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Well, one difference is that OpenAI is still independent, so it stands to capture much more of the upside than DeepMind does if they're equally successful. I do think that motivational difference matters a lot. It isn't just Altman vs. Hassabis who are motivated differently, it's everyone at the respective organizations.
I think so. RL (DM's apparent primary focus) has been kind of a bust; all of the exciting stuff is in the realm of large language models these days, and OpenAI bet big on that area after they got bored with DOTA.
OpenAI have come back to RL, though (with a twist, in the form of RLHF and related techniques) – its product is what we are seeing here. And it's not like end-to-end RL is dead, I'm seeing some very strong papers recently. Technically it can be very different, but the spirit is the same. Agents will plausibly have their time to shine.
But LLMs stil rule, and I hope you're right and the race will be smooth for OpenAI. That, considering structural advantages of Google, is the smallest form of multipolarity we can ask for.
True, but RLHF is a pretty different beast from game RL (which they are still grinding hard on -- just today they announced that they cracked Stratego). Not sure that advances in one are particularly useful to the other.
Also I'm not calling it yet for OpenAI... the race is definitely still on and machine learning has a way of flipping the board every couple of years as one approach or another reaches a critical breakthrough and consolidates mindshare. Maybe pure RL is going to have its moment one of these years, and these LLMs will look like parlor tricks in hindsight.
They've cracked Stratego in June, I was making some noise about it back then, but much like BYOL-Explore and some other papers that catch my fancy, it didn't impress anyone else. It only took them half a year to get that into a traditional journal. I wonder what else they have had cooking for this span of time. Well, they'll be sure to boast of it soon, what with NeurIPS and all.
I think LLMs are cumbersome parlor tricks compared to the potential of agents, a transitional technology. But they do have the advantage of being intrinsically toothless (whatever orthodox Yuddites say about mesa-optimizers hiding within), so I think with the fear of AI misalignment we'll see them as pizza dough for general-purpose personal assistants in the nest few years (assuming, as usual, that things go remotely well).
Ah, good catch.
Agreed that LLMs don't seem to have "agentic potential" today, although I can imagine a future where AGI is basically really powerful LLMs attached together with some really simple scaffolding, where we all agree that the LLMs are the dynamo and the scaffolding is just some scripts moving embeddings between LLMs based on their instructions or whatever. Which is not to say that imagining a future is worth much.
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