BRIDGE REALITY

“I think over the next few years, past that, we saw ever-improving models. We saw reasoning. I think test time inference became really important. That led to more reliable error correction, more reliable tool use, and we see 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. It was just not possible with the AI technology of 2022.”

“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. You have to conduct experiments. You have to learn from them. You have to interface with reality. And the creation of ChatGPT in late 2022 was an important technology, but it was still far too weak. We couldn't have done periodic on technology of that era.”

“I think it's a great way to think about the world. It's very principled, very hard-nosed scientists, very careful. And I don't know, I think it's such an incredible field. You have such high leverage in computer science, in AI. And so I think a lot of physicists were seeing that, particularly in like high-energy physics. After the discovery of the Higgs, I think a lot of high-energy physicists were sort of looking for what's next. Ultimately, it becomes bottlenecked on the new apparatus for pushing the next energy frontier.”

“One of the engineers on our team was looking at a reported material property. And it was just sort of extracted values from literature. It was really interesting to see the reported value spanned many orders of magnitude. And so you train an ML system on that and it's like, well, the best you can do is model this distribution, but you're no closer to like a ground truth. And that's where experimental data comes in, where you now have a grounding in this.”

“Really important, it's not just like a pool of data. It's this interactive closed loop system that is so powerful. Once you have the experimental data, you can look through it. You can look for aberrations. You can look for patterns. You can look for consistency with simulation data, with literature. And then that helps drive the next set of experiments. So it's not just a pool of data. It's this very active loop.”