Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman

Hi, listeners. Welcome back to No Priors. Today, we're here with Eric Zeltman, previously of Stanford and XAI. We're gonna talk about the contributions he's made to research, reasoning, and scaling up RL, as well as his new company, Humansend. Eric, thank you so much for doing this. Thank you. You have had an amazing impact as a researcher, including starting from just your time at Stanford. I wanna hear about that, but first, background of how you got interested in machine learning at all. I I guess going back, like, really far, I've I've been motivated by this question of, like, you have, you know, all of these people out there of, like, all of these things that they're really talented in, all of these things that people are really passionate about. Like, you have, like, so much, like you know, there there's just so much talent out there. And I've always been, like, a little bit disappointed that, like, you know, like, so much of that talent doesn't get used just because everyone has, like, circumstances and, like, has, like, these, you know, situations where, you know, they can't actually pursue those things. And so for me, AI is All of humanity is not living up to their full potential. I mean And then you gotta do AI. I mean, it's the the thing I've always been excited about is, like, how do you actually build this technology that frees people up to kind of do the things that they are passionate about? Mhmm. Like, how do you basically, you know, yeah, allow people to actually focus on those things? You know, originally, I thought of automation as kind of, like, the most natural way of doing that. Like, you you automate away the parts that, like, people kind of don't want to do and that, you know, frees up people to do the things that they do want to do. But I guess I realized, like, increasingly that that's, like, it's actually, like, pretty complex. You actually have to understand. If you want to empower people to do what they want to do, you have to really understand what people actually want to do, and building systems that understand kind of people's goals and outcomes is actually really hard. Yeah. Did you have, like, this human centric perspective when you were choosing research problems to work on originally? I I guess, like, at the very beginning. I was just, like, when I was choosing research problems, I was just interested in, like, how do you actually make these things half decent? Okay. Like So it's more increased capability at all first. Yeah. I think I think for me, like, you know, when I looked at, like, AI, like, or, you know, language models back in, like, 2021 or whatever, You know? I was like, these things aren't very smart. They can't do that much. And and there there was some, like, early work around there, like, that …