Who does it serve for plebs to be shown the approach openly? I don't know that it does a disservice to anyone by showing the approach. But in general it is likely more due in part to the fact that it's going to happen anyway, if we can share our approaches and research findings, we'll just achieve it sooner.

benreesman 4y ago
Once upon a time you could lie only a little bit and Stanford would give you the whole ImageNet corpus. I know because, uh, a friend told me. I’ve got no interest in moralizing on this, but if any of the big actors wanted to they could put a meaningful if not overwhelming subset of the corpus on S3, put the source code on GitHub, and you could on a modest budget see an epoch or 3. I’m not holding my breath.

Herodotus38 4y ago

Is Maxwell's Demon applicable to this scenario? I'm not a physicist but I recently had to look it up after talking with someone and thought it had to do with a specific thermodynamic thought experiment with gas particles and heat differences. Is there is another application I don't understand with computing power?

benreesman 4y ago
You’re absolutely right that I used a sloppy analogy there. It’s Boltzmann and Szilard that did the original “kT” stuff around underlying thermodynamics governing energy dissipation in these scenarios, and Rolf Landaeur (I think that’s how you spell it) who did the really interesting work on how to apply that thermo work to lower-bounds on energy-expenditure in a given computation. I said Maxwell’s Demon because it’s the best known example of a deep connection between useful work and computation. But it was sloppy.
- Herodotus38 4y ago
  OK thanks I figured there was a connection between computational power and thermodynamics when you get to a small enough scale but I wasn't sure how to apply it!

chillee 4y ago

> Neither OpenAI nor FAIR ever has the top score on anything unless Google delays publication. This is ... very incorrect. I am very certain (95%+) that Google had nothing even close to GPT-3 at the time of its release. It's been 2 full years since GPT-3 was released, and even longer since OpenAI actually trained it. That's not to talk about any of the other things OpenAI/FAIR has released that were SOTA at the time of release (Dall-E 1, JukeBox, Poker, Diplomacy, Codex). Google Brain and Deepmind have done a lot of great work, but to imply that they essentially have a monopoly on SOTA results and all SOTA results other labs have achieved are just due to Google delaying publication is ridiculous.

gwern 4y ago
Yeah, at the time, GB was still very big on mixture-of-expert models and bidirectional models like T5. (I'm not too enthusiastic about the former, but the latter has been a great model family and even if not GPT-3, still awesome.) DeepMind pivoted faster than GB, based on Gopher's reported training date, and GB followed some time after. But definitely neither had their own GPT-3-scale dense Transformer when GPT-3 was published.
- benreesman 4y ago
  At the risk of sounding like I’m trying to defend a position that I’ve already conceded is an oversimplification, I’m frankly a little skeptical of how we can even know that. GPT is, opaque. It’s somewhere between common knowledge and conspiracy theory that it gets a helping hand from Turks when it gets in over its head. The exact details of why a BERT-style transformer, or any of the zillion other lookalikes, isn’t just over-fitting Wikipedia the more corpus and compute you feed to its insatiable maw has always seemed a little big on claims and light on reproducibility. I don’t think there are many attention skeptics in language modeling, it’s a good idea that you can demo on a gaming PC. Transformers demonstrably work, and a better beam-search (or whatever) hits the armchair Turing test harder for a given compute budget. But having seen some of this stuff play out at scale, and admittedly this is purely anecdotal, these things are basically asking the question: “if I overfit all human language on the Internet, is that a bad thing?” It’s my personal suspicion that this is the dominant term, and it’s my personal belief that Google’s ability to do both corpus and model parallelism at Jeff Dean levels while simultaneously building out hardware to the exact precision required is unique by a long way. But, to be more accurate than I was in my original comment, I don’t know most of that in the sense that would be required by peer-review, let alone a jury. It’s just an educated guess.

deleted 4y ago

[deleted]