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.

clear all ✕

why aren't all 5 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

Assertion Supported
Finn: Diverse Home Training Matches Custom Target-Environment Performance
“And we find that if we actually increase the amount of homes, the amount of locations that are represented in the data, The performance increases, which is great. And it actually gets to the same level of performance as if we train on data from that target env…”
Chelsea Finn Jul 22, 2025 ▶ 23:45 Chelsea Finn: Building Robots That Can Do Anything · Y Combinator
Assertion Supported
Finn: Physical Intelligence adapted its model to an unseen third-party robot
“We're also able to apply that same recipe to robots at other companies. This is a robot that I've actually never seen in person before. They collected data. They sent the data to us. We fine tuned our model on their data. We actually didn't even know exactly h…”
Chelsea Finn Jul 22, 2025 ▶ 16:37 Chelsea Finn: Building Robots That Can Do Anything · Y Combinator
Assertion Supported
Finn: Full Pre-Training Mixture Boosts Robot Performance Over 20% in Novel Homes
“And we find that these kind of bars on the right, which are excluding data from static robots in labs and environments and so forth reduces performance significantly. So the performance goes down to less than 60% when you exclude that data when evaluated in no…”
Chelsea Finn Jul 22, 2025 ▶ 23:06 Chelsea Finn: Building Robots That Can Do Anything · Y Combinator
Assertion Supported
Finn: Architectural fix boosted robot language following rate from 20% to 80%
“And second, it also followed language far better an 80% follow rate rather than a 20% follow rate which suggests that we're able to preserve the kind of pre-training in the vision language model backbone.”
Chelsea Finn Jul 22, 2025 ▶ 21:08 Chelsea Finn: Building Robots That Can Do Anything · Y Combinator
Assertion Supported
Finn: Modern AI coding assistants build on general data, not just code
“For example, if you want to build a coding assistant, you don't nowadays develop something specifically for coding, but you develop and you build on models that were trained on large amounts of data, not just on code.”
Chelsea Finn Jul 22, 2025 ▶ 1:25 Chelsea Finn: Building Robots That Can Do Anything · Y Combinator
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