Speakers in the public record
Claim mix
belief 19prediction 4evaluation 3uncertainty 1preference 1recommendation 1
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Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
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The useful parts, with receipts.
29 published records
“I actually, I think I was back then actually when I was working on Quora, I think the thing that everybody was fascinated in was just like general like deep learning advancements and stuff like GANs and generative like images and just like new architectures that were evolving.”
- Publisher
- Latent Space
“I think that's probably what's going to happen. So I'm like reasonably bullish on this because I don't think there's really a good alternative beyond you just human annotating a bunch of data sets, um, and then trying to like just manually go through and curating, like evaluating eval metrics.”
- Publisher
- Latent Space
“Like, I think I actually think that I don't think anyone's like, I think a lot of people have thoughts about that, but like, for what it's worth, I don't think the final state will be right.”
- Publisher
- Latent Space
“There's some structured annotations and there's some like unstructured texts. And so like, um, somehow combining all the expressivity of like SQL with like the flexibility of semantic search is something that I think is going to be really important.”
- Publisher
- Latent Space
“I think that we, we added integration with, and so we just, uh, by virtue of having more of these services, I think more and more people are trying it out.”
- Publisher
- Latent Space
“I think in the end, like this is a very fast moving space and we want to just like be one of the, you know, like dominant forces and helping to provide like production quality outline applications.”
- Publisher
- Latent Space
“Oh, it's just like, I mean, you just define your own subclass. I think, I think that's it.”
- Publisher
- Latent Space
“I think the stuff has been mostly solved or at least there's just a lot of other stuff you can do to try to improve the overall performance.”
- Publisher
- Latent Space
“Um, and I think that will probably just expand the query sophistication of these vector stores and basically make it so that users don't have to think about whether they would just call this like hybrid querying.”
- Publisher
- Latent Space
“I also did finance and I think I saw that you also interned at Two Sigma where I worked in New York.”
- Publisher
- Latent Space
“The second piece is like, take, there's like certain components in there that might not be directly related to the LLM app that would be nice to just like have people use, uh, an example is the PDF viewer, like the PDF viewer with like citations. I think we're just going to give that right.”
- Publisher
- Latent Space
“I've said, so a lot of people come to me for my Twitter advice, but like, I think you are doing one of the best jobs in AI Twitter, which is explaining concepts and just consistently getting hits out.”
- Publisher
- Latent Space
“I think, um, yeah, we try to remain unopinionated about storage providers. So it's not like we don't try to like play favorites.”
- Publisher
- Latent Space
“I don't know if this is mentioned in your explainability also allows for sourcing.”
- Publisher
- Latent Space
“I think if there was a universal default, that would be amazing. But I think increasingly we found, you know, people are just defining their own like custom parsers for like PDFs, markdown files for like, you know, SEC filings versus like Slack conversations.”
- Publisher
- Latent Space
“I know. But like, I just think, uh, from like the AGI, like, you know, just modeling like the human brain perspective, I think that there is something nice about just like being able to optimize that system.”
- Publisher
- Latent Space
“You know, at the end of the day, OpenAI still wins on that. I think that's true.”
- Publisher
- Latent Space
“I just know from a technical side, I think, I think people are going to do more of it.”
- Publisher
- Latent Space
“I think we're trying to curate this into some like more opinionated principles because there's some like open questions here.”
- Publisher
- Latent Space
“I was like, oh, I'll play around with LLMs a bit and then hacked around on stuff. And I think I've told the story a few times, but you know, I was like trying to feed in information into GPT-3.”
- Publisher
- Latent Space
“Like probably not as for some people, the three lines of code might work, but I think increasingly, like honestly, 90% of the users I talked to have questions about how to improve the performance of their app.”
- Publisher
- Latent Space
“Um, the way I think about this is kind of like, obviously RAG is the default way, like to be clear, RAG right now is the default way to actually augment stuff with knowledge.”
- Publisher
- Latent Space
“I think in general, when I talk to founders about like the fundraise process, it's never like the most fun period, I think, because it's always just like, you know, there's a lot of logistics, there's lawyers you have to, you know, get in the loop.”
- Publisher
- Latent Space
“What we really wanted to do was, because for us, like time was of the essence, like we wanted to ship very quickly and still kind of build Mindshare in this space.”
- Publisher
- Latent Space
“Um, but this kind of relates to the initial like AI engineering posts that you put out and then also just like the role of an AI engineer and the skills that they're going to have to learn to truly succeed because there's an entire On one end, you have people that don't really, uh, like understand the fundamentals and just want to use this to like cobble something together to build something.”
- Publisher
- Latent Space
“There's like a certain amount of just like LLM ops, like tooling and concepts and just like practices that people will kind of have to internalize if they want to optimize these. And so I think that the reason I think being able to build like RAG from scratch is important is it really gives you a sense of like how things are working to get, help you build intuition about like what parameters are within a RAG system and which ones actually tweak to make them better.”
- Publisher
- Latent Space
“I actually think once the multimodal models come out, I think there's just like mathematically nicer properties of you can just get like join multiple embeddings, like clip style.”
- Publisher
- Latent Space
“One is like RAG is basically just, just a hack, but it turns out it's a very good hack because what is RAG rag is you keep the model fixed and you just figure out a good way to like stuff stuff into the prompt of the language model and everything that we're doing nowadays in terms of like stuffing stuff into the prompt is just algorithmic.”
- Publisher
- Latent Space
“Because I think what I also ended up discovering was the fact that there was starting to become a wave of developers building on top of GPT-3 and people were starting to realize that what makes them really useful is to apply them on top of your personal data.”
- Publisher
- Latent Space