Diarmuid Gill: evaluation

Transcript context

…So many things. So with the first very first thing you said, okay, that's there's still a lot of things to unpack, to uncover. We are precisely at this stage because of course there are many questions about privacy of the data. question. So, so far there's no answer yet. But one thing that I can answer is though, is that those conversational agents they provide with a new perfect before you just had essentially the websites or some apps that you could use and now they are have that. One thing that is very important is that those models like , etcetera, they are very, very good at general reasoning. So they can do some recommendation and in some ways you can think that they're going to be good to do. And if you ask, okay, I would like to buy shoes, they are going to propose you with shoes that are relevant. But one of the things that we is commerce data. We are going to tell us about what exactly people are interested in. And the big challenge that we have today is precisely to make the, to have both models, the LLM models that are behind all those conversational agents with all the models that we have in Criteo that are capable of providing very accurate commerce information. And the challenge in terms of technical challenge is precisely to merge the two and maybe some ways, but that's not on our part from the LLM. They're going to have some information that is going to be encoded. But what is precisely not necessarily to have this information, but more to know and see how we can enhance or those models can enhance our commerce models that we built for years. And that's where we sit as of today. To you, Dermot. Yeah, I think, yeah, that's exactly right. So the thing about the LLMs, and it's amazing technology. We're super, super impressed by the power of all of these. I think everyone is. But when those companies, when they train their model, it is true and accurate at that moment in time. Commerce data is actually way, way more dynamic, right? And so, for example, you know, kind of they would not be able to know, for example, that there are flash pricing. So, you know, around Black Friday and so on, where things change very rapidly. They also would know, for example, things like ruptures in stock, right? So the way that they gather their information is by doing this massive crawling of the internet. And then at that point in time, when they've updated their model, very quickly, it starts becoming stale, at least from the product point of view. So Gradeo has this massive network of 17,000 retailers. We ingest their product data on a daily basis, sometimes multiple times a day, and it means that we always have access to fresh data. So like Liva said, we did this hybrid architecture where an LLM in partnership with technology provided by Accumulate Criteo can ensure that when a user asks for a product, they not only get all the richness that an LLM can provide, but they can also ensure that it's up to date and it's accurate because From a user point of view, it's a very bad experience when you search for a product and it comes back with something, you click true, and the product's either a different price, or it's out of stock, or it's not what you were thinking about. So that's why that hybrid architecture makes so much sense. Yeah, so today, just to make sure I'm getting it, the process is ultimately pretty similar to what you'd have on the open web when you're in the chat interface, maybe with a richer query. AI is being inserted in a whole bunch of other different places in the stack, but that applies really across all surfaces.…

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