Entrepreneurial Thought Leaders (ETL)
00:36:30 · Carina Hong, Ravi Belani, S3
[00:36:30] S2: Right? [00:36:30] S1: Yeah. Yeah. It's like the ancient Greek heroes. Your strength is your... It can be also a tragic flaw. Awesome. Thank you. We'll we'll do student driven questions. Great. And please go close to the mic so they can hear in the back. [00:36:40] S3: Yep. This is good. That's perfect. If if I understand correctly, just very concisely, and you can correct me if I'm wrong, Axiom Math is basically building mathematician with the broader vision of building recursively improving AI systems. Why why do you see mathematical reasoning specifically as a particularly, like, effective path towards achieving recursively improving AI systems? [00:37:06] S2: Yeah. I probably would say formal mathematics specifically, which is the term math into machine checkable code. And so then basically by turning into code, you solve more math. So that's code for math. You also have math for code. So I think it's probably right to think of us as a company that's very close to coding. So we will be using the formal mathematical reasoning engine, the same stack that we built to prove software and also hardware. So that's, at the beginning, I had this idea where solve math, you solve everything. A lot of, I think, devils are in the details and the right sort of framing, I would say, is that every single part of the stack you build is now reused to to build coding, which I think is going to change the world. [00:37:54] S1: Thank you for the question.
00:37:54 · Ravi Belani, S4, Carina Hong
[00:37:54] S1: Thank you for the question. [00:37:56] S4: Some people will say before starting a company, you should essentially join, like, a big lab or a big company and kind of gain that training experience before starting. So in your opinion, do you think that's still relevant or would you advise someone to just jump into the deep waters jump into deep waters and just go for it? [00:38:17] S2: I think there's a lot of value in starting out at a big lab. If it's a big lab that you think allows you to learn and see the frontier. I'm in this weird situation where I think academia actually was the frontier of this emerging field, AI for formal math. I was reading all these papers that are academic publications because I think big labs were not very interested in. Well, that kind of like a spontaneous thing for like a year or a couple of quarters, and then they just dropped it. So I think if you're working in, for example, a field that is well developed, like mature, probably frontier the Frontier... Frontier has Labs have the Frontier, and it's good to start out that way. That would be my suggestion. [00:38:56] S4: But so now with, like, AI advancing so quickly, do you think there's still time to join, a Frontier Lab or, like, learn as you essentially get your training in before you start or do you capitalize now and just start?
00:38:56 · S4, Carina Hong, S3, Will
[00:38:56] S4: But so now with, like, AI advancing so quickly, do you think there's still time to join, a Frontier Lab or, like, learn as you essentially get your training in before you start or do you capitalize now and just start? [00:39:09] S2: One thing is I think you can learn a lot in a very short amount of time. That's I think one thing it's unexpected. Like learning is also... Everything's being time compressed. Right? So you're learning, make that also like more time compressed at the general index of time being time compressed, I think it will be good. The other thing is when I say the first question is to choose a hard enough problem. When it's hard enough, then you give yourself a little bit more buffer to really like go through, I call the learning process of from pre rigorous to rigorous to post rigorous. [00:39:39] S4: Yeah. Thank you. [00:39:40] S3: Thank you. [00:39:42] S5: Thank you so much for being here. I'm Will. I'm a math major. I'm just wondering what you think the mathematician of the future, what their job will look like and whether, you know, I'll be able to apply the skills I'm learning right now in the future.
00:39:56 · Carina Hong
[00:39:56] S2: Yeah. So the skills that you're learning definitely I think will be applicable for the future. The general question, I have a lot of thoughts about math the human human math. So I kind of talked about it. It was a lot of mathematicians too, who see different parts of the spectrum. One, this is all decision tree. One, is math closer to cancer biology or is math closer to chess? I think one needs to kind of have their work view and make a decision on that. If it's like cancer biology, then it has intrinsic value, a problem being solved, a discovery being made, something being formulated as a conjecture. It has intrinsic value because after AI compress a timeline of landing to practical word, then you will basically have math be super useful to scientific applications, various applied scientific and technological engineering applications. So that's what I believe in. I believe it's closer to cancer biology than to chess, but some other people also do math for the fun of it, then to those people it's chess. And then my arguments subsequently will not apply. So if you're with me, that is closer to cancer biology, and AI will compress the timeline to make it seem more useful despite it already is. Cite Hardy's mathematician's apology. Then there's a question of, why do you need human understanding if you can get the drop down? And the most convincing argument I have heard right now is that if you can, for example, have human understanding that you can have paradigm shifting ideas that AI is not capable to come up with. And those human geniuses are going to, you know, discover methods like maintenance sieve for bond gap, for example, to push the entire field forward. And now the best counterargument of that is if you assume AI progress continues, which I think is a capital markets problem Okay. Not a technological problem
00:41:39 · Ravi Belani, Carina Hong
[00:41:39] S1: Yeah. [00:41:40] S2: Okay. If you assume AI progress continue, then it will be able to conjecture. It will be able to build mathematical theories. It will be able to even come up with those out of distribution paradigm shifting ideas. Then there is a sort of... Then the counter to that, which is that, okay, well, where are human mathematicians, as you say? And if you believe that it has intrinsic value, then mathematician will have more of like a dev workflow because coding is an example of something that has intrinsic market value. If you do not believe it, then then it's the chess route. Then I don't think it's going to apply. [00:42:14] S1: But if the capital markets fulfill the demand and AI continues... [00:42:18] S2: The worst case actually is... [00:42:20] S1: Our our our math majors will still be relevant? [00:42:23] S2: I think the worst case is capital markets crash, AI progress hinders. Not a lot of people are sort of, you know, invested in understanding math. And so there's less paradigm shifting ideas, and then we'll be in a sort of plateau situation. That will be the worst case. [00:42:40] S1: And in the best case, if if the markets continue. [00:42:43] S2: The best case continues and a lot more people are going to be mathematicians, developed... This defined as people involving mathematics as a significant or majority part of their workflow. [00:42:54] S1: And and and their need isn't gonna be isn't gonna be supplanted by AI machines in the future?
00:42:54 · Ravi Belani, Carina Hong, S3, S6, S4
[00:42:54] S1: And and and their need isn't gonna be isn't gonna be supplanted by AI machines in the future? [00:42:59] S2: I think if you assume really AI progress continue, then you would actually need more mathematical theories to derive specification, what to prove out of the data alongside with the code base will also develop. So now there's a question of, of course, like, time bridge. So it might be great, and then it might crash. And so that might be a problem. [00:43:27] S3: Thank you. Okay. Thank you. [00:43:31] S6: Hi. Can [00:43:33] S1: you go us... Go go closer? [00:43:34] S4: Oh, yeah. Yeah. [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?