Personal agents need discretion as well as coding ability

Transcript

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

1. 49:58

   I mean, I think that the best way to think about Meta is that we are an end-to-end technology company, right? So even when we were, you know, primarily just building social apps, you know, we were never just an app maker, right? I mean, we built, like, we built the data centers, we built the chips, we built the infrastructure, like all of this stuff was necessary in order to tune the end-to-end experience to be as good as it is.

2. 50:22

   I think that that's obviously going to be true here, too. And the most important part of the experience going forward is the model. And when you talk about being state-of-the-art, I think the reality is that this is a very multidimensional problem. And, I mean, so people publish all these benchmarks. Then you have a lot more benchmarks even internally. And there are different things that your model can be better and worse at that you can focus on.

3. 50:46

   And that basically contributes to its personality. and there are some capabilities that I think are pretty universal. Like the ability to code, I think, is very important because a lot of the things that you talk about, even with a personal agent, kind of reduce to that. Like the kind of MMA coaching visual pipeline, it is a coding project right at the end of the day, right? It's like it's writing code, I don't see the code, but it does that.

4. 51:10

   You know, it's like someone I gave it to in beta just mentioned to me, it's like, Okay, she had it make a little Jeopardy game for her friends that she could cast from her phone to play, and it's just, okay, that's code. So I think there's a bunch of stuff, both for Meta's own internal development across the company, for our own advancing of our research program, and as a core capability of what it needs to do.

5. 51:35

   It needs to be excellent at things like that. But then there are other things that I think a model that's going to focus on personal superintelligence needs to be the best at that maybe others don't care as much about. So, I mean, I'll give you one example. Discretion, right? So you're going to tell your Muse agent, it's going to know a bunch about you, and it's going to need to go out into the world and interact to get stuff done for you.

6. 51:59

   But not share certain stuff. stuff exactly yeah so like let's say you have some kind of allergy or sensitivity or um you know or you're pregnant and like okay fine so you're making a reservation somewhere you don't necessarily want to say like i'm pregnant but like but maybe you want a place that has good mocktails like i don't know whatever it is right it's like you kind of want to be able to achieve your goals without having to necessarily reveal a lot about yourself and it needs to know what is sensitive without having to, like, ask you a million questions.

7. 52:30

   So that's something you put into the training. That's a specific thing that we care about. And then there's all these reasons why, like, maybe, you know, if you're making Claude code, that's less important, right? Because you're, like, you're, you know, you're working on a coding project and you're working within a team and an enterprise. And, you know, theoretically, like, if you're within a company, everyone can kind of see the project.

8. 52:50

   So, like, you don't kind of have that need to be able to differentiate between what is sensitive and what's not. And so there's a lot of stuff like that, that I think are like pretty deep. And so then it's kind of like just how in order to build the best Instagram feed, you don't just build like the app, you build the app and the infrastructure and the machine learning research and the chips and like all the stuff.

9. 53:12

   I think similarly, if you want to build the best personal agent, I just think that there's no way that another company is just going to take something off the shelf and post-train it a little bit and be able to do something that is as good as if you designed it from the ground up and put all this data into pre-training to get the capabilities that you want. It's just like, it's not going to happen.

10. 53:34

    We're going to definitely, as this compounds over time, over several years, have models that are way more capable for those goals. But we're focused on that. We're also very focused on coding. We're very focused on kind of the recursive improvement, because that's going to be important to stay at the frontier. So there are a few areas that I'd say our research agenda is overlapping with the other labs.

11. 53:58

    And then there's a few areas where I think we will have a unique focus.