6 ms·

Altman’s push for regulatory capture makes so much sense given how fast this field is going. Open models you can run on regular hardware are still behind GPT-4

by api 3y ago

Altman’s push for regulatory capture makes so much sense given how fast this field is going. Open models you can run on regular hardware are still behind GPT-4 by some distance but they are closing in at a rate that leads me to believe there’s not much of a moat there. There’s also a ton of promising work on quantization and pruning and other acceleration and compression techniques to make more powerful models run on smaller devices. So far the focus has been on just getting these things to work, not efficiency. There’s probably a lot of fruit to be picked here. A few more years and a gaming PC may be at GPT-4 level or maybe even better. No everyone won’t run their own models but it shows that there will end up being many commercial apps and services and they won’t all have to use OpenAI’s API. There’s going to be lots of competition. Unless of course it’s regulated away.

sva_ 3y ago
To be fair, Altman has been fairly outspoken about not limiting open source models. In how far that was just for streetcred, I cannot say however.
- api 3y ago
  If progress really continues like this any regulation that does not limit open models would be a pointless exercise. It’s only a matter of time before people really crack distributed training algorithms that can be run in a less organized swarm configuration. At that point open trainers could actually train near the frontier of what is possible. Most of the data is open.
  - skybrian 3y ago
    Whether or not the regulation goes anywhere, OpenAI keeps doing what they're doing, and they look good to politicians for being cooperative.
  - sanxiyn 3y ago
    > It's only a matter of time before people really crack distributed training algorithms that can be run in a less organized swarm configuration. Why do you think this? This continually failed, and seems extremely unlikely to me. Barring surprising breakthrough, there is inherent communication complexity, and physical limit to communication bandwidth.
    - akiselev 3y ago
      Yeah the network bandwidth is insane. Each A100 in an 8x A100 (80GB) pod [1] has its own 200 gigabit NIC! Including storage that's nine 200gbps interfaces for almost two terabit of total internal bandwidth. The newer H100s each have a 400gbit NIC. [1] https://shop.lambdalabs.com/deep-learning/servers/hyperplane/customize https://shop.lambdalabs.com/deep-learning/servers/hyperplane...
      - api 3y ago
        The assumption I'm questioning is that it will always take a rig like that to train large models. I don't see anything inherent in the math or nature of these things that forbids a wider, more heterogenous, less synchronized, less orderly training path that involves basically a mob of people training and merging results. We just haven't really figured it out yet. I actually see a little bit of this happening on Huggingface with people creating variations and "remixes" of generative models like Stable Diffusion and trying to one-up each other or make models to do esoteric things like render everything looking like anime. You're not going to get to the next frontier model with those methods but it shows that the interest exists and a flourishing ecosystem is forming. Now give that ecosystem new methods that are more powerful. People with more money can obviously buy or rent more hardware. The question is whether that advantage will stay as meaningful as it is today forever.
    - zirgs 3y ago
      We could try something like Civitai is doing already, but automated. Each node could train the model on a separate concept and then combine the results.
  - chaxor 3y ago
    The wording of this would be extremely difficult though. Are local NER models part of this? Relation extraction? What about GPTs that only decode to DSLs? If the model only outputs DNA sequences is that an area that can be more illegal or less if done for research by an individual? The breadth of different tasks and architectures can make this exceedingly challenging to regulate.
    - deleted 3y ago
      [deleted]
- starfallg 3y ago
  Open models means that OpenAI has access to and can learn from them. If the move is to target commercial competition, then this doesn't preclude it.
- yyyk 3y ago
  I won't take him seriously on this - if one really believes in the case for regulation, than there's no good reason to exclude open source models. Let's take the most pessimistic possibility, that they have a slower rate of progress than commercial models, and real progress depends on hardware. We still end up with the same result - every 'risky' point commercial models get to, open source will get to as well.
mwcampbell 3y ago
I wonder if there's a campaign that Americans can get behind to counter Altman's push for regulation. Such a thing might get me to engage in politics again. I don't want the current pace of innovation in running these models on one's own computer to slow down, because it could be a good thing for assistive technology, e.g. running something like the Be My Eyes Virtual Volunteer [1] on one's own computer. [1]: https://www.bemyeyes.com/blog/introducing-be-my-eyes-virtual-volunteer https://www.bemyeyes.com/blog/introducing-be-my-eyes-virtual...
yyyk 3y ago
>Open models... are still behind GPT-4 by some distance but they are closing in at a rate that leads me to believe there’s not much of a moat there Commercially, this doesn't matter even if true. As I keep reminding here: moats are rarely about raw tech. Moats are much more about integrations, brand ("Nobody got fired for buying X"), access to API/raw compute... If OpenAI have a superior Office 365 integration they'll have a de facto moat. If OpenAI have a larger plugin ecosystem they'll have a de facto moat. If OpenAI has access to better compute (that's far from a certainty) they'll have a moat. If it's much easier to use OpenAI than install a local model they'll have a moat, etc. And that's true even if they don't improve their model at all. What will open source offer? Privacy? You can see for how little and how easily people barter that. Moats do fall - but for that, FOSS will have to think product. We're not there at all at the moment. [EDIT: Oh, I didn't notice the author's name. We've had that conversation in the past here. Sorry for being repetitive.]
- ithkuil 3y ago
  > Open models... are still behind GPT-4 by some distance but they are closing in at a rate that leads me to believe there’s not much of a moat there This assumes that openai internally doesn't also "closing-in" towards something even more impressive to be released next year or whatnot.
- l33tman 3y ago
  I agree with you on that a good integrator can get successful, but we should not forget here that the underlying code running all of this is in the order of hundreds of lines (see alpaca.cpp for example) in combination with a boatload of money required for training (that is quickly going down), to simplify the argument a bit. It's hardly comparable to the gigantic ecosystem of services and micro-services that your comparison alludes to with IBM and Microsoft. OpenAI is nowhere near such a brand recognition, and grassroots support for it within a company would quickly move to the next free chatgpt-clone that is better or cheaper or faster or more accessible just as what happened with Dalle-2. The value of the models themselves will quickly come down to just a slight bit over the underlying hardware costs and Altman knows this. Microsoft bought themselves a $10B time window to try to do what you're saying, so let's see :) But even for them, when they've built LLM-adaptations to their most popular products, it's fairly simple to just swap it out with something new and more shiny and cheaper that's not OpenAI, and the end customer won't notice as it's the Microsoft or Office brand that they buy into. They are not going to advertise what's inside their products with big banners "Powered by OpenAI" in the long run, I think (do they now?)
n4r9 3y ago
Just to be super clear, do you mean Altman's push for greater regulation, or that he is pushing for actual regulatory capture i.e. corruption of regulating authorities?

