Building an AI Physicist: ChatGPT Co-Creator’s Next Venture

26:01Yeah. I mean, it's like Doge said, it's like we, in order to do this, there are so many foundational pieces to solve. and our tactic is in order to actually get to this goal of AI scientist, you need to make contact, do the full loop somewhere. If you say you're doing this in just like very vague terms, you sort of just end up back on archive papers and textbooks. And so it's really important for us to do the loop, but then create this repeatable process. Like how do you go from subdomain to subdomain? And there's really interesting questions about how well do the ML systems generalize between these things?

26:38What is the generalization of a system between superconductivity data to magnetism data, for instance? And maybe that looks very different than its ability to generalize to fluid mechanics. And I think there's fundamental arguments to make there. But the goal is create this repeatable system, prove it, and then just go through the different domains that way. So I can see the argument for why cracking room temperature superconductivity from an experimental basis is extraordinarily valuable for humanity. But you guys are building a startup. And to use an analogy for why you need to have a clear medium-term path or short or medium-term path along the way to a North Star that is both commercially viable and net positive to society, society, what we've seen, for example, with other frontier labs that are working on automating white collar work or software knowledge work is that, you know, there's this North Star of an AI researcher.

27:40But that along the way, there were a bunch of sub goals and so on. But a concrete kind of application that opened up a ton of commercial value and benefits for users on the way to that AI researcher was the idea of AI programming, right? Software engineering has become probably the first major domain that's caused people to really update their priors about how useful AI models are beyond kind of consumer applications. And in terms of productivity, their impact has been extraordinary just in a few short months. So if the traditional Frontier Labs' Northstar was an AI researcher and the path along the way to get there was programming, AI programming, what is that for periodic?

28:24Basically, co-pilots for engineers, researchers in advanced industries. So maybe perhaps just being in Silicon Valley, we really think about like computer oriented work. Everything is digital. Everything is bits. But there's so many industries like we were kind of talking about a few like space, defense, semiconductors, where they're dealing with iteration of materials of physics. And that's part of their workflow. How are they designing these new technologies, these new devices? And in the absence of data, in the absence of good systems, they don't really have particularly good tools. That is our opportunity.

29:04And these are massive R &D budgets. So, yeah, while high temp superconductivity is a great North Star, we very much understand that technology and capital are intertwined. We're going to be able to maximally accelerate science if this is a wildly successful commercial entity. And to do so, we want to accelerate advanced manufacturing in all these different industries, become like an intelligence layer for all these teams to accelerate their workflow and start reducing their iteration time, get them to better solutions more quickly, accelerate their researchers and their engineers. Let's click a little bit deeper on that in practice, sort of a day in the life of a periodic team member where let's say half the team, this is roughly right, about half the team are ML scientists with machine learning backgrounds and the remaining half are physical scientists with physics or chemistry backgrounds.

29:58How do you start by uniting the cultures, right? How do you take somebody whose primary career so far in work has been experiments in a lab, in wet labs, doing physics and chemistry and give them an intuition for ML and vice versa? Because, you know, you guys are both physicists who then had the career trajectory where you also had the chance to be at Frontier AI Labs and were part of training systems that are now considered sort of landmark, hallmark machine learning systems like JackGPT, like GNOME. But for others who might be coming from one domain, how do you get the team to build an intuition for the other?

30:38Yeah, so this is a great question. I mean, I feel like it's actually crucial for us to make sure these teams work very closely with each other. So one of the things we're seeing is the physics and the chemists need to figure out how to teach the LLM how to reason about these things. Because I think the frontier AI labs have figured out how to train them on math and logic, but not yet on physics, chemistry. So one thing we're seeing that's been really, I think, productive is the physicists and chemists are thinking about what are the steps we should include in the mid-training, in the RL training that will teach the LLM how to reason correctly about quantum mechanics, how to reason correctly about these physical systems.

