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?
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.
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.