by api 3y ago

sva_ 3y ago

To be fair, Altman has been fairly outspoken about not limiting open source models. In how far that was just for streetcred, I cannot say however.

api 3y ago
If progress really continues like this any regulation that does not limit open models would be a pointless exercise. It’s only a matter of time before people really crack distributed training algorithms that can be run in a less organized swarm configuration. At that point open trainers could actually train near the frontier of what is possible. Most of the data is open.
- skybrian 3y ago
  Whether or not the regulation goes anywhere, OpenAI keeps doing what they're doing, and they look good to politicians for being cooperative.
- sanxiyn 3y ago
  > It's only a matter of time before people really crack distributed training algorithms that can be run in a less organized swarm configuration. Why do you think this? This continually failed, and seems extremely unlikely to me. Barring surprising breakthrough, there is inherent communication complexity, and physical limit to communication bandwidth.
  - akiselev 3y ago
    Yeah the network bandwidth is insane. Each A100 in an 8x A100 (80GB) pod [1] has its own 200 gigabit NIC! Including storage that's nine 200gbps interfaces for almost two terabit of total internal bandwidth. The newer H100s each have a 400gbit NIC. [1] https://shop.lambdalabs.com/deep-learning/servers/hyperplane/customize https://shop.lambdalabs.com/deep-learning/servers/hyperplane...
    - api 3y ago
      The assumption I'm questioning is that it will always take a rig like that to train large models. I don't see anything inherent in the math or nature of these things that forbids a wider, more heterogenous, less synchronized, less orderly training path that involves basically a mob of people training and merging results. We just haven't really figured it out yet. I actually see a little bit of this happening on Huggingface with people creating variations and "remixes" of generative models like Stable Diffusion and trying to one-up each other or make models to do esoteric things like render everything looking like anime. You're not going to get to the next frontier model with those methods but it shows that the interest exists and a flourishing ecosystem is forming. Now give that ecosystem new methods that are more powerful. People with more money can obviously buy or rent more hardware. The question is whether that advantage will stay as meaningful as it is today forever.
  - zirgs 3y ago
    We could try something like Civitai is doing already, but automated. Each node could train the model on a separate concept and then combine the results.
- chaxor 3y ago
  The wording of this would be extremely difficult though. Are local NER models part of this? Relation extraction? What about GPTs that only decode to DSLs? If the model only outputs DNA sequences is that an area that can be more illegal or less if done for research by an individual? The breadth of different tasks and architectures can make this exceedingly challenging to regulate.
  - deleted 3y ago
    [deleted]
starfallg 3y ago
Open models means that OpenAI has access to and can learn from them. If the move is to target commercial competition, then this doesn't preclude it.
yyyk 3y ago
I won't take him seriously on this - if one really believes in the case for regulation, than there's no good reason to exclude open source models. Let's take the most pessimistic possibility, that they have a slower rate of progress than commercial models, and real progress depends on hardware. We still end up with the same result - every 'risky' point commercial models get to, open source will get to as well.

