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Many still see AI as futuristic or out of reach—but what does it really take to make it useful in everyday work? We’ll cut through the hype with Florian Douetteau, CEO and co-founder of Dataiku, and discuss what it takes to build real, lasting value with AI. With a $3.7 billion valuation and over 700 customers, we'll unpack how this once Parisian startup has made enterprise AI accessible, practical, and impactful across entire organizations. 

EVERYDAY AI: BEYOND THE BUZZWORD

Conferences

Hello again. AI is still often discussed like it's futuristic or out of reach, but for many companies it's already part of everyday work, helping decision making processes, improving operations, and changing how teams function. To discuss what makes I really useful and where its value truly lies, please welcome co-founder and CEO of Data IQ, Florian Duetta. So Florian, you co-founded Dau in 2013. How long ago was that in AI time? Uh, 100 years, probably 100 years, give or take. Well, again, the reason I, the reason I bring it up is because so much has happened in AI since then. What were those early days of Data I like? In in the early days it was lots of things were similar than than today, meaning uh enterprise were facing new opportunities and technological challenges, and uh the question back then is like, what do you help people? There was enterprise to get value um from AI. And my take back then and until now is that the The roadblocks where I are usually not the one that people think. It's not that much about technology or the quality of technology per se. All of those innovation pouring in. It's about the ability for enterprise to make it theirs. And in particular, back then, what was fascinating to me is that you had the opportunity to create more value with data, of course, but many companies were like, oh my God, in order to make it happen, we need to hire a lot of experts, lots of data scientists, or what else? And this perspective would not work from my perspective because What you really need if you're an enterprise is to transform your business and actually to enable the people in your teams to make more by themselves, which led me to this venture of building a platform to make it easy for the enterprise, for people in the business to create their own AI, their own models to work on their data, to create stuff by themselves, which led to the company I know lead. Well, let's let's name it. What are some of the roadblocks that enterprises often run into as they start to adopt AI? When they start to adopt AI, um, I think the first issue they've got is the This they've got two things. They've got the skill gap, as in like what do we need to have in the business as skills to make it happen, and they've got another one which is just the fear, the fear of failure, the fact that AI is not perfect, AI is not deterministic, and so a business should run the lack a clockwork. So who do you actually reconciliate those two facts? And you're expressly running into a fear of failure. Where do you think that comes from? The fear of failure comes from the fact that indeed AI technology is RO, meaning the RO power. In order to make it work, you need to actually combine AI with your own data. You need to combine AI with things that make sense for your business. You need to put it in control before building it into a process. And it's not just about, for instance, one of our customers started to deploy agents in their shops. They are selling clothes and part of the motivation was to help people in the business to look for inventory so that they could help you faster determining whether this or that piece of item could be available or not. So instead of like going through scanning or whatsoever being To search for it faster. Great, but an agent can help you do that for sure. But in order for the agent to actually be smarter, you also want the agent to be able to suggest if the piece is missing, what else should be provided to the customer. Proactive needs to be proactive. And when you want the agent to be proactive, you don't want the agents to hallucinate. You want it, for instance, to give a recommendation based on good old AI built on models that Helps you recommend one product, one color, one type of jacket versus another, one type of pants versus another, or what else? And so any type of business, would that be in finance, would that be in healthcare, would that be in retail, would that be in manufacturing, they need to combine the power of. Les the power of GAI with the data, their metrics, they are good on models in order to create things that make sense for them, not just for the eye. Well, there's a pretty wide breadth of customers you work with. Give us a sense of the breadth of customers. What are we talking about here? Yeah, so yeah, the business is More than a decade old and we grew into uh having 700 customers, enterprise customers, um, and our business is around 300 million of IR. So our customers are for a lot of them, well 14 2000 companies and names come on, yeah, so some names include, uh, uh, BNP Pariba, Morgan Stanley, um, Fier, uh, Farms, which makes might not resonate with. UK people, because you, you don't