8 ms·

What is the reason Google published their research details about Imagen? Why don't they just keep their findings to themselfes and build products on top of the

by TekMol 4y ago

What is the reason Google published their research details about Imagen? Why don't they just keep their findings to themselfes and build products on top of them? Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it?

pyinstallwoes 4y ago
Absolute power, corrupts absolutely. Perhaps it’s a game theoretic approach to leveling the playing field.
minimaxir 4y ago
Publishing high impact research gives credibility to the ML teams, which helps recruiting and prestige. It's less cynical, more incentive alignment.
- hackernewds 4y ago
  Also good for society. Less evil, more nice people. Respect Google for these traits.
  - benoketamad 4y ago
    Oh, they don't even want to create/release a usable image search... Respect, of course.
    - Filligree 4y ago
      Didn't they run into legal trouble with that?
      - Geonode 4y ago
        They could have won that fight, but they don't care enough. I use Yandex for images now, it's very good.
      - lostmsu 4y ago
        Care to link?
DeathArrow 4y ago
They did it out of goodness of their hearts. :)
dotnet00 4y ago
I think there are three main reasons why: - being open is kind of just how things in ML generally work right now, it's in stark contrast to things like chemistry or physics where paywalls are pretty common - it's a matter of clout, ML is moving ridiculously quickly, with work from just 5 years ago being considered outdated in terms of capability, if you don't publish, someone else will and they'll get the credit. This likely also matters for the researchers since they get credit too. In a sense this is just publish or perish culture from academia. - it's also somewhat about hiring, which is related to the clout. By putting out this kind of research, they're attracting talented engineers to consider working for them. This of course is pretty relevant to the rest of their business, especially given how heavily Google leans on AI to handle moderation.
curiousgal 4y ago
What would the product be? My experience is that most ML papers are very brittle, for every cool result/example you see there's a plethora of nonsense spit by the models.
frozenport 4y ago
1. This finding is hard to monetize, especially with ROI that Google typically does (for example a an app that makes $500 a month isn't worth it) 2. Deploying models in a cost effective way is hard 3. Lessons learned from building this model can indeed be monetized and many of them may be kept secret.
29athrowaway 4y ago
Because the competitive advantage is also in the training datasets and ML infrastructure.
nl 4y ago
> Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it? Yes they can actually. If you are a shareholder you can either sue (unlikely to succeed) or vote against the board. That's pretty much the only recourse.
- pmoriarty 4y ago
  "If you are a shareholder you can either sue (unlikely to succeed) or vote against the board. That's pretty much the only recourse." Or you could sell or just threaten to sell your shares. Buying more of the company's shares to take it over is another option. Good luck doing that with Google.
nmfisher 4y ago
Two reasons: 1) Even though it's all technically very impressive, so far there's not a huge amount of commercialization potential here. OpenAI is charging for its GPT-3 model but its revenue is probably negligible next to the hardware costs (sunk + ongoing) to train it in the first place, let alone the researcher salaries they're paying 2) Most of the stunning examples are cherry-picked. These things fail much more often than they are willing to admit, and they're probably assuming (correctly) that not enough people are willing to pay for something that only-sorta-kinda-works 1/3 of the time, when you're holding it the right way.
- adamhowell 4y ago
  I'm currently working fulltime on AI-powered design suite Accomplice (https://accomplice.ai https://accomplice.ai) and if you ask me on a good day I would tell you I do think there's already huge commercial potential. On a bad day, though ;) My current approach is a "model marketplace" (https://accomplice.ai/models https://accomplice.ai/models) where the most popular open source text-to-image models (VQGAN+CLIP, Disco Diffusion, DALL-E Mega coming soon…), sit alongside the most popular open source style transfer models, and then finally I have the ability for a user to finetune their own models using a simple drag-and-drop tool (https://accomplice.ai/no-code-model-training https://accomplice.ai/no-code-model-training). Using this approach a user has enough models to try or train that they can have a higher hit rate. For example, Accomplice currently has finetuned models for photo realistic people (https://accomplice.ai/models/f58bfa91-bb18-406f-a0e1-db00fcf2b080 https://accomplice.ai/models/f58bfa91-bb18-406f-a0e1-db00fcf...), watercolor backgrounds (https://accomplice.ai/models/91b8a080-faca-4ff4-8b11-64b07895291a https://accomplice.ai/models/91b8a080-faca-4ff4-8b11-64b0789...), etc… So theoretically if there were a searchable marketplace of 100s of different finetuned models people could choose from, they would use it much like an iStockPhoto and be able to create the kind of images they want instead of just downloading them. But it's of course a constant work in progress. Slowly growing though and lots of promising stuff ahead!
  - TekMol 4y ago
    I wanted to try your site, but after clicking on the link in the email, it tries to send me through some redirectingat.com link which is blocked by my ad blocker. What does that link do?
quickthrower2 4y ago
It could be classed as a way to attract talent. It is basically then a very expensive fusball table.
toxik 4y ago
It’s simply flag planting. If they don’t do it, some other player will.
blueblob 4y ago
I think it is because the proprietary part for them is the data, not the particular algorithm. They benefit more from other people making advances on their technology because they have the data to get more benefit than anyone else. If they kept it to themselves, they would get no "free" advancement. So they trade-off the secret of the technique in the hope that others will advance the technique, making their data more valuable.
mzs 4y ago
You realize this doesn't include the trained data, right?
yaroslavvb 4y ago
Researchers like to talk about and show off their work outside the company. If you don't let them, they get unhappy and leave.
dekhn 4y ago
Google started publishing for several reasons but the primary one was recruitment (showing off was a secondary goal). The mapreduce, GFS, and bigtable papers played an important role in attracting an early generation of distributed computing/high performance computing people from around the valley and CMU/MIT, who helped build the second really successful versions of the web search engine (retrieval and ranking), ads serving (the auction, the logs joining pipeline), etc. The other reason is that the leaders at Google at the time believed that we would achieve the singularity faster if Jeff Dean periodically sent ideas back 10 years in time to Doug Cutting.
vintermann 4y ago
This is an important question. You're saying it's Google who have done this research. In a way that's true. But really it is Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet and Mohammad Norouzi who did it, with material support from Google. It's likely that some or all of these people would have refused to do the work they do if Google kept it all as their secret sauce. And moreover, there are excellent reasons why they wouldn't want to. It's not just the obvious that if it all were secret, they wouldn't be able to use it in their non-Google career advancement. It's also that research without the freedom to talk is far more difficult and frustrating. On paper, scientific papers are supposed to document the whole of the discovery/innovation. So you might think that an insider, who got to read all the secret Google research papers AND all the public ones would have an advantage. But problem is, even the best written papers with full code and comments inevitably leave out things, especially of the "why this and not that" type. If you're a researcher in the free world, you can just ask. Especially if you have a public track record of great papers yourself, they will WANT to talk to you. You can learn so much more from the interactive process of back and forth questions than you can from a static piece of information like a scientific paper. If you work for a secretive and command-driven organization, you need to be careful about what you reveal of your own research when you ask. You can't talk freely. The thought of having to justify your communication to some old-school IBM lawyer type, is going to chill even the most enthusiastic reseacher. It's easier to just stay in your own corporate bubble, and focus on the things your corporation does well since at least you can talk freely to your colleagues (although in really paranoid organizations like the NSA or old IBM, even that may not be true). But then at best you specialize, at worst you fall behind.

