Alessio Fanelli: preference

Transcript context

…Yeah, absolutely. So I think this again, rather than having open ended retrieval, open ended reasoning, our approach is to decompose the problem into multiple different subsystems that have specific goals. And so, I mean, temporality is a great example. When you think about time, I mean, just look at all of the libraries for managing calendars. Time is kind of at the intersection of language and math. And this is one of the places where, without taking specific technical measures to ensure that you get high quality narrative overlays of statistics that are changing over time and have a description of how a PE multiple is increasing or decreasing, and like a retrieval system that is aware of the time, sort of the time intent of the user query, right? So if I'm asking something about breaking news, that's going to be very different than if I'm looking for a thematic account of the past 18 months in Fed interest rate policy. You have to have retrieval systems that are, to your point, like if I just look for something that is a nearest neighbor without any of that temporal or other qualitative metadata overlay, you're just going to get a kind of a bag of facts. 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. And so I think, at least presently, you have to have subsystems that are aware of the semantics of the documents, or aware of the semantics of the intent behind the question, and then have multiple, we have multiple evaluation steps. Once you have the generated outputs, we assess it multiple different ways to know, is this a factual statement given the sort of content that's been retrieved? Yep. And what about, I think people think of financial services, they think of privacy, confidentiality. What's kind of like customer's interest in that, as far as like sharing documents and like, how much of a deal breaker is that if you don't have them? I don't know if you want to share any about that and how you think about architecting the product. Yeah, so one of the things that gives our customers a high degree of confidence is the fact that Brandon operated a federally regulated derivatives exchange. That experience in these highly regulated environments, I mean, additionally, at Workday, I worked with the financials product, and without going into specifics, it's exceptionally sensitive data, and you have multiple tenants, and it's just important that you take the right approach to being a steward of that material. And so, from the start, we've built in a way that anticipates the need for controls on how that data is managed, and who has access to it, and how it is treated throughout the lifecycle. And so that, for our customer base, where frequently the most interesting and alpha-generating material is not publicly available, has given them a great degree of confidence in sharing. Some of this, the most sensitive and interesting material, with systems that are able to combine it with content that is either publicly or semi-publicly available, to create non-consensus insight into some of the most interesting and challenging problems in finance.…

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