chicken, chicken, chicken, a lot of chicken, they make so much chicken and exactly at GE Aviation, they don't make chicken, but they fly definitely not chicken, exactly. And so all of those companies have in common that they actually Generate and can generate lots of data because they are banks because they are healthcare organizations, because their manufacturing processes are very complicated and generate lots of data, and they also have in common the fact that they can derive lots of value in AI because you have lots of processes you can optimize and you can optimize them because in the manufacturing line you've got someone that may spend one hour a day going through some logs in order to write a log report of the day. And this person is paid a good amount of money just to do something in the very same thing every day, and AI can automate that and those are the type of things that our customers are building with our platform. Um, it is uh something that is valuable for them because healthcare companies, they manage lots of data with patients in order to understand what you, what kind of drug they could actually um. Uh, they could actually work on the market in order to understand what the actual use of the drug already on the market that they could better understand and that they do lots of surveys, lots of texts, and a good amount of their workforce is working on analyzing patient interview and patient data in order to trans that into something they can then use in order to make some determination, and this can be automated with AI. And so those things are small, each of those, but like those are the hours and hours that AI will actually automate, and I think that is the concrete version of AI that would transform the enterprise. Well, one of the things I find really interesting about all of these different use cases is pharmaceuticals are very different from aviation applications, which is very different from chicken, right? So what is the thing that binds them all together? What is true in every case, if anything? What is true in every case is that the people that actually in order to actually create a good AI project, you need first to understand the context because many of those companies are building fairly sophisticated products. Why? Because they exist for decades and decades. There is a way actually to do chicken, a way to build an airplane engine. There is a way to do finance. There is a way to actually push a drag on the market and They actually have lots of knowledge and they need to translate this knowledge and so you need the context of the business in order for the EI implication to make sense. And so for that you need to enable people that are those domain experts. The second thing they've got in common is that they've got large operations that do work, so you don't want actually AI to break things up. So the main risk of AI is that you flip the switch, you build a model, you let it run something, you build an agent and so forth, and if it's not good enough. Uh, best case just distract people, which is never good. No one loves distraction. I do love distraction, but not when I work. Or actually I love distraction when I work, but I should not. Whatever. You want the right amount of distraction at the right time. Exactly. So worst case is just distraction and Best case is discussion and worst case they actually break something. They make a customer service poor. They actually create an issue that you wouldn't have before. And so consequences which yeah, which should have practical consequences. And so the the the thing that is key for them is that. When they run AI systems and when they get more and more sophisticated, they actually need to just build more and more tests, essentially speaking to focus a lot on the quality of those systems and the issue when using generative AI at scale or using agents in the enterprise is today not so much the relative quality between 1 and 03 or whatever else. Of course it matters, but it's. And from my perspective and not just that is the fact that in order to use them for real, you need to build them, test them, connect them to your actual data and so forth in a way that is. That require some work. Now, one of the things that is talked about a lot is AI being hyped or overhyped. How do you help customers differentiate between what will really work and what's noise? I think that the perspective from our customer is winning. The, the, um, well, a couple of months ago, um, we gathered some of our customers and that would be mostly chief data officer for instance of various, um, large organizations, um. And one of their concerns was that essentially speaking in their company, lots of people were talking about AI. The management team of the company was talking about AI. The board of the company was talking about AI, and so they were like, OK, we actually have to do more AI. Everyone's talking about it, so we have to do it or not, blah blah, to manage it. And so one of our next step is that. In order to understand better, we actually try to survey CEOs themselves again from various organizations across Europe and US and various sides of the company, but like, OK, what do they actually think about AI? And indeed part of the eye and part of