The Ledger, every show

“I don't have very high hopes for humanity to develop that conceptual understanding to the level that we would need it in order to do all the interventions we want to do.”

“At the end of the day, in my mind, that's the one and only thing we know really works. If you want to push deep learning forward is to make it faster and more effective and more efficient on a given piece of Hardware.”

“And if you want to look at, say, the biggest differences, for example, between the transformer, as it was described in the attention is all you need paper, and some of its ancestors, like this decomposable attention model, the big difference is just that the t…”

“I don't think GPUs are at the sweet spot when it comes to large-scale deep learning with respect to exactly those trade-offs, and so it may very well be that if we actually try these combinations, we might actually even quickly find something that's better.”

“The other main contributor, I think, to the success of this architecture was optimism and hope. So suddenly you were in a situation where, for whatever reason, a bunch of things that people tried with this started to work, and then more started to work, and th…”

“And ironically, and this comes back to a question that many people ask, I think around, does it make any sense to train on generated data? Because information theory, family information theory, very clearly says, nope, you're not going to get more information …”

“I think it's super effective in test time search. I do think it's clunky because it's not something that you can easily end to end optimize. So, right, basically this is also what I'm, what I was trying to get at a little bit maybe with saying, well, some of t…”

“And now we're talking about a modality that might end up before the end of the decade being the second or third biggest modality in terms of revenue and potentially also in terms of impact.”

“Because screening is just not going to cut it 10 to the second and 30th, and that's really just one antigen that we're coding for there when we actually want to code for many and update those for any given. [1415] Sarah Guo: For any given, yep. [1416] Jakob Us…”

“There are people trying, and I think it's worth trying. I, I'm not super optimistic about that. I think it'll work for some cases, right, where it's simple enough that we can get it. I think there are many cases where it just isn't, right? Like, say, climate a…”

“Then ultimately, the way you scale that compute depends on the prompt and how much, how long that is. The longer the prompt, the more compute you get. And it depends on, and there's of course many different screws to tweak here, the length of the response. The…”

“I'm very bullish on human augmentation in the very long term, but it's one that I don't see intuitively. I think looking at our brains, even just physically, they seem to be very focused, and this is not surprising, on our IO. And why would there somewhere in …”

“But I think that's because we confuse fine tuning and pre-training. Pre-training is all of evolution. And then basically you arrive at this thing that It's maybe doing something that's completely, in a certain sense, a completely irrelevant task at first, but …”

“There was always this dream of, and I think it's a pipe dream, of having the cycle between experimentation, and then you put that into some, something in silico, something running on computers, and then that informs the experiments, and then you kind of Iterat…”

“In terms of adaptive, adaptive time transformers, et cetera, we tried this universal transformer thing actually a long time ago. It just hasn't caught on. And that's because it just doesn't work right at this point. It doesn't work well enough. It's not like i…”

“But right now, if you look at RNA, manufacturing and distribution infrastructure, we're going to have six to eight billion doses two years from now, manufacturable and distributable across the globe.”