YouSaid · the spoken record

Eric Zelikman

lines on the record: 47
first: 2025-10-09
most recent: 2025-10-09
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“One thing that I think is actually probably a good thing that my previous company did is thinking of everyone kind of to some extent as like engineers, I think I'm looking for really strong info folks who can build stuff. I'm looking for really strong researchers who can build stuff. I'm looking for really strong product folks who can build stuff. I'm looking for people who have thought a lot about users who have thought a lot about memory on the research side. I'm looking for on the infra side for people who've thought a lot about distributed systems, really fast inference, people who've been there to scale really big projects up on the product side. I think people who are like, you know, really creative about new modes of interaction, people who have who really deeply care about building beautiful, tasteful products.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Everyone kind of has things that they're passionate about and given the opportunity. I think Like people can do like really cool things. I think the role of the model should be to allow people to do those really cool things that everyone kind of wants to do and accomplish those things that everyone kind of wants to accomplish. And I think like, you know, we shouldn't outsource all of the thinking and all of the, you know, everything to these AI overlords or whatever. I think what we really want are models that are able to empower us.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, I think, and you know, it's something that I think as a field, we will probably get better at. I'm not going to pretend that I'm going to one-shot this problem, but I think even any serious effort gets you quite a long way”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I think to a certain extent it's probably a little bit true. It's not easy to build these really good molds of people. But I do think that the task for the model needs to be that it should be trying to do that. Like the model needs to actually be like trying to learn all these, like trying to learn about you, trying to learn about the things that you care about. The actual objective of the model needs to be to kind of understand you. probably won't be perfect like but boy you know like you can be a blob better than the current models like uh that seems”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Would say that memory is definitely like a feature that has been under invested in. By the field. But I would say that it is kind of difficult to invest in memory in this very task-centric regime. Because if you have a bunch of these independent tasks, the amount of information that each of those needs from other things that you've discussed is not all that high. Because of the current paradigm, memory doesn't end up being super useful in the training. And so these models are not particularly good at doing it.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Someone you basically like remember your name and like, you know, maybe what you do and like just like the really high level sketch of your life, like it would be, that friendship probably would not last very long. Yeah, I think that's kind of what the current models are.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“The model knows that you're going to some wedding, for example. And then you ask it about booking hotels in Paris. It might consider, oh, hey, around the time of this event, I know that this user has all these things that are true about them. A model that's generally able to kind of think about how everything that you say fits into your understanding of that person would just be like, I think a very fundamentally different interaction. Because right now, if you want to ask a question like that, you kind of have to dump all of this context in. You have to tell like, oh, you know, can you help me find a hotel in Paris? This is because I'm going to a wedding. I have these constraints. I have like these people who need to be with me. I have it needs to do this. It needs to be, you know, you have.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Years, but yeah, I think I think you get a lot of behaviors that you currently don't really see in these models. I think you have models that are much better at understanding how the things that you say and ask fit in to the overall context of the stuff that you're doing.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I think it's a really fundamental question. I think there is actually some good academic work that has started to explore some of this. Yeah, there's some work recently around RL from human interaction. There's a cool paper called a Collab LLM that trains against simulation. There's a lot of very cool work kind of starting to explore this in academia. But in general, I would say there's a lot less attention being paid to this kind of stuff in industry because I would say for most labs, and maybe this is a strong statement, but I would say for most labs, the human is kind of, you know, the intermediate until you have this fully automated system. And so spending a lot of time optimizing things for being really good at understanding and really good at interacting and really good at collaborating with things is kind of like almost like an intermediate.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“And they might realize that it was not actually a good physics idea. Having a model that can kind of roll out the long term implications of the things we're trying to”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Harmful effects that you get if you think about things in this very single task or like task-centric way. But if you have models that actually consider the long-term implications of, oh, hey, if I tell this person to start a company that sells gloves for catching ice cream, if I tell them that that sounds like a good business idea, they might actually go and they might actually build that business.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“And you basically think of it as like, okay, you had this interaction, you're done, you need to make sure that this one response has all of the plausible answers, has all of the possible content. You don't ever ask questions. You don't ever try to clarify things. You don't really tend to express uncertainty. You don't tend to be proactive. You don't tend to think about the long-term. You see a lot of, like even single-term side effects of this kind of regime. And most of them are treated as kind of their own problems to solve. You see issues around that people highlight around sycophancy. You see issues that, you know, there was recent news around the psychosis stuff. There's a lot of these like...”