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
Siddharth: ASI means automating 90% of computer tasks for 90% of humans
“And I think of ASI as us having automated 90% of the tasks that 90% of humans do today in front of a computer, right?”
Siddharth: Reasoning models ended demand for simple AI data factories
“This industry had a huge shift after the reasoning models came out late last year, O-one being the first. The shift is, before the reasoning models, this industry needed simple data. Gobs and gobs of simple data. You needed a data factory. Or, ah, this industr…”
Siddharth: Enterprise AI performs best on 0.5B-to-10B parameter custom models
“It'll probably probably be a smaller model. Oftentimes we see a half a billion to a ten billion parameter model. That's the regime, not a trillion parameter model. And this will be faster, more accurate.”
Siddharth: Verifiable domains allow self-play reinforcement learning to replace RLHF
“Now, for these verifiable domains like coding and math, instead of doing reinforcement learning with human feedback, you can do reinforcement learning. Because you can automatically check when you got the correct answer or not in these verifiable domains. And …”
Siddharth: Frontier AI Training Demands Complex Reasoning and STEM Data
“The race to HGI is on, and the data needs of these models have shifted. These models need incredibly complex data for training them to get better at coding, advanced reasoning, STEM, etc.”
Siddharth: Enterprise AI does not require 100% accuracy with human oversight
“I feel like the self-driving car industry did a lot of damage to AI in one particular way, which is it because with self-driving cars, 99% accuracy is not enough. That, that last one percent really matters. But with a lot of AI systems, if you're automating th…”