31:18Another one, of course, is the LLM researchers are learning quite a bit about the physics, the simulation tools, the goals. So they've been working together really well. We have weekly teaching sessions where the LLM researchers teach, you know, how the RL loops work, how the data cleaning works. And then the physicists and chemists are teaching about different aspects of the science, the history of science. That's also very important. So we feel like that's been going really well. And, you know, one way of looking at this is the things we have to teach the LLM to be able to discover, say, a superconductor includes being able to read the literature really well, like read all the papers, the textbooks, find the relevant parts, and then being able to run simulations, theoretical calculations, and then take action, run experiments.

32:03You know, we feel like this is quite similar to the physical R &D researchers in these companies. They have to read the literature, read maybe internal documents or external documents, and then run simulations, run theoretical calculations, and then actually attempt to think experimentally, learn from that. So we feel like all the progress we're making towards our internal superconductivity or physics goals actually is making our LLMs much better at serving our customers who are doing very similar workflows? Yeah, I think just culture, no stupid questions. You can ask just like the dumbest like physics question, the dumbest ML question.

32:42And I mean, there's a few faculty as part of our company and they're actually excellent teachers. So, I mean, these like learning sessions have been really fantastic. And another thing I noticed is computer scientists often think in terms of like APIs. So scientists will say something and they're always trying to map it. You're like, okay, well, what's the input? What's the output? What's the target? How do I map that back? And it's always just like this translation. And I think we also have built up, as part of the team, there's people like on these different edges. So like if you have a simplex of like, you know, pure ML, LLM, pure experimentalist, pure simulation, There's people who like kind of live in this inside as well.

33:25And so they've been like excellent bridges for translating between these different groups of people. So it's like active learning to like learn the other spaces, creating APIs and then these kind of bridge connector peoples. I think Doge being an excellent example of that. Is it a requirement for somebody who wants to join periodic to have to have an advanced degree in physics and chemistry? Absolutely not. One of the jokes we're making is who was the NBA player who was saying that I'm much closer to LeBron James than you are to me? We were saying the opposite of that two candidates because the amount that even our best physicist doesn't know about physics is much bigger than the amount that they know about physics.

34:07So for this new candidate, even if they have no background in physics, how much they have to learn about what we're trying to do is actually not that different than how much the best physicist has to learn. Because there's so much chemistry to learn, so much material science to learn. And I think this is one of the interesting aspects of science today. You know, in the past, in the 1800s, there were these physicists that could do so many different things at the frontier. Today, we've reached a point where our intellectual knowledge is so large that a leading thinker can usually only advance in one very specific field.

34:38And maybe this is actually holding us back because, say, to discover an amazing superconductor, as we keep going back to this example, you have to know so much about chemistry, physics, synthesis, characterization. And unfortunately, I don't think any human knows enough about all of these. So we have to collaborate. So I think our team is kind of like a small example of this, where we have, as Liam said, like a lot of different points in that simplex. And for any person, they have so much to learn, but that's true for basically every other scientist. So for example, I supposedly come from the physics side of it, but I've been learning so much more physics because we now have people from different areas of chemistry in the team, different areas of physics.

35:21And I think it's true for LLM researchers as well. I mean, they come in, there are aspects of LLM that they probably didn't know until they started working with other researchers in our team. So I think it's a great, and it's like a small example of what we're trying to do with the LLM because we're trying to teach this LLM all these different things that we're learning as researchers. It's like a really fun experience, I think, yeah. And what are you finding makes a great researcher at Periodic that's different from what might make a great researcher at OpenAI or Anthropic or DeepMind? I would say there's very high overlap.

35:54But probably one of the biggest determinants is, do you care about this mission? Is accelerating science, to you, is that like the big goal? And I think looking at the team right now, it's just incredibly mission-driven set of folks who are like, yeah, this is the North Star. Let's do that. If someone really wants to improve some Megacorp's products, yeah, you'd probably be better off at that Megacorp in iterating and improving their products. But if you care about scientific discovery, I think Periodic Labs is the best place to do that. How big is the team today? We're roughly 30, I believe. Yeah.