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“The most fundamental thing is that the models kind of don't understand the long term implications of the things that they do and say. When you treat every turn of a conversation as kind of its own game,”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Relate to some like immediate thing means that you can kind of say, Oh, yeah, this team did like 2% better than this team. So they deserve like all of the resources or, you know, this team like improved the benchmark by like 10% while this team improved it by 5%. You know, let's allocate accordingly. And I think in general, that's part of it. I think another part of it is kind of more aligned with the easiest ways to train these models. It's not easy to have our own environments and stuff. You have lots of these companies popping up, obviously, that are trying to sell environments to different people.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Even the ones that are like. There's very few benchworks out there that actually try to consider what if you actually have a person that's interacting with this model. Like, you know, at best you have some multi-turn benchmarks that try to simulate what an environment would respond in different inputs. But even that is still far from considering, hey, if you actually have this model, then interacts with the person for some amount of time. How does it actually affect that person's life? It's really remarkable that the field is kind of like so stuck in this kind of task-centric regime. And I think, but it makes a lot of sense. One thing that I was told by some folks at Google is that one of the reasons is that it's actually very useful for credit assignment. So being able to have these benchmarks that are very easy to quantify and very easy to like”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Like that fundamentally these bones don't really understand people, they don't understand people's goals. I would say part of it is like the general kind of training paradigm that the field is in. It's very, I would say, single task focused or task-centric.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“And by simulating students, you can actually design better tests for those students. And that was like a really cool finding. Like, hey, if you have models that are really good at modeling people, you can actually design systems that are better for people. And this was something that I found really cool. And kind of as we move towards the current kind of capabilities frontier, it became more and more obvious that we have these incredibly smart models that are capable of so much, but they're not used for anywhere near what they're capable of. Like the role that they play in people's lives is a lot less deep, a lot less positive than it could be. And I spent a lot of time thinking about like, okay, why is that? Like, why are these models not more, like I said, deeply positively integrating people's lives? And it seemed like a really big part of it.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I guess I've been thinking about this kind of stuff for some time now. Even back in my PhD, I think one of my less well-known works was actually about, we show that you can train language malls to simulate different kinds of students. Right. Yeah, y”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I'm sure that many of the researchers in the field disagree, I guess, in the long run, we'll see kind of what plays out. But I personally strongly believe that we're much more likely to solve a lot of these fundamental human problems by working by building models that are really good at collaborating with large groups of people, that are really good at understanding different people's goals, different people's ambitions, different people's values, understanding different people's weaknesses, and how to kind of coordinate with these large groups of people to make everyone more effective. And I think the vision of this AI that goes off on its own for 20 hours does its own thing and kind of like, you know, comes back with the answer to life, the universe, and everything. I think that this is like. Less likely.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, no, I'd say so. I think it's like. When I say that, you know, I'd like to work on models that empower people instead of replacing them. People are like, oh yeah, sure. But I'd rather like, you know, work on curing cancer or something. Obviously, that's a really important goal, right? Building models that are able to kind of solve humanity's most difficult and most fundamental problems is incredibly important. But I also think that like.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“And I think in general, if the purpose of these models is to kind of replace the person for this chunk of work, you end up with a lot less, I think, real innovation on kind of what's possible. Yeah, I think if you actually have models that really understand what people's goals are and really empower them more, you end up in a very different situation.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, I think it's probably some combination. I think another thing that I kind of think about is like, you know, the most natural thing to do as you kind of automate away the existing set of tasks is, you know, you kind of look at the world GDP, you like carve out the parts that are most easy to replace with these models. And that's kind of the things that you target. Like, oh, wow, you know, coding is like an X billion dollar market. Let's automate all of that. Or this other segment is like an X billion dollar market. Let's automate all of that. But I actually think if you kind of empower people, if you have models that really understand what people are trying to accomplish and really support them in accomplishing those things, you have the potential to actually grow that pie instead of basically replacing all of those segments.