Unsupervised Learning with Jacob Effron
00:03:59 · Diogo, Jacob Effron
Like, I don't think a computer really ever wants a string except when that string happens to be an extreme cardinality enum, which is a very valid use case. But it's very rare that that is the thing that computers want because they operate on bits. And when I just ask myself or anyone this question, even the most string pilled of people will give many nines of, like, percent for the machine native component. It's just a question of when that will happen. And to me, it was always like, okay. What does that thing look like? Let's work backwards from that cognitive core and make it work. And I will say, Jev is not yet there. There's still so much more work to do to make it so much better. And that's that's what really drives us all. [00:04:40] S0: Is Jev not yet there because, I mean, you know, today, the shape of of, like, you know, calls and tokens actually is more for humans than it is for for computers themselves? How much of it is is just you're also waiting for the use cases themselves to evolve, and how much of it is, you know, areas you wanna push on the model?
00:04:57 · Diogo, Jacob Effron
[00:04:57] S1: I I think it's a bit of both. It's it's like it's like this interactive dance, and this is also why we released, like, these dreamer ready features, like the things that we are... You know, like, wouldn't recommend for the most stable of use cases because we really want to be honest about our model's properties. So we've released a lot of these things because the reality is that the people who best know how much reliability, you know, robustness, like intelligence, is needed for their downstream application is not us. It's the developers themselves. Because developers are the expert at this, and they will know way better than anyone on our team. We have smart people, but they will know better what reliability profile they need as long as we give them the right knobs, which string models don't tend to do. So there's, like, an entire spectrum of this. I think that they need to coevolve together. The reason why we launched was not like, okay. This is, like, a good moment in AI. Everyone told us not to launch then, just as FYI. It was that we felt like, hey. This is getting to be something we're really proud of. Like, it's kind of, like, impressively smart at a lot of things if you prompt it right, And we are ready to, like, keep pushing that limits... That... Those limits with the rest of the field to help us coevolve use cases, figure out weaknesses, deal with jaggedness. Like, there's so much we do to focus on jaggedness and insufficiency and intelligence that having people play with it would be so helpful. [00:06:13] S0: Why'd folks tell you not to launch?
00:06:13 · Jacob Effron, Diogo
[00:06:13] S0: Why'd folks tell you not to launch? [00:06:15] S1: We launched around the time of the pacing the frontier discussion, and people were like, hey. Like, it's kind of, like, a bad vibe to, like, launch a capabilities product during this. Like, everyone's, like, really doom and gloom about AI, and it's a kind of a bad vibe. And, like, I don't know if you guys should do this. And we're just like, that discussion never really made sense in the first place, or it is, at best, incomplete, in my opinion. [00:06:39] S0: You talk about kind of, you know, wanting to push in in so many directions. I mean, I think one thing, you know, that you that you guys do is you're always kind of transparent about the things the models can't do, you know, the areas where they're not as strong. How much of that is, like, the road map for what you're pushing on? Like, you know, how much of it is is other areas? Like, you know, how do you think about, like, where where you want, the company to to push in the next month or two? [00:06:58] S1: Month or two. Oh, that's a that's a [00:07:00] S0: I mean, AI world, don't know if I can ask, I don't know if I can ask longer than that. Right?
00:07:00 · Jacob Effron, Diogo
[00:07:00] S0: I mean, AI world, don't know if I can ask, I don't know if I can ask longer than that. Right? [00:07:04] S1: I wouldn't say jaggedness is purely, like, a... Our road map. There are some things that models truly are weak at. And, like, this is where, like, that taste stuff comes in. Right? Like, what I was talking about, like, the reality of what you can extract from these models. The reason we call them system one models and among other things... And system one, actually, people did not want us to name that it either because system one thinking tends to be a bad thing. Like, people use that as kind of, like, an insult. Right? [00:07:29] S0: Yeah. It's like the the part that just naturally happened from evolution, you know, from back in the day before Exactly. Instinctual yelp, which makes mistakes, etcetera. Brains. Yeah.
00:07:29 · Jacob Effron, Diogo
[00:07:29] S0: Yeah. It's like the the part that just naturally happened from evolution, you know, from back in the day before Exactly. Instinctual yelp, which makes mistakes, etcetera. Brains. Yeah. [00:07:37] S1: Exactly. But... And, like, people often, like, say that that is the mistake making side. And I believe that the shape that actually AI gets compressed to happens to be, like, very system one, and therefore, the honest thing to do is to communicate its weaknesses. Because I think that on its own can be incredibly, incredibly valuable for software, and most of what people need actually is system one stuff. You know? Like, why can we solve Millennium Prize problems in math but not automate anything touching accounting? Like, that is... That should be really weird to a lot of people. And, like, I think that that is, like, still our north star even if people get distracted by, like, cost, speed. You know? Like, oh, it's it's it's good for, you know, like, really fast decisions or whatever else. Like like, I see these as, like, distractions compared to what is really needed for AI to meet its potential, and that is my my north star. [00:08:29] S0: And so, obviously, the... You put the models out there, and you have a lot of people using them in different ways. How much does, like, the the real world usage of the models come and and feed back into the road map versus just, like, the stuff you were... You're all planning to do?
00:08:39 · Diogo
[00:08:39] S1: I would say it's almost none real world feedback. I think people should be complaining to us so much more. I think that, like, this... The field is still in a Stockholm syndrome of, like, oh, yeah. The model providers just do stuff in their one track path, and we happen to be, like, the victim is the wrong word. But, like, you know, like, in, what's that movie? Wall e. You know, like, they're, like... They're just like the people in the chairs that are just getting fed the AI. And I think this is, like, the state of things right now where, like, people plan around AI changing, but they don't have a sense of agency of where to take the field. And I think that this is not the way it should go. That's actually really disempowering and kind of lame in my opinion. And, also, I think that there's so much more we could do collaboratively as a field. And I think that the field could, like, push us a lot more to say, like, hey. We want to be doing these things. We think it is within the realm of capabilities. Let's figure out how to do it together. But not just putting stuff into the model, but figure out how to prompt them, figure out how to, unearth capabilities. Let's work together for these sci fi use cases because I think that is the way the future is gonna go. And I would want so much more of that. My my joke internally is that the number one way to get me to respond to something is to talk about model jaggedness because you have, like, very high up people say, like, hey. Can we meet with Diogo? And I'm just like, no. Can you meet with my CTO or GTM head instead? But if they complain about model intelligence, I am, like, on that and, like, debugging that myself for better or for worse because that, I think, is the thing that will really move the needle for us long term. So right now, we mostly have to go with our old road map because we we are not... I I think people are still in that mode of, like, yes. Let's just do whatever the model providers let us do instead of, like, let's collaborate on this, like, insane future together. And, actually, there's another aspect of that. I think people have a deep distrust of model providers too because they train on their data. And, you know, like, this whole, like, discussion of, like, can GPT wrappers even exist, I think, is very disempowering too because the whole application ends up becoming a chatbot.