Speakers in the public record
Claim mix
prediction 4evaluation 4preference 3belief 2uncertainty 1recommendation 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.
15 published records
“You know, if I think that GLP-1s are going to blow up the diet industry, identifying and putting in context a negative result from a human clinical trial, or for example, that adherence rates to Ozempic after a year are just 35%, what are the implications of this?”
- Publisher
- Latent Space
“Getting these systems to behave in a predictable and repeatable and observable way is equally challenging to a lot of the methodological challenges. But then you bring in, whether it's law or medicine or public policy or in our case finance, I think a lot of the most valuable, like Grammarly is a good example of a company that has generative work product that is valuable by most humans.”
- Publisher
- Latent Space
“I mean, we've talked a little bit about this, and it's notable that I think there's a lot of anthropomorphizing going on, and that it reflects the difficulty of evaluating the systems.”
- Publisher
- Latent Space
“I don't know if one year ago you could have told me that that was going to happen.”
- Publisher
- Latent Space
“I think there is, the more that I have learned about how teams at hedge funds actually behave, and you look at like systematics desks or semi-systematic trading groups, man, it's a lot like a big machine learning team.”
- Publisher
- Latent Space
“One, temporality of data is important because every quarter there's new data and like the new data usually overrides the previous one.”
- Publisher
- Latent Space
“I mean, just the semantics of how we think about gender as it relates to professions are encoded in the structure of these models and like language models, I think are much more sort of complete representation of human sort of beliefs.”
- Publisher
- Latent Space
“lities proliferate because more people have that experience, you're gonna see teams that release data corpuses that just imbue the models with new behaviors that are especially interesting and useful.”
- Publisher
- Latent Space
“You start to see how organizations are organisms. And I think of the way that like an accountant or the market encodes information in databases similar to how social insects, for example, organize their work and make collective decisions about where to allocate resources or time and attention.”
- Publisher
- Latent Space
“Yep. And what about, I think people think of financial services, they think of privacy, confidentiality.”
- Publisher
- Latent Space
“I think that that is explicitly not helpful, because the worst failure state for these systems is that they are wrong in a convincing way.”
- Publisher
- Latent Space
“Or we think that Eli Lilly is actually very exposed because of how unpleasant it is to take examples.”
- Publisher
- Latent Space
“I think we will generate a small corpus of high-quality domain expert annotations and always compare that against how well is either LLM supervision or even just a heuristic.”
- Publisher
- Latent Space
“When I started the company, I think I had more faith in the ability of large context windows to generally solve problems relating to synthesis.”
- Publisher
- Latent Space
“As you think about the graph of those states that your system is moving through, once you develop conviction that one behavior is useful and repeatable and worthwhile to differentiate down into a specific kind of subsystem, that's where like fine tuning and like specifically generating the training data, like having human annotators produce a corpus that is useful enough to get a specific class of behaviors, that's kind of how we use fine tuning rather than trying to imbue net new information into these systems.”
- Publisher
- Latent Space