Entrepreneurial Thought Leaders (ETL)
00:43:34 · S6, Carina Hong, Ravi Belani
[00:43:34] S6: Yeah. To give you some context behind my question, I work with a group named... Called the assumption of Physics, and they bring together researchers from all around the world to try to figure out what is the minimum set of physical assumptions that you can find that re... That gives you physics. And this also implies that we making physics mathematically consistent. And since Axiom is, you know, is interested in math, is Axiom also looking towards physics? Because physics is a field where the interpretation of the physics also really matters, and you also need to back up those interpretations with predictions. So is this something that Axiom is looking towards? [00:44:13] S2: Yeah. This is very interesting. So we have a few collaboration projects with applied, like, groups. We haven't... We actually maintain... We started to maintain mass econlib. It's a econometrics library. I believe we have a quantum computing partnership with like another like quantum computing academia group. I believe we have theory of deep learning. Just physics, I think there is a Physics Lib, which is the open source communities library for physics. So we haven't been sort of like navigating that space specifically. I am intrigued by the sort of you described the first order logic way of looking at the minimal set and like what can be built, right, like on top of it in the sort of lean dependency tree fashion, but we should definitely talk afterwards. Yeah. [00:44:57] S1: That's great. Thank you. Last question.
00:44:57 · Ravi Belani, Carina Hong
[00:44:57] S1: That's great. Thank you. Last question. [00:45:00] S2: Hi. I'm Sanvi. Great to meet you. This is just a more minute question, but you mentioned that the future of agents is different than sort of the hard code harnesses we've been seeing, but I was wondering if you could double tap on that and more describe what you're talking about. Yeah, we need trainable harness, right? So now I think there are a few labs that have the ability to do agentic RL for the model. And so the model will be able to have access to all the tools, all the skills, even call itself for smaller checkpoints of itself, right? And I think this is going to be the 2026 new award, or we might have a 2027 new award, and I don't know what that's gonna look like. I think swarm is pretty much up there. Without swarm, we will not be able to get to what we have done. And there's a question of optimizing the cost of swarm, which is going to be the next frontier. And part of optimizing the cost of swarm is you need like very good small models. And because of that, I think the sort of small labs training, genetic RL model story will not end because you probably need a larger one than a smaller one. Easier. It's hopefully easier to train. Thanks. [00:46:07] S1: Well, everybody join me in thanking Carina for sharing so profusely even though... Sorry about that. Especially grateful given this is a day of resilience of of of serendipitous trials. So we appreciate it, Carina. Thank you, everybody. We'll see you next week with Celine from Loyal.
00:46:07 · Ravi Belani, S0
[00:46:07] S1: Well, everybody join me in thanking Carina for sharing so profusely even though... Sorry about that. Especially grateful given this is a day of resilience of of of serendipitous trials. So we appreciate it, Carina. Thank you, everybody. We'll see you next week with Celine from Loyal. [00:46:25] S0: The Entrepreneurial Thought Leaders series is an original production of Stanford eCorner and the Stanford Technology Ventures Program. To learn more, please visit us at stvp.stanford.edu/ecorner.