36:29And as you think about taking a lot of the research that's going on at the company and deploying that out in the real world, the kinds of customers that we've talked about, space, defense, advanced manufacturing, these are mission critical industries that are known for being essential to whatever part of the economy they're part of. but often they're not the most, they're not the fastest to adopt new technology. How do you think about deploying the kinds of frontier agents that we've talked about that are great at science, great at physics in companies or organizations that might not be anywhere close to as sophisticated as you are in AI or ML?

37:19Is there, do you have a working thesis for how to make sure that the arc of progress is not bottlenecked on deployment. It sounds like you have a fairly good thesis on how to unblock the arc of scientific progress on the research side. But when it comes to deployment, what might be a working theory that you guys are optimistic about that would help get the systems that periodic is building out into the real world? Well, maybe one thing that we've noticed in our conversations with all these companies is they all are looking for their AI strategy. They understand that the technology is shifting really quickly, and they're looking at how they're doing their work, and it's not changing as quickly as they think it should be.

38:01Some industries also are losing key expertise in different fields, and they're losing these senior engineers, senior researchers, and they're like, okay, how do we preserve that? But one thesis is understand, you know, kind of thinking about these like APIs and thinking about what are the evaluations? What are the biggest bottlenecks for these companies? Looking at some of the problems they face and we can map that to our systems and we say, well, we think we can dramatically accelerate this. And so it's not coming in and saying, hey, we're going to transform your fab line on day one. We're going to transform how you're doing everything.

38:38Forget everything. It's like, no, we're going to solve a really critical problem, well-scoped, very clear evaluations. You kind of co-draft that with them and just show them how powerful this technology can be when you optimize against the thing you care about. So nothing particularly surprising here, but sort of like a land and expand type method as you might expect. But really looking for who are the biggest promoters within that company and what are the biggest problems. make sure you're solving a very real thing for them and intersect that with where is our technical capability the highest.

39:15You know, you were on a call this morning with one of the customers in your pipeline. We don't need to name who, but what were some of the things you heard as the most urgent problems that they'd like for periodic to solve? So one of them was simulations. You know, they spend a lot of time training people on some of these simulations they need to use is critical for their development. and being able to automate those simulations, I think would be quite enabling. The design process and then kind of like some of the small things like matching the formats, being able to feed, you know, the simulation results into the design pipeline.

39:52All of these seem quite important. And then being able to treat the data together in the same place. What else? Well, I think there's a really fundamental question. So a lot of these companies will rely on retrieval. So that's sort of like a super lightweight thing. Someone shows up with a neural net and they're like, great, we'll just retrieve over all of your data. And then that's your solution. However, as we've seen with things like ChatDBT and other things, when you pre-train on the data, when you actually encode the knowledge into the weights, it's not just a retrieval system. You have a richer, deeper understanding of the material.

40:26And I think this is a big fundamental challenge. So for instance, for this customer, they can give privileges to their employees and have retrieval as acting on behalf, like the system acts as the user. And so you can match those same kind of like privileges for access. But if you start doing pre-training or mid-training on different parts, it's like, well, if you pre-train on every piece of data, that might only be accessible to say like the CEO of that company. then you have to figure out how do you sort of bucket that knowledge and create different types of systems um but i think right now like we're after talking with the user they don't seem to have a great solution for sort of distilling all of the knowledge um in into like a single model or into a set of models so like going you know going beyond retrieval to you know proper training And then I think also the supervised training they're doing is really akin to the early days of ChatGPT, where it's like input-output, you have a few examples.

41:33And kind of transforming this new way of thinking, which is like, no, high compute reinforcement learning is really effective. This is how you should think about the strategies it's using. This is how you create effective tool using towards those problems. And this is how you optimize it effectively. Could you describe for folks who may not be familiar with it, what you mean by mid-training? Because people are familiar with pre-training, they're familiar with post-training, but in the periodic context, what does mid-training mean? Yeah, sorry for the lingo. So I think this term came up years ago where it's like, well, we had pre-training, we had post-training, but sometimes you need to put in a little bit more knowledge.

