The Exchanges

Q And what did you work on specifically at OpenAI?

A Well, so the goal was we need to come up with some productionization of GPT-IV. So we, OpenAI had GPT-IV. It was pre-trained, and there were some, like, um, post trains on it. And there's questions about, like, how do we turn this incredibly powerful model into products? And we're all spitballing ideas, like writing bot, uh, coding bot. You know, very natural at the time. Some of our least interesting ideas were a meeting bot, so it would just sit in a Google Meet, take notes, and then send out, like, to-dos after. But John Schulman was very opinionated. He's like, we think we should keep it very general. Let's do a chat bot. And that became a large part of the effort, um, for those few months.

AI assessment note: “we need to come up with some productionization of GPT-IV”

Q How did that lead you to materials and atoms and, you know, the physical world again? I know that was sort of your starting point in terms of academics, but what brought you back given how much is being transformed right now through language?

A I think just the inevitability of connecting these systems to the physical world. The opinion that I and others held as periodic was, you're not going to see the same kind of acceleration in science and technology unless you start connecting these things to the physical world. Science ultimately isn't sitting in a room thinking really hard. Um, you have to conduct experiments. You have to learn from them. You have to interface with reality. And the creation of ChatGPT in late, um, was a, you know, Important technology, but it's still far too weak. Like we couldn't have done periodic on technology of that era. I think over the next few years past that we saw ever improving models. Um, we saw reasoning. I think like test time inference became really important. Uh, that led to more reliable error correction, more reliable tool use. And we see like the rise of coding agents and other agents. And I think those were foundational technologies necessary To then connect these systems to the physical world. Like it was just not impossible, not possible with like the AI technology of.

AI assessment note: “I think just the inevitability of connecting these systems to the physical world.”

Q Oh, so interesting. Yeah, that's cool. And then from a architecture perspective, is there anything unique that you're doing or interesting? Or can you talk a little bit about how you're actually constructing some of these models on top?

A Yeah. So, uh, language models are incredibly powerful. It's a very natural interface. Uh, and so we continue to use these, um, but we think about them almost as like an orchestration layer. So that's sort of a, a co-pilot assistant, but also like a system that can direct, um, experiments. And it's almost, it's orchestrating other specialized models as well. So we do construct neural nets that, um, are specially designed for atomic systems where there's like some symmetry awareness, um, and those have much lower latency and they've been like fine tuned for that. And so basically you kind of think of this like orchestrating layer that can ingest literature. It can go through our experimental data. It can go through different modalities. But they can also use specialized neural nets as tools, as reward functions. So it's like an overall system.

AI assessment note: “we do construct neural nets that, um, are specially designed for atomic systems”

Q In other words, do you need something like pi or skills or something else to work in order for, uh, periodic to hit that escape velocity in terms of a closed loop system?

A No, but it's a huge accelerator. Um, the goal for periodic is to generate high quantity, high quality data, diverse data, and automation is assistance to that. So right now we employ people as well, and we have autonomous parts that are just, you know, very reliable. If you had a dexterous humanoid who could wander into an unstructured lab and make sense and follow instructions reliably, that would be a huge accelerator. Right now, the automation of physical systems is, requires a very careful design and it's slow, but I think with improvements in robotics, that's just going to accelerate this. But already the reliability of the sort of like hybrid systems is sufficient to produce Huge amounts of, um, reliable data, but it's just gonna accelerate us forever.

AI assessment note: “No, but it's a huge accelerator.”

Q And the same seems to be true for material sciences. So how do you think about where you're going to commercialize this first or who you're going to work with or are there specific domains of products that you're working on first?

A So we've begun working very closely with scientists. Um, we've treated periodic as our customer zero and seeing how can we transform how this field of science is done, but there's huge opportunities across all of these industries, all these enterprises that are interfacing with the physical world. People who are bottlenecked by materials engineering and process engineering. And again, those are kind of this like the same natural interfaces where engineers are asking questions about their data. They're trying to find aberrations. They're trying to debug machinery. They're trying to get to a better formulation. It's actually a quite universal thing as well. And so we've kind of created our little testing ground internally. And now we're sufficiently excited about the tech we've been building and to see this acceleration for advanced manufacturing more broadly.

AI assessment note: “we've treated periodic as our customer zero”

Q pipes into everybody's homes and they all have three D printers and you download blueprints and it just creates whatever you need in the physical world. And some people start evolving different nanobots to do different things. It's this very advanced kind of AI plus materials kind of future world. Um, what is your vision or conception of what our world looks like in 10 years, assuming periodic is successful?

A Well, I mean, I think As you're pointing out, you're going from systems that aren't just writing essays, not just writing software, but to literally generating matter. And I think it has pretty profound implications to semiconductors, airspace, energy. And I think it's incredibly important for, can we increase like the pace of just like the physical development of the world? I mean, we see how quickly the digital Realm is changing. Um, software engineering now looks wildly different than even six months ago. Um, but I think we see like, you know, similar opportunities in the physical world. Of course, like atoms are hard and so you will have, um, some limits of physics, but just because atoms are hard doesn't mean there's not an order of magnitude or two to speed up. Um, just making sense of huge amounts of data and getting to solutions more quickly. Um, Yeah, so I think what we're trying to do is give humanity this agency for atomic rearrangement, um, synthesis, and we think it's going to just be a huge accelerator. So, I mean, if our physical world could keep up at some fraction to our digital world, I think life will just feel dramatically different.

AI assessment note: “give humanity this agency for atomic rearrangement, um, synthesis, and we think it's going”