The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 11 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 1 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Fedus: Pre-training on domain data outperforms retrieval-augmented generation
“However, as we've seen with things like ChatGPT 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.”
Fedus: Physics and chemistry demonstrate scaling laws similar to AI
“On the material science side, we're seeing scaling laws within physics, within chemistry both with respect to simulations, with respect to experiment, and it's like the same kind of principles at play and ML.”
Fedus: Physics provides ideal verifiable reward functions for AI
“Physics is very verifiable. It's a great reward function, fairly fast iteration loop. You have simulators for large classes of physical systems.”
Fedus: Periodic Labs uses physical experiments as RL reward functions
“And what we're doing, and by having the lab, is we create a physically grounded reward function. That becomes the basis on which we're optimizing against. And so, If a simulator has some deficiencies or some issues, we always error correct, because for us, the…”
Fedus: Early ChatGPT was mathematically weak due to friendliness rewards
“The reward functions that we were using originally couldn't determine whether you were mathematically correct or not. So early versions of Chachapiti were mathematically not particularly strong, and it sort of results from the reward function. What did you opt…”
Fedus: AI physics requires generating new experimental data, not web scrapes
“The technology that we think is necessary to do it has really just emerged in the last couple of years, and this data Isn't like on a Reddit forum or something like you need to actually go produce experimental data, simulation data.”
Fedus: AI science requires real-world experimental feedback loops
“Ultimately science is driven against experiment in the real world. And so that's what we're doing with periodic labs. We're taking these precursor technologies and we're saying, okay, if you care about advancing science, we need to have experiment in the loop.”
Fedus: Unpublished negative scientific results are uniquely valuable for AI
“Then another point is, it's very uncommon to publish negative results. All of the results are basically positive, and a valid negative result is very valuable.”
Fedus: Periodic Labs will build AI co-pilots for space and defense
“Basically co-pilots for engineers, researchers in advanced industries. So maybe perhaps just being in Silicon Valley, we, you know, we really think about like computer oriented work. Everything is digital. Everything is bits, but there's so many industries. Li…”
Fedus: High-compute reinforcement learning is essential for AI tool use
“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.”
Fedus: Mixing data distributions does not guarantee AI model generalization
“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.”