42:10So before search worked really well, there was an issue of freshness. So we had pre-trained models and they have a knowledge cutoff. So there's like a scrape of the internet at that point, but users want more real-time knowledge. So it's like, how do you get that in there and enter mid-train? Mid-train is basically you're taking new data, new knowledge that's not in the model, and you continue pre-train. And this differs from standard post-training where post-training typically is more reinforcement learning, supervised learning. And the mechanism is basically, or the goal of it is just put a lot of knowledge into the model that doesn't exist before.

42:48So that's mid-training in a nutshell. And in the periodic context, does that mean essentially going and injecting a ton of custom sort of data from an experimental implementation in a particular customer, a particular industry? What are the lines, the atomic unit that you guys think of mid-training that will improve the capabilities of the models on problems that they're just terrible at today? I mean, it's all the knowledge. So it's like you can have very low-level descriptions of physical objects, like crystal structures, for instance. You can also have higher level semantic descriptions of like, well, this is how I made material XYZ.

43:36And trying to get all this data into the model is really valuable. So it's like simulation data, experimental data, none of this exists. And basically putting that knowledge into the model and making sure that these distributions are connected in some way. And what I mean by that is if you just sort of mix together distribution A, B, and C, there's no guarantee of generalization. What you want to hope to see from these systems is the inclusion of this other data set is improving performance on the other data sets. And so these are sort of just like machine learning techniques or machine learning problems to solve.

44:14But basically just make it an expert in physics and chemistry and where it was deficient before. Sure. You guys both know that I spent some time running evals on a bunch of these models at the Stanford Physics Lab earlier this year. And the results were that the models are terrible at scientific analysis. Because they weren't trained to do so. Because they weren't trained to do so. But on the other hand, you know, many of the existing research teams working on the general models are investing in trying to make these better. Is there something about the way you're building periodic that gets to draft off of all of that progress in the base models?

44:49Or do you have to start everything from scratch and therefore not be able to be composable with advancements happening in the mainline models today. Yeah. I mean, we benefit from all the different advances. So one of them is the LLMs are getting better. And we definitely benefit from that because we take a pre-trained model and then mid-train it, you know, high computer. Another one is the physical simulation tools are getting better. So DeepMind, Meta, Microsoft, academic groups. They're open sourcing new ways of simulating new ways of using machine learning to predict properties. So we get to basically utilize all of those.

45:25And it seems like machine learning has made such an impact in the physics and chemistry fields that we expect these improvements to continue. I think another thing is when we think about tools for agents, we think of like, here's a browser, here's a Python. But increasingly, people think about tools as other neural nets as other agents. And so if you look at a lot of physics code, it's not particularly deep. This isn't competition programming. This is kind of like hacky scripts. But you can rely on some of the best systems for wherever they spike on. So neural net as a tool to these agents is something that immediately accelerates our work.

46:12So you don't have to to like replicate every everything there's a historical pattern that a lot of the fundamental research in the physical sciences that uh we're talking about here physics chemistry biology has historically been done at university labs um is there a role at all that the university ecosystem you think will play in periodic's future or do you think these are just completely divergent paths absolutely i mean so much of the simulation tooling we use have been developed in academia. Many of it is in Europe, for example, a lot of the novel synthesis methods. So we definitely benefit from a lot of these different, very deep technical progress.

46:55Like for example, all the physical simulation tools are these, you know, complicated Fortran code that in our team, for example, we don't really like know how to develop very efficiently, but we feel like there's definitely a very deep connection between academia and industry labs. So for example, recently, a lot of the large scale simulations have been done in industry labs like Microsoft DeepMind and Meta, but a lot of those tools have been actually developed in academia and then passed on. So there's actually really nice synergy there. I think it added a few other things too. So like you found when you were evaluating models on their ability to do scientific analysis, they were deficient.

