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
Suhail Doshi: Video AI models mimic training data rather than understanding real physics
“What's really doing is it's representing
The physics it understands in the videos it's being trained on, which could be incorrect physics. It's really what it understands what it's being trained on is kind of my main, the main thrust of my point. And that to a…”
Doshi: AI companies would leave NVIDIA if cheaper compute alternatives existed
“It's not CUDA that's keeping, I think, keeping a lot of us. It's actually that there is nothing really dramatically better than NVIDIA's GPUs. And so if there's nothing dramatically better than, I mean, the reality is the cost for training and inference are so…”
Doshi: Articulating explanations does not prove AI is genuinely reasoning
“Just because something is able to articulate its reason for doing, for getting to an answer, doesn't mean that it is necessarily reasoning.”
Doshi: Computer vision lacks a single unified model equivalent to LLMs
“This is missing in vision, but definitely kind of exists in language and language. We can solve hundreds or thousands of different tasks. But in graphics, but in vision, it's all separated. It's kind of like where language was back three or four years ago wher…”
Doshi: AI advancement is outpacing Moore's Law by a lot
“We're still so at the beginning, the early phases of this thing that it is moving faster than Moore's law. By a lot.”
Doshi: True AI reasoning can only be tested out-of-distribution
“One way that you could maybe litmus test reasoning is if you gave the model something truly out of distribution, right? Like an example of something that's truly out of distribution that humans face was COVID, a pandemic that it had not yet seen before. And th…”
Doshi: AI Image Models Homogenize Styles by Overfitting to Average Human Preference
“I think that the models are probably too curated and maybe overfit to be based on human preference. And human preference isn't your human preference. It's your preference. I mean, it's some kind of average of human preference.”