Dwarkesh Podcast
Sholto Douglas & Trenton Bricken - How to Build & Understand GPT-7's Mind
28 Mar 2024
Had so much fun chatting with my good friends Trenton Bricken and Sholto Douglas on the podcast.No way to summarize it, except: This is the best context dump out there on how LLMs are trained, what capabilities they're likely to soon have, and what exactly is going on inside them.You would be shocked how much of what I know about this field, I've learned just from talking with them.To the extent that you've enjoyed my other AI interviews, now you know why.So excited to put this out. Enjoy! I certainly did :)Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. There's a transcript with links to all the papers the boys were throwing down - may help you follow along.Follow Trenton and Sholto on Twitter.Timestamps(00:00:00) - Long contexts(00:16:12) - Intelligence is just associations(00:32:35) - Intelligence explosion & great researchers(01:06:52) - Superposition & secret communication(01:22:34) - Agents & true reasoning(01:34:40) - How Sholto & Trenton got into AI research(02:07:16) - Are feature spaces the wrong way to think about intelligence?(02:21:12) - Will interp actually work on superhuman models(02:45:05) - Sholto’s technical challenge for the audience(03:03:57) - Rapid fire Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Full Episode
Okay, today I have the pleasure to talk with two of my good friends, Shilto and Trenton. Shilto.
You should have asked me to stop.
I wasn't going to say anything. Let's do this in reverse. How long have I started with my good friends?
Yeah, Gemini 1.5, the context, like, just wow.
Shit. Anyways, Shoto, Noah Brown... Noam Brown, the guy who wrote the diplomacy paper, he said this about Shilto. He said, he's only been in the field for 1.5 years, but people in AI know that he was one of the most important people behind Gemini's success. And Trenton, who's an anthropic, works on mechanistic interoperability, and it was widely reported that he has solved alignment.
With his recent paper on Twitter.
Yeah. So this will be a capabilities-only podcast. Alignment is already solved, so no need to discuss further. Okay, so let's start by talking about context links.
Yep.
It seemed to be underhyped given how important it seems to me to be that you can just put a million tokens into context. There's apparently some other news that got pushed to the front for some reason. But yeah, tell me about how you see the future of long context links and what that implies for these models.
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