Public evidence record
Published podcast speaker
Claim ledger
16 transcript-backed records
01 / belief
“I'm not sure how you would maintain this equilibrium for a long period of time, but I think if we got to that point we would be in an okay position.”
02 / prediction
“I expect that models will be able to use websites that are designed for humans just by using vision, after the vision capabilities get a bit better.”
03 / belief
“Some of the people are very talented, and we even find that they're at least as good as us, the researchers, at doing these tasks and they're much more careful than us. I would say the people we have now are quite skilled and conscientious.”
04 / belief
“I agree that you'd probably also want to supplement that with some kind of fine-tuning.”
05 / belief
“There are a lot of different, separate axes for improvement. We think about data quality, data quantity.”
06 / belief
“I think people will still have different interests and ideas for what kind of interesting pursuits they want to direct their AIs at.”
07 / belief
“Obviously, the user might ask the model to do something that we think is actively harmful to other people.”
08 / belief
“On the other hand, I would also expect to gain a lot from doing practice at training time. So I think that you’d get the best results by combining these two things.”
09 / uncertainty
“I don’t know if it’s a phase transition but I might expect the same out of models where there might be some capabilities that work at multiple scales.”
10 / prediction
“I expect the mental model of a helpful assistant or helpful colleague to become more real.”
11 / evaluation
“We had browsing as another feature in it although we ended up deemphasizing that later on because the model's internal knowledge was so good.”
12 / evaluation
“I would say there's a decent amount of room for variation in exactly how you do the training process.”
13 / evaluation
“There are some algorithms that work this way, like mixture models or multiplicative weight update algorithms, where you have—I don’t want to say mixture of experts because it means something different—basically a weighted combination of experts with some learned gating.”
14 / evaluation
“We found that a tiny amount of data did the trick, even when you mixed it together with everything else.”
15 / evaluation
“To some extent, putting a lot of stuff into context will take you pretty far because we have really long context now.”
16 / prediction
“I realized a lot of the things that people thought were flaws in language models, like blatant hallucination, could be not completely fixed but things that you could make a lot of progress on with pretty straightforward methods.”