Speaker unverified: belief

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…Yeah, precisely one of the very important things is to being capable of valuing the expectation of revenue of placement and knowing that if you are going to place an advertising in that placement, then there is a high probability for you for it to be clicked on or not. And you have to evaluate that. In order to do that, you're going to use machine learning and AI models that are precisely are going trained to evaluate whether a placement and given some products that we can put on is going to be something that is going to bring revenue. And for that, we precisely do a lot of, we collect all those data that Jeremy talked about. And there is a huge machinery that we put in place in order to learn from that data. And If I had to summarize the type of problem that we're doing, even though it's a bit more complicated, should we bid or should we not bid on that place, that placement? And we learn a classifier from that. And it's, of course, there's this question that you start, you started with the weather is when you click and want to see. or your information. There's this question about utility and readability that exists. If you want to be very precise in terms of evaluating the value of a placement, then you have to use very sophisticated models. You probably have heard about deep learning models. And the more sophisticated the models are, the less easy it is to understand what they have computed. So there is this trade-off to have. Now we have all in the industry and in particular in Catilia, we use those deep learning models in order to assess whether a placement is good and assess whether this product is going to be relevant for you. So it means that in a way we have what we have gained in terms of precision and relevancy, we have something to make up in terms of explainability. And just so you know, because we talked a bit before we started, that's a big topic in terms of research, scientific research, AI research to provide explainability on those models that are doing crazy stuff. And one of the things that we are looking at as well, but it's not easy like to have both high utility and high explainability. And jump in, I actually have a question I'm interested in here. The idea of the user profile and what this person might be interested in is obviously super, super core. One thing that I think is really interesting is you guys have this OpenAI partnership, which is super cool and I know very new, so some of these answers might not exist yet. But one thing that people talk a lot about, of course, is that the queries are much richer in the context of AI and chat. But something that I haven't heard people talking about is whether the profiles are meaningfully different. Claude or an agent, like it knows a lot about me. Is that starting to change what how we understand the user and where do you see that going? 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.…

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