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Me. Yeah, it's just like you make these PRs and they're like a hundred thousand lines of like, you know, like. I think in general, this is kind of going to be. I love the”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Questions of like what do those numbers actually mean and how should we take them at face value? But regardless, this kind of in the metric that people are looking at more and more to measure progress. But as we kind of get these models that increasingly, you know, Remove people from the interaction, you end up with basically people having less say in kind of the things that get built. You end up with like, you know, I think if you have a model that goes off and does its own thing for like eight hours and comes back to you with something that is somewhat there, I think this is a weird regime where people probably feel less real agency over the things that they're building. And I think also I kind of anticipate that people will feel like they don't really understand the things that are being built.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I'd say that the main thing is just that, like, As you kind of have these models that expand in terms of the horizon that they're automating, you have these malls, the recent or recent-ish IMO results are like a kind of a good example of this. You have these models that go on for hours of reasoning without any kind of human intervention. And this has kind of been an increasing measure of success, I would say, for these labs. So, for example, there's this METR meter. Like a benchmark that everyone likes to share whenever there's a new model. And it's like, oh, we went from being able to have these models work for complete two hour tasks autonomously without human surveys to 2.5 hour tasks without human intervention. And obviously there's like.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“There's still meaningful dimensions of scaling that haven't been, I think, fully explored in terms of IQ. I think there's a lot of cool efforts out there. There's a lot of cool stuff that can still be done on the capabilities access. I do think that one, as you start thinking about some of these new kind of axes of scaling, it's actually very natural to realize that there are ways to do them in ways that Incorporate people, and there's ways to do them in ways that kind of leave people out more and more. And being very mindful of, oh, hey, I'm designing this new algorithm. And it's going to scale IQ of this model by X amount. If you effectively keep people to effectively keep people in the loop is actually a very active decision. And so I think in general, if you're thinking about these things, that's important”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Back, right? And also thing that matters a lot is just like how verifiable are the things that you're trying to get them all to do. I mean, obviously there's been, you know, a ton of work out there on making models less dependent on verifiable words. Lots of cool published papers. I believe most people would say that there's still a gap between how well these models perform on verifiable tasks versus non-verifiable tasks.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Which is kind of this longer running background thing versus cursor, which is more interactive. You have a bit more luxury with those more background approaches to tackle harder problems, I'd say. Yeah, I think it's a tricky question. A lot of things depend on how far the distribution of what you're asking is from the distribution that the models were actually trained with. So, you know, if you happen to be asking a problem that's very similar to the kind of problems that it's seen before, then it'll do great. And if you're asking a problem that's very out of domain, so like to some extent, this question is kind of hard to answer concretely. Unless you know, like, basically what the RL data for a lot of these specific tasks is.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Part of it is there's, I think, a balance. When people kind of want to give users these models, it's actually important that they're not annoyingly slow. And so I think there's actually a number of problems where if you gave the models more time, they would actually be able to answer. Better. But for example, in the kind of coding context, you kind of have to be reasonably responsive. At least it depends on the kind of setup, right? Like if you look at products like OpenAI's Codex.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I guess one thing that I think is really important to keep in mind is that the more kind of context you can give the current generation of models, the better you kind of are, the better off you are. Their answers are super sensitive to whatever additional information you can give them. Yeah, I think this is like a really important thing. I would generally say existing models are particularly good at handling questions that are like Easy to answer and kind of like a closed form. Like if there's like a simple numerical answer to what you're asking or like a simple way of choosing from a set of things, this is something that these models actually like, obviously it's all dependent, but this is something that makes it easier for them all. If you can imagine it being easy to check your answer Actually, I think makes it easier for the models.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“That assumption doesn't hold. And this turns out to be like a bunch of those kinds of problems. So I think it's like they're pretty smart, but also they're more, I think, tripped up by some of these tricky things. But also they don't really, I think one of the core things is that they're not smart emotionally or like they're not smart on the level of actually understanding kind of what people care about or kind of like how to actually help people accomplish the things that they care about.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Anybody who isn't looking at this. Yeah, so looking at these humidities last exam questions, I kind of One kind of category that is actually quite big are these trick questions that require basically people like if you're familiar with it, you'll be like, oh, they're trying to get you to assume something. But actually, if you think more carefully about this problem.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Also, a lot of them were like, you know, one interesting category of these I spend a lot of time looking at the HLE questions. One interesting category of them. Sorry.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I think it's hard to compare directly because it's very jagged. It's true that some of these, for example, some of the HLE questions that these models are able to solve are genuinely things that are non-trivial for actual PhD researchers. I'm not saying they're open problems or anything, but they are pretty non-trivial.