the thing that we learned from the survey is that many CEOs realize that they have to do things about the eye and are almost to some extent fearful of losing their job or their position. Because of AI, because of not doing enough, like 80% were actually fearful of that. And at the same time they were conscious about like regulation and the impact of regulation, and we're aware that for 30% of them that some projects of AI were canceling the company because of regulations. And so what I, what I think is that part of the hype is because there is this realization that it's a moment, it's a moment where a lot of organizations will actually transform their productivity. Where the one that will go faster to AI will actually potentially be a big winner, but also where people that go too fast and actually create a risk in their business could actually also be the first losers because they would just essentially open themselves to the critique coming either from their customers or shareholders or whatsoever to the risks and consequences of AI. So from the perspective of leadership in the organization, depending on. The way you look at life, it could actually be either a win-win type of situation or a lose-lose type of situation because you might lose on both and if you go too fast, you lose because people will yell at you that you're a responsible user of AI or if you go too slowly, well, exactly. And then, well, you could be replaced as as a leader by the next generation of CEOs that would be AI first, whatever that means. Now, I'm gonna come to you all for questions in a moment, but Florian, the IU has reached quite a bit of scale and has long been rumored as an IPO candidate. Anything I need to know? In front of all these people. Oh yeah, of course, that's the type of place where you make revelation about IPO. Yeah, I guess it's been the question all day. Yeah, yeah, meaning we built as many companies, we, we, we've built ourselves to our public readiness and we brought together in the last few, um, in the last few quarters, actually, um, quite a few seasoned people in the next, uh, uh, a CFO, a CRO, a chief people officer, a GC coming from uh Twitter, Zoom. Cisco and so forth, and which is I think what you need in order to scale and grow a business. Uh, a related question uh can be uh in which country and those kind of things that you haven't asked, but I can see you please continue, but indeed as a company we, um, as a company and I think for many companies of our generation, we decided fairly early on to, um. Uh, essentially get, uh, US investors and uh focus our business in the US even if, uh, as a company we are very nuanced because we, uh, operate our product from Paris. I'm based in London and the company investor based in the US. Any questions for Florian? It's not, I can keep going in the back. Oh, hello there. I think then, co-founder and CEO of Nana where we do home-based digital diagnostics using AI and applied machine learning. Lauren, I have a question because you guys are kind of horizontal across the enterprises like an AI developer of different project needs. So I imagine. Your success with your enterprise customers actually comes down to their data sets and what the project they want to develop. How do you take those learnings from one enterprise and one sort of industry vertical to others, because obviously you can't take that data with you for your next project. Oh yeah, of course not. And because our product enable our customers to differentiate by building their own models, their own AI and so forth, it's not desirable. And actually many of our customers operate their IT in a very nuanced way with things on the cloud, but they're on VPCs, things on Prem, and parts of our value proposition must be actually to be very agnostic to that, and then keep the data where it is, whatever cloud, whatever data platform, like, yeah, keep it, keep it, we don't want it and uh. So what we, what we do though is uh focus on the ease of use so that people in the business can actually use the platform and actually build the solution and accelerators so that the most common business needs can actually be done faster on the platform, uh, for instance, um. Well, marketing mix modeling, that's something that every company needs to do, that every pharma company needs to do, every retail company needs to do, uh, and in the grand scheme of things, no one really cares to differentiate that much because of that, because it's a bit of plundering. Um, if you're honest, so, well, very smart, but like still, and so yeah, they are very happy to take something that is off the shelves if they can adapt it to their own data because many of the situation, the issue of like of the shelf solution from the perspective of the enterprise and especially the larger one is that it does not really work for the mix of their data, their constraints, the code increase the operate in, and that's kind of like something they can do with our platform and the way we think about it. Any other questions for Florian? I can keep going. What do you think Is the most misunderstood thing about enterprise AI right now. Hm. I think that's the If I, if I reflect about the AI in the last 10 to 15 years, one thing that has been