by TekMol 4y ago

pyinstallwoes 4y ago

Absolute power, corrupts absolutely. Perhaps it’s a game theoretic approach to leveling the playing field.

minimaxir 4y ago

Publishing high impact research gives credibility to the ML teams, which helps recruiting and prestige. It's less cynical, more incentive alignment.

hackernewds 4y ago
Also good for society. Less evil, more nice people. Respect Google for these traits.
- benoketamad 4y ago
  Oh, they don't even want to create/release a usable image search... Respect, of course.
  - Filligree 4y ago
    Didn't they run into legal trouble with that?
    - Geonode 4y ago
      They could have won that fight, but they don't care enough. I use Yandex for images now, it's very good.
    - lostmsu 4y ago
      Care to link?

DeathArrow 4y ago

They did it out of goodness of their hearts. :)

dotnet00 4y ago

I think there are three main reasons why: - being open is kind of just how things in ML generally work right now, it's in stark contrast to things like chemistry or physics where paywalls are pretty common - it's a matter of clout, ML is moving ridiculously quickly, with work from just 5 years ago being considered outdated in terms of capability, if you don't publish, someone else will and they'll get the credit. This likely also matters for the researchers since they get credit too. In a sense this is just publish or perish culture from academia. - it's also somewhat about hiring, which is related to the clout. By putting out this kind of research, they're attracting talented engineers to consider working for them. This of course is pretty relevant to the rest of their business, especially given how heavily Google leans on AI to handle moderation.

curiousgal 4y ago

What would the product be? My experience is that most ML papers are very brittle, for every cool result/example you see there's a plethora of nonsense spit by the models.

frozenport 4y ago

1. This finding is hard to monetize, especially with ROI that Google typically does (for example a an app that makes $500 a month isn't worth it) 2. Deploying models in a cost effective way is hard 3. Lessons learned from building this model can indeed be monetized and many of them may be kept secret.