mwcampbell 3y ago

I wonder if there's a campaign that Americans can get behind to counter Altman's push for regulation. Such a thing might get me to engage in politics again. I don't want the current pace of innovation in running these models on one's own computer to slow down, because it could be a good thing for assistive technology, e.g. running something like the Be My Eyes Virtual Volunteer [1] on one's own computer. [1]: https://www.bemyeyes.com/blog/introducing-be-my-eyes-virtual-volunteer https://www.bemyeyes.com/blog/introducing-be-my-eyes-virtual...

yyyk 3y ago

>Open models... are still behind GPT-4 by some distance but they are closing in at a rate that leads me to believe there’s not much of a moat there Commercially, this doesn't matter even if true. As I keep reminding here: moats are rarely about raw tech. Moats are much more about integrations, brand ("Nobody got fired for buying X"), access to API/raw compute... If OpenAI have a superior Office 365 integration they'll have a de facto moat. If OpenAI have a larger plugin ecosystem they'll have a de facto moat. If OpenAI has access to better compute (that's far from a certainty) they'll have a moat. If it's much easier to use OpenAI than install a local model they'll have a moat, etc. And that's true even if they don't improve their model at all. What will open source offer? Privacy? You can see for how little and how easily people barter that. Moats do fall - but for that, FOSS will have to think product. We're not there at all at the moment. [EDIT: Oh, I didn't notice the author's name. We've had that conversation in the past here. Sorry for being repetitive.]

ithkuil 3y ago
> Open models... are still behind GPT-4 by some distance but they are closing in at a rate that leads me to believe there’s not much of a moat there This assumes that openai internally doesn't also "closing-in" towards something even more impressive to be released next year or whatnot.
l33tman 3y ago
I agree with you on that a good integrator can get successful, but we should not forget here that the underlying code running all of this is in the order of hundreds of lines (see alpaca.cpp for example) in combination with a boatload of money required for training (that is quickly going down), to simplify the argument a bit. It's hardly comparable to the gigantic ecosystem of services and micro-services that your comparison alludes to with IBM and Microsoft. OpenAI is nowhere near such a brand recognition, and grassroots support for it within a company would quickly move to the next free chatgpt-clone that is better or cheaper or faster or more accessible just as what happened with Dalle-2. The value of the models themselves will quickly come down to just a slight bit over the underlying hardware costs and Altman knows this. Microsoft bought themselves a $10B time window to try to do what you're saying, so let's see :) But even for them, when they've built LLM-adaptations to their most popular products, it's fairly simple to just swap it out with something new and more shiny and cheaper that's not OpenAI, and the end customer won't notice as it's the Microsoft or Office brand that they buy into. They are not going to advertise what's inside their products with big banners "Powered by OpenAI" in the long run, I think (do they now?)

n4r9 3y ago

Just to be super clear, do you mean Altman's push for greater regulation, or that he is pushing for actual regulatory capture i.e. corruption of regulating authorities?