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 10 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 1 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
Biderman: AI-native companies will amass trillions of internal tokens within 18 months
“In 18 months, many companies would have maybe trillions of tokens, which of internal company data, proprietary data. I'm talking about like maybe trillions. It sounds exaggerated, but I don't think it's an impossibility if they're really AI native.”
Biderman: Hard engineering tasks will require test-time gradient updates
“We think that eventually part of the solution for very hard tasks in, in science and engineering and defense and all that stuff will involve some form of gradient based updates during during doing these long horizon tasks.”
Biderman: In 18 months, data scale will require weight-based learning
“Other parts of it are bets that in 18 months from now, the scale of the data will require the methods that we know from pre-training work.”
Biderman: PC hardware will soon run near-trillion-parameter models locally
“And in the long, long term, I do think these things will actually run on people's devices, and we're seeing right now the new hardware on personal computers is already, ah, you know, soon approaching the ability to run inference on close to trillion parameters…”
Biderman: AI models must learn to autonomously filter out erroneous user feedback
“Increasingly the models will get better, and increasingly they'll know more things than we do, so the model in some way has to learn and understand and kind of, like, discern what, which feedback is valuable and which feedback should be ignored.”
Biderman: Semi-supervised learning will become super crucial again
“My PhD was focusing on On, on a field that's not super in vogue today, but I think will become super crucial again, which is semi supervised learning”
Biderman: Model accuracy will still degrade at 10M context window scale
“But two is like, for the agentic tasks of 18 months from now, inside those major repositories of knowledge, and asking the models more and more things in underspecified ways, I suspect that the accuracy of the models would go down. The phenomenon of context fr…”
Biderman: Harmless enterprise queries on frontier models cost thousands of dollars
“And now you can solve these tasks with frontier models and compaction. And when you ask them to do so, they will consume thousands of dollars for queries that we think are harmless. That every employee in the company would be able to answer.”
Biderman: AI solutions will rely on model routing, not single monolithic models
“So I think routing will be part of the solution there for sure, and I think Many people, not just myself, say this solution is multi-modal. It's not Engram taking over. There's one model, and you teach it things, and you can close Stargate. That's not our appr…”
Biderman: Processing a Wikipedia article in Llama 70B consumes 80GB HBM
“If you take a Lama, a 70 B model, and you load one article from Wikipedia, which is a few tens of kilobytes, and you have the model read this The brain state of the model when reading this few tens of kilobytes is like, 80 gigabytes. 80 gigabytes on, on the HB…”