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I guess, in terms of IQ stuff, I'd say there's a lot of, and if you're able to pose the problem very well, like some very advanced physics problem or math problem, I would say they're reasonably smart. I think a lot of the failures that people see”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Showing that it's really valuable for us to have a baseline where you like, you know, the harder for harder problems, you learn more for easier problems. You don't learn quite as much. And I think that there were a bunch of nuggets in there that even at the time, I don't think I fully thought of as like, oh, wow, that's actually a cool improvement over the original thing.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“These arbitrary kind of like chunks of text, for example, and he tries to predict what's going to come next, which is the standard language modeling objective. Can you actually get models that more generally learn to reason? One of the kind of cooler things that I think is kind of overlooked about the Rational Quiet Star paper is we showed a bunch of kind of key improvements to the star paper that were necessary to actually do this kind of thing. So that was, for example, showing that it's really valuable for this algorithm to be online.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“So Quiet Star was kind of the last thing that I did back at Stanford. And it was really fun. I guess we showed a few things that were kind of cool. One of the main goals of that paper was to show that you could actually scale this up to pre-training skill by using basically pre-training style data. I guess now there's a bunch of these works that have come out recently around RO pre-training and stuff like that. And that's, I guess in some ways similar to some of what we showed in the quiet star work. Instead of having question answer, if you actually just have, um,”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“So, if you only train on the positive examples, then you end up in this kind of potential minimum where there's just no more data that it can actually solve. And so back then we were like, what if we just show it the problems I didn't solve and try to teach it from those? But I guess another thing that other work has done since then is, oh, what if you just sample a lot? And that also seems to work in those works.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I think I was generally interested in, yeah, I think there were a few things though. There was one part of it that we introduced to kind of, we observed that there was a bunch of the data that the mall wasn't learning from. And so we proposed another variant of this, where we actually were like, oh, what if you actually take the ones where it fails and you basically ask it to reason about why it should have gotten it right and then you train as if it got it right And this version was kind of a way of extending beyond the kind of the parts of the data that it couldn't see.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“And one of the really interesting things for me was that as you actually trained for more and more iterations, the number of digits that it was actually able to do kept increasing. And I think that this was like one of those big surprises for me. Like, oh, wow, there's no obvious plateau here.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“There was one experiment that I remember doing, though this was quite a while ago at this point, but we looked at the, I think it was like n digit, like addition or multiplication, sorry, it's been a second. And one thing that was really interesting was that this back then, this was like a task that was considered hard for language models.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“Guess the intuition is if you have a model and it's able to solve these slightly harder questions by thinking about them, then what if you actually teach it? Like, hey, this solution that you came up with, that got you to the right answer. Good job. Or, you know, if you, or if the model didn't, then you basically don't reward it. I guess the original version of SAR actually had, or yeah, there weren't a baseline at the time. We compared it to reinforce, which is this popular algorithm in, I guess, reinforcement learning, like very simple policy gradient thing. But yeah, I guess, you know, at the time, it was a very simple algorithm, just, you know, you iteratively generate solutions. If the solutions get you to the right answer, you learn from them. If they don't, you don't. And then you just kind of keep doing this as the model solves harder and harder problems.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I think for me, when I looked at AI or language models back in 2021 or whatever, I was like, these things aren't very smart. They can't do that much. And there was some early work around there that showed that, for example, you could use chain of thought to get models to answer. More smartly, but it was still like only a small step improvement at that time. Like there was still the benefit of that was as much as you can really get with just prompting. And so back then I was like thinking about, okay, how do you actually make them half decent at actually solving these harder problems?”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I guess at the very beginning, I was just like when I was choosing Ris Frods, I was just interested in how do you actually make these things half decent.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I mean, the thing I've always been excited about is how do you actually build this technology that frees people up to kind of do the things that they are passionate about? Like, how do you basically Allow people to actually focus on those things. Originally, I thought of automation as kind of like the most natural way of doing that. You automate away the parts that people kind of don't want to do. And that, you know. Freeze up people to do the things that they do want to do. But I guess I realized increasingly that that's like, it's actually 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.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT
“I guess going back really far, I've been motivated by this question, all of these people out there who have all of these things that they're really talented in, all of these things that people are really passionate about. Like you have so much, like, you know, there's just so much talent out there. And I've always been a little bit disappointed that so much of that talent doesn't get used just because everyone has 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.”
2025-10-09 · No Priors · Humans&: Bridging IQ and EQ in Machine Learning with Eric Zelikman · IDENTIFIED FROM THE TRANSCRIPT