47:36This was probably, I mean, not a direct goal for those teams training those models. So I think academia and these collaborations say, well, help us inform, what are the important tasks? Like, how do you do this analysis? What skills do we want to put in the model? A skill could be a full analysis or a skill could be like a smaller primitive as part of a larger analysis. But also secondarily, it's how do you think? So one of the physicists was looking at the reasoning strategies of one of our modelers. He's like, no, it's all wrong. It's all wrong. And we're like, what do you mean? He's like, no, this should be thinking higher level.

48:13It should be thinking in terms of symmetries. This is the book that encodes the thinking strategies that will be more effective. And of course, your reinforcement learning environment needs to reward those types of strategies. But given some of the most premier scientists are using these strategies, they're likely effective. And these are types of things where it's like an industry-academic partnership can just be so powerful because industry just simply is blind to these types of analyses, these tools, as well as just this way of thinking. Yeah. And there's a way of connecting that to the tool in question as well, because language is very important, but then in the human brain, we also see all the visual processing, like geometric.

48:54So it's plausible that while these LLMs will keep getting better and better, they'll actually benefit from having a geometric reasoning that's separate. So today we can do that with equivalent graph neural networks. We can do it with diffusion models that are kind of geometric tools by construction. And the LM can call them so then it can have both the language aspect, which is very good for a synthesis recipe, but also the geometric aspects, which is very good for representing atoms, just design geometries in general. So how are you thinking about deepening periodic styles with academic labs?

49:27Yeah, this is very important for us. So we have two major initiatives in this direction. One of them is we're starting an advisory board. This will be kind of expertise spanning from superconductivity to solid state chemistry to physics. And we want to make sure, you know, we're in touch with this kind of long term research directions. A lot of important government funding goes to these groups and we want to have a tight coupling between what's important for them and us. So this, you know, includes superconductivity expertise such as ZX Chen from Stanford on the experimental side and Steve Kvelson from the theory side.

50:01We also have census expertise on the advisory board from Mercury Kanazidis from Northwestern University and Chris Wolverton on the high throughput DFT side. And then we have Kostya from Manchester University, who is really well known for discovering graphene. So he'll be able to advise us on these, you know, novel, exotic electronic states and materials. And our second initiative is going to be through a grant program. You know, we really want to enable some of this amazing work going on in academia. And some of the work isn't a good fit for industry. You know, it's best done in academia. So we want to kind of accept grant proposals and we want to enable and support the kind of work that's going to help community, especially in relation to LLMs, agents in synthesis, materials discovery, physics modeling.

50:53So, you know, maybe after this show, you can include the link. Yeah, we'll include them in the show notes if grants are open starting today. Absolutely. Great. So for people who might be interested in joining Periodic, what are you guys looking for? First off, someone deeply curious, someone who really wants to understand the machine learning, the science at a deeper level, who wants to make contact with reality, who wants to advance science. This has to be a driving thing. But also pragmatic. What we're trying to do is incredibly challenging. And someone who has very careful process and they get to their solution oriented, they get to goals quickly.

51:35And really, someone world class along some dimension. We're looking across all these different pillars. So machine learning, experimentalist, simulation. And people who can bring some sort of innovation on how do you create a creative ML system? How do you bring new types of tools or new types of thinking to some of these state-of-the-art models? Someone who can advance simulations and make it more robust and more reliable with experiment. Yeah, and maybe one more thing I'd add is Liam and I have been really looking for a sense of urgency in candidates because we want these technologies not in 10 years.

52:14We don't want these LMs to start improving science in 10 years, but we want them ASAP. So if the candidate feels like a sense of urgency for improving these physical systems, discovering these amazing materials, innovating on superconductivity, they would be a good fit. Yeah. If you match all these, please reach out. All right. Sounds like we got to amp up the speed, the scale of stuff happening at Periodic. And we'll put the career links in the show notes. Thanks for coming, guys. Thanks for listening to the A16Z podcast. If you enjoyed the episode, let us know by leaving a review at ratethispodcast.com slash A16Z.

52:49We've got more great conversations coming your way. See you next time. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com forward slash disclosures.

53:46Thank you.

54:13Thank you.