Surely the solution here is "put the model on BitTorrent, you cowards".
Like, okay, the model's big and unwieldy to run. But hardware's always getting better, and there are lots of research use-cases where it's okay if it takes ten minutes to page the model in and out of SSD while generating predictions. Plus, maybe we'd get some more discoveries in the field of efficiently running huge models.
The arguments about "safety" were PR nonsense when they were making them about GPT-2, and they're nonsense now. It's a robot that blends up Reddit posts in a food processor, it's barely more advanced than tapping the iPhone predict-next-word button over and over, it's not going to hack the Pentagon or take over the world. The only reason OpenAI has ever had to not publish their models -- and I am ashamed that this industry doesn't call them out more often on this -- is so that they can generate positive press coverage on launch day with unrefutable cherry-picked examples.
It’s beyond absurd to build a model with public, user-created data, gathered and released for free by a nonprofit, and then claim “uh, the model’s too big and unwieldy, so we have to keep things under lock and key.”
I don’t doubt that they’ll profit handsomely from this approach, but it’s the height of cynicism to engage in this kind of stuff and their statements around the practice should be taken in kind.
The author though raises concerns about both the availability (openness) of the model as well as the current ability to run it due to cost (equity / equality of access). Making the model available would still not make it equal access.
I’m not saying I agree or disagree with the openness argument, but the equality argument is separate.
If they released it, people would figure out a way to run it “equitably” within months, if not weeks.
The amount of cheap GPU access floating out there is nuts. You can spin up a GPU instance to do best-in-class ML stuff using Fast.Ai on services like Paperspace or Colab, right now, for free.
Like, okay, the model's big and unwieldy to run. But hardware's always getting better, and there are lots of research use-cases where it's okay if it takes ten minutes to page the model in and out of SSD while generating predictions. Plus, maybe we'd get some more discoveries in the field of efficiently running huge models.
The arguments about "safety" were PR nonsense when they were making them about GPT-2, and they're nonsense now. It's a robot that blends up Reddit posts in a food processor, it's barely more advanced than tapping the iPhone predict-next-word button over and over, it's not going to hack the Pentagon or take over the world. The only reason OpenAI has ever had to not publish their models -- and I am ashamed that this industry doesn't call them out more often on this -- is so that they can generate positive press coverage on launch day with unrefutable cherry-picked examples.