29athrowaway 4y ago

Because the competitive advantage is also in the training datasets and ML infrastructure.

nl 4y ago

> Public companies can't do stuff just for the fun of it, right? So there must be some commercial reasoning behind it? Yes they can actually. If you are a shareholder you can either sue (unlikely to succeed) or vote against the board. That's pretty much the only recourse.

pmoriarty 4y ago
"If you are a shareholder you can either sue (unlikely to succeed) or vote against the board. That's pretty much the only recourse." Or you could sell or just threaten to sell your shares. Buying more of the company's shares to take it over is another option. Good luck doing that with Google.

nmfisher 4y ago

Two reasons: 1) Even though it's all technically very impressive, so far there's not a huge amount of commercialization potential here. OpenAI is charging for its GPT-3 model but its revenue is probably negligible next to the hardware costs (sunk + ongoing) to train it in the first place, let alone the researcher salaries they're paying 2) Most of the stunning examples are cherry-picked. These things fail much more often than they are willing to admit, and they're probably assuming (correctly) that not enough people are willing to pay for something that only-sorta-kinda-works 1/3 of the time, when you're holding it the right way.

adamhowell 4y ago
I'm currently working fulltime on AI-powered design suite Accomplice (https://accomplice.ai https://accomplice.ai) and if you ask me on a good day I would tell you I do think there's already huge commercial potential. On a bad day, though ;) My current approach is a "model marketplace" (https://accomplice.ai/models https://accomplice.ai/models) where the most popular open source text-to-image models (VQGAN+CLIP, Disco Diffusion, DALL-E Mega coming soon…), sit alongside the most popular open source style transfer models, and then finally I have the ability for a user to finetune their own models using a simple drag-and-drop tool (https://accomplice.ai/no-code-model-training https://accomplice.ai/no-code-model-training). Using this approach a user has enough models to try or train that they can have a higher hit rate. For example, Accomplice currently has finetuned models for photo realistic people (https://accomplice.ai/models/f58bfa91-bb18-406f-a0e1-db00fcf2b080 https://accomplice.ai/models/f58bfa91-bb18-406f-a0e1-db00fcf...), watercolor backgrounds (https://accomplice.ai/models/91b8a080-faca-4ff4-8b11-64b07895291a https://accomplice.ai/models/91b8a080-faca-4ff4-8b11-64b0789...), etc… So theoretically if there were a searchable marketplace of 100s of different finetuned models people could choose from, they would use it much like an iStockPhoto and be able to create the kind of images they want instead of just downloading them. But it's of course a constant work in progress. Slowly growing though and lots of promising stuff ahead!
- TekMol 4y ago
  I wanted to try your site, but after clicking on the link in the email, it tries to send me through some redirectingat.com link which is blocked by my ad blocker. What does that link do?

quickthrower2 4y ago

It could be classed as a way to attract talent. It is basically then a very expensive fusball table.

toxik 4y ago

It’s simply flag planting. If they don’t do it, some other player will.

blueblob 4y ago

I think it is because the proprietary part for them is the data, not the particular algorithm. They benefit more from other people making advances on their technology because they have the data to get more benefit than anyone else. If they kept it to themselves, they would get no "free" advancement. So they trade-off the secret of the technique in the hope that others will advance the technique, making their data more valuable.

mzs 4y ago

You realize this doesn't include the trained data, right?

yaroslavvb 4y ago

Researchers like to talk about and show off their work outside the company. If you don't let them, they get unhappy and leave.

dekhn 4y ago

Google started publishing for several reasons but the primary one was recruitment (showing off was a secondary goal). The mapreduce, GFS, and bigtable papers played an important role in attracting an early generation of distributed computing/high performance computing people from around the valley and CMU/MIT, who helped build the second really successful versions of the web search engine (retrieval and ranking), ads serving (the auction, the logs joining pipeline), etc. The other reason is that the leaders at Google at the time believed that we would achieve the singularity faster if Jeff Dean periodically sent ideas back 10 years in time to Doug Cutting.

vintermann 4y ago

This is an important question. You're saying it's Google who have done this research. In a way that's true. But really it is Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet and Mohammad Norouzi who did it, with material support from Google. It's likely that some or all of these people would have refused to do the work they do if Google kept it all as their secret sauce. And moreover, there are excellent reasons why they wouldn't want to. It's not just the obvious that if it all were secret, they wouldn't be able to use it in their non-Google career advancement. It's also that research without the freedom to talk is far more difficult and frustrating. On paper, scientific papers are supposed to document the whole of the discovery/innovation. So you might think that an insider, who got to read all the secret Google research papers AND all the public ones would have an advantage. But problem is, even the best written papers with full code and comments inevitably leave out things, especially of the "why this and not that" type. If you're a researcher in the free world, you can just ask. Especially if you have a public track record of great papers yourself, they will WANT to talk to you. You can learn so much more from the interactive process of back and forth questions than you can from a static piece of information like a scientific paper. If you work for a secretive and command-driven organization, you need to be careful about what you reveal of your own research when you ask. You can't talk freely. The thought of having to justify your communication to some old-school IBM lawyer type, is going to chill even the most enthusiastic reseacher. It's easier to just stay in your own corporate bubble, and focus on the things your corporation does well since at least you can talk freely to your colleagues (although in really paranoid organizations like the NSA or old IBM, even that may not be true). But then at best you specialize, at worst you fall behind.