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 6 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
Dan Roberts: AI scaling requires novel algorithms, not just pure compute
“It's not that scale is all you need. You need to also have good ideas to guide the scaling.”
Dan Roberts: AI models do not experience discontinuous emergence or grokking
“You have these crazy, huge systems that have all sorts of interesting phenomena, and, you know, if you think about it the right way, they don't grok. There's just this nice continuity.”
Pre-training models on language before reinforcement learning is the correct architecture
“Having the model have a prior of language and being able to like, think in language and then train on top of that, that seems like clearly the right. The right thing to do.”
Dan Roberts: AI scaling laws should be analyzed big-to-small
“The way to think about scaling and say, scaling laws is not small to big, but big to small.”
Roberts: Powerful pre-trained models are necessary for effective RL and reasoning
“If you have a powerful enough pre-trained model, then it can start to do well at RL. It can start to like think at use test time compute to for instance, solve, solve math problems that it wouldn't otherwise be able to do.”
Roberts: AI models improve performance by generating running thought tokens in language
“The natural way it thinks is in language. It's a language model, and so that's sort of this key insight that, that you can cause it to do better just by producing a thought process in, in, in token space, in, in language.”