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 7 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 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 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: 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Fedus: Unpublished negative scientific results are uniquely valuable for AI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: 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.”