Everything Doge Chubuk said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Chubuk: AI cannot reason to breakthrough superconductors from training data alone
“I think it's still true that it would be difficult to just reason your way into a much better superconductor. I actually would guess that there's a law out there that we haven't discovered yet that says that you can't just look at your training set that's diff…”
Chubuk: LLMs already bridge solid-state chemistry and physics better than human specialists
“Like, there was probably a time when a physicist could contribute and be one of the best in the world on many fields of physics, but it's definitely not true today, and this is one of the reasons I think we are very excited about LLMs, because when you talk to…”
Chubuk: OpenAI o1 showed test-time compute improves results beyond training sets
“So what O-one showed is if you spend test time compute, you can get better results. So that was very exciting to me because there was one way of investing resources that was beyond the training set.”
Chubuk: AI is currently not better than humans at hypothesis generation
“It does seem like today there are things that ML, AI is better than humans, but one of those things is not hypothesis generation.”
Chubuk: GPU compute and training costs drove Periodic Labs' $300M seed
“We are going to train LLMs, we are going to use GPUs to run simulations, so that does end up being a large part of the cost. Yeah, it's funny, like, before, you know, if you asked me this question 10 years ago, I would have thought that the biggest part of the…”
Chubuk: Science requires out-of-domain generalization unlike standard ML
“Machine learning works best on the training set distribution. But in science and technology, we almost only care about auto-domain generalization, right?”
Chubuk: High-throughput liquid- and powder-mixing robots have become commoditized
“These robots, they became quite commoditized, actually, just mixing powders, or mixing liquids, and then sending it to characterization.”
Chubuk: Periodic Labs aims to achieve automated materials characterization soon
“I think one thing that isn't as advanced right now, but we feel like we can do pretty soon, is automated characterization itself. So, you mix powders, you put it in the, some characterization tool, you get the result out, What is the actual output? I think tha…”
Chubuk: Superconductivity moonshot will yield independently valuable automated lab capabilities
“To discover a exciting superconductor, we probably have to develop so many capabilities on the way there that's By themselves very useful. For example, automated synthesis, automated characterization, being able to model or predict high temperature superconduc…”
Chubuk: High critical magnetic field may matter more than Tc for fusion
“Another one could be a really high critical magnetic field, which turns out might even be more important for fusion applications than TC itself.”
Chubuk: Physical lab measurement prevents AI reward hacking
“For real life experimental measurement of TC, it's much harder to reward hack, which we love.”
Chubuk: Periodic Labs is not prioritizing full wet-lab automation
“You know, we, we're not really prioritizing full automation anyway, so if we get better results with humans doing part of it, that's great.”
Chubuk: Minimal physical experiments carry huge information value by validating synthetic simulations
“What's interesting about scientific data is it's not just a few bits or numbers, right? Like, for example, there are certain experiments you can run where the result you get from it is just, say, three floating point numbers. But the implications of those coul…”