Speakers in the public record
Claim mix
belief 9recommendation 3evaluation 3preference 2disagreement 1prediction 1
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
The useful parts, with receipts.
19 published records
“Something that I think a lot of people are uncertain about, and I don't expect you to know either, is that whether or not you can fine tune new information in, and I think that that is the focus of some of your open questions.”
- Publisher
- Latent Space
“You know, if we lock things down to the people that we think, you know, the elites that we think can be trusted to run it for us, yeah, I think all bets are off about where that leaves us as a society, you know.”
- Publisher
- Latent Space
“I think he still counts. And anyway, just to round out the bio, you have a lot more other credentials, obviously, but most recently you started Fast.”
- Publisher
- Latent Space
“I think that's an approach which more developers took to publish some of their work along the way.”
- Publisher
- Latent Space
“You've shown that it's possible. And I think your constant advocacy, your courses, your research that you publish, you know, just the other day you published a finding on, you know, learning that I think is still something that people are still talking about quite a lot.”
- Publisher
- Latent Space
“You know, it still requires kind of really understanding the GPU architecture and writing it in that kind of very CUDA-ish way. So yeah, I think, you know, if Mojo or something equivalent can really work well, we're going to see a lot more FlashAttentions popping up.”
- Publisher
- Latent Space
“I think that's the struggle of pitching small models, because small is great, you know, you don't need a lot of resources to run them, but the performance evaluation is always so iffy, it's always just like, yeah, it works on some things, and we don't trust it for others.”
- Publisher
- Latent Space
“You know, particularly in kind of an enterprise setting, I think there's a lot of like repetitive kind of processing that has to be done. It's a useful thing for coders to know about, because I think quite often you can like replace some thousands and thousands of lines of complex buggy code, maybe with a fine tune, you know.”
- Publisher
- Latent Space
“Now that's an important message. And yeah, that's why we've been promoting a lot of open source developers, open source communities, I think, letting the builders build and explore.”
- Publisher
- Latent Space
“You know, work with vision, work with tables of data, work with kind of recommendation systems and collaborative filtering and work with text, because we felt like those four kind of modalities covered a lot of the stuff that, you know, are useful in real life.”
- Publisher
- Latent Space
“Don't, you know, don't, I keep telling people, don't track validation loss, track validation accuracy because at least that will still be useful.”
- Publisher
- Latent Space
“You know, and I showed again through research that we demonstrated in our videos that you can do better than GANs, much faster and with much less data. And nobody cared because again, like if you want to get published, you write a GAN paper that slightly improves this part of GANs and this tiny field, you'll get published, you know.”
- Publisher
- Latent Space
“I try to make like, when I work with some domain, I try to make it like, I want to make it as enjoyable as possible for me to do that. So I always try to kind of like, like with GHAPI, for example, I think that GitHub API is incredibly powerful, but I didn't find it good to work with because I didn't particularly like the libraries that are out there.”
- Publisher
- Latent Space
“Even though I originally created three-step approach that everybody now does, my view is it's actually wrong and we shouldn't use it. And that's because people are using it in a way different to why I created it.”
- Publisher
- Latent Space
“Yeah. And then when I came across neural nets when I was about 20, you know, what I learned about the universal approximation theorem and stuff, and I started thinking like, oh, I wonder if like a neural net could ever get big enough and take in enough data to be a Chinese room experiment.”
- Publisher
- Latent Space
“You know, it took a lot longer than it should have because I spent way longer in management consulting than I should have because I got caught up in that stupid rat race.”
- Publisher
- Latent Space
“5 has never read Wikipedia, for example, so it doesn't know who Tom Cruise is, you know, it doesn't know who anybody is, it doesn't know about any movies, it doesn't really know anything about anything, like, because it's never read anything, you know, it was trained on a nearly entirely synthetic data set, which is designed for it to learn reasoning, and so it was a research project, and a really good one, and it definitely shows us a powerful direction in terms of what you can do with synthetic data, and wow, gosh, even these tiny models can get pretty good reasoning skills, pretty good math skills, pretty good coding skills, but I don't know if it's a model you could necessarily build on.”
- Publisher
- Latent Space
“And then in step three, rather than fine tuning on a reasonably specific task classification, let's fine tune on a, on a RLHF task classification. And so that was really, that was really key, you know, so I was kind of like out of the NLP field for a few years there because yeah, it just felt like, I don't know, pushing uphill against this vast tide, which I was convinced was not the right direction, but who's going to listen to me, you know, cause I, as you said, I don't have a PhD, not at a university, or at least I wasn't then.”
- Publisher
- Latent Space
“And then I read ULM Fit and turns out it did work. And so I did it, you know, bigger and it worked even better.”
- Publisher
- Latent Space