A new AI lab can compete beyond verifiable code

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 13:37

   are you working under the assumption of? Like, it seems like if some people are right, if the people who are most bullish on timelines are right, then Anthropic or Opening Eye will just, like, do RSI and, like, rapidly improve to superintelligence within, like, a year or two years. Do you think in that case Flappy airplanes would have a chance to succeed i mean tons for what's worth super respect our our colleagues and friends at openanthropic they're shipping great models we use our models every day i i i think their success is broadly great for the success of the industry um i think there's absolutely a case for coexistence and i see ourselves as being a very complimentary asset because at the end of the day what we're bet on is actually that at the age of research is not dead that there is a lot of stuff left

2. 14:17

   to be discovered that is off the critical path for the capability stuff of these labs for example with coding it's actually this very narrow domain where you're it's easy to verify what's going on you can kind of just scale the thing and we've seen people scale a thing basically no one i know programs anymore myself included i love programming and i still don't program anymore and so what we see our alpha as being is is we do not have to focus on the same areas where everyone else is really having this dogged competition and driving cost down to near zero and really frankly doing the super intelligence in code or otherwise we see that oh there's all these places where actually no one's really making any progress the models are still not that great at creating these new research ideas they're still not great at interfacing in these

3. 14:50

   places where there's limited data the fact that data labellers like a job and a growing job even indicates that there's something something wrong right and it suggests to me that we will have a lot of room to operate in a separate space with a very different kind of tan yeah so what does ml research actually look like right now its models are so good at coding that you aren't writing any code anymore but also like you've described them throughout this whole talk as like being useless at real world tasks so i think again it's very spiky right they're very good at some real-world tasks and completely useless at others.

4. 15:21

   If I wanted to bring in that dead horse, can't do it. Impossible. You know, they can do backflips on stage with your unit tree or whatever because you have this program routine in that has a nice little PID or whatever. That's fine. You want to bring the dead horse they left outside? Nonsense. I can't even beat it. Like, I think that what it looks like using these models is actually taking advantage of places where they're superhuman and then really this almost like classical economic story of where's your comparative advantage? My comparative advantage is an idea generation. It's in actually deploying these models out in the world it's an it's about actually going and if scaling the things that need to scale and a lot of human things that frankly are quite hard to do at least we found in systems these models are still nowhere near

5. 16:00

   As one, Benjamin Spector, and two, the rest of our inference team, I think that we've been both surprised and disappointed by the models. There's some places where they're so spectacularly good for things in distribution, and then when you take them to slightly more novel things, like some of the things we're working on, which are really quite weird, it's a bit hard for the models to adapt as fast, and they might be more likely to make rookie mistakes and things of the sort.

6. 16:19

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