Evidence receipt / belief
Published · transcript-backedSholto Douglas: belief
28 Mar 2024 Dwarkesh Podcast Sholto Douglas & Trenton Bricken — How LLMs actually think
“I think that's more about nines of reliability and the model actually successfully doing things.”
Source trail
Everything needed to verify it.
- Speaker
- Sholto Douglas
- Attribution
- Verified speaker
- Claim type
- belief
- Recorded
- 28 Mar 2024
- Publisher
- Dwarkesh Podcast
Transcript context
…Right, but you can proxy for that just by getting better at doing long-term context tasks. One of the bottlenecks for AI progress that many people identify is the inability of these models to perform tasks on long horizons, engaging with the task for many hours, or even many weeks or months, where they’re an assistant or an employee and they can just do a thing I tell them to do for a while. AI agents haven't taken off for this reason from what I understand. So how linked are long context windows, and the ability to perform well on them, and the ability to do these kinds of long-horizon tasks that require you to engage with an assignment for many hours? Or are these unrelated concepts? I would take issue with that being the reason that agents haven't taken off. I think that's more about nines of reliability and the model actually successfully doing things. If you can't chain tasks successively with high enough probability, then you won't get something that looks like an agent. And that's why something like an agent might follow more of a step function. In GPT-4 class models, Gemini Ultra class models, they're not enough. But maybe the next increment on model scale means that you get that extra nine. Even though the loss isn't going down that dramatically, that small amount of extra ability gives you the extra. Obviously you need some amount of context to fit long-horizon tasks, but I don't think that's been the limiting factor up to now. The NeurIPS best paper this year, by Rylan Schaeffer who was the lead author, points to this as the emergence of mirage. People will have a task and you get the right or wrong answer depending on if you've sampled the last five tokens correctly. So naturally you're multiplying the probability of sampling all of those and if you don't have enough nines of reliability, then you're not going to get emergence. And all of a sudden you do and it's, “oh my gosh, this ability is emergent,” when actually it was kind of there to begin with.…
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