constant is change, as in if you're the, if you're an enterprise, um, you can look at all of the technology popping in and lots of these core tech technology is actually coming from the, from the Bay Area. And in fact this technology keeps changing, meaning the best database, the best models, the best, I don't know, open source, whatever that can be useful, you've got something new every 2 or 3 years. And so the issue for the enterprise is that an enterprise is also something that is stable and needs to accommodate change in a very specific way. So something that is misunderstood is that the The reason why entropy succeed in AI and so forth will not be that much based of that technology, but whether they build a culture, they build a framework, they build a way to think or integrate those technology so that they get it slightly faster than the other and slightly better for their own business. And that relates to a little bit of framework or platform like ourselves perhaps, but also to the culture of the company, and I think that's what's potentially misunderstood about this. ultimately, the success will be cultural. Well, and sort of in this vein, what does every single person who's an executive at a company need to know about enterprise AI in 2025? Um, I think that's what they. What they need to know is um hm. I think they need to understand what's magic versus not. They need to try it for themselves. I think they need to understand who is in the business doing it versus not. Uh, in the survey we did, I think that more than again 80% of the CU had the perspective they had a good grasp of what was happening in AI in their company, but I'm sure, I'm not sure I believe it. The same CEO were also saying that. So they're reporting to you, they have a good grasp. You're sort of sitting here saying what is a good gras just they've got the perspective of like the things that were coming to them. And they also have the perspective funnily enough that uh and I think it was a very high number like 90% of them plus were considering that AI could do a better job as at least one of the above member. Yeah, well, well, I don't know, maybe they all have like very, very big board, so law of the statistics say that in a very big group you always have a stupid person. I don't know. Yeah, say that sentence did not. I thought it was going to end. I'm not sure if they understood what the answer to this question because they also participate with the board, so it could be applied to. But so I think that it's also understanding what what we actually delegate or not we are having a good understanding of the limit of this technology and how to, how to think about it is probably something they should do and by getting a eye to their business, not just using it casually through the GPT in order to prep their next interview or their next meeting. Now we're gonna end somewhere that I know is central to JI2. Everyday AI. What does it mean and why does it matter to the future of AI as a whole. Yeah, we, we focused uh uh quite a bit of messaging or even build events around the topic of everyday AI as in the moment where AI and then we started that back like 7 years ago, back to. This idea that at some point AI would be so pervasive in our day to day lives that we should stop talking about it because today we talk a lot about it because it's new, but like ideally at some point we stop talking about AI because it would be an everyday thing. I think that with the advent of a generative AI and so GPT and so forth, we made a step toward this because indeed uh 12 years ago AI was a funny word of mad scientists, so. I was very, very careful before saying AI today like everyone probably in this room has done some. GPT today we got a we got a full room of hands earlier on. So a lot, a lot has changed and you've watched that trajectory. Thank you so much for taking the time, Florian.

Martin Casado, General Partner, Andreessen Horowitz, Dawn Song, Professor, Computer Science; Co-director, The Berkeley Center for Responsible, Decentralized Intelligence, University of California, Berkeley

Brainstorm AI 2024: How Much Should We Regulate AI

Martin Casado, General Partner, Andreessen Horowitz, Dawn Song, Professor, Computer Science

Hi, folks. Thanks for being with us. Thanks for having us. Yeah, we figured we'd end with a nice lighthearted topic, you know. Uh, happy to help. Yeah. Oh, yeah, of course. Of course. Um, let me just level set here. Does a, I need to be regulated? Don? Oh, I think our answer is, is very similar, but I'll probably present a little bit different than that. You did, which is, um, a, I is a software system. Software system is regulated by rules that have been developed for the last 30 years. They're very robust. They've been part of an ongoing discourse. Um, and so in as much as software is regulated, A I already is regulated and then there's a question of, does it need new regulation? And the answer to that is we'd have to understand what is new about it, which is still a research question. And so until we understand that I would say if you regulated it, you may be regulating the wrong thing if you did it too early. And that could be, you know, even more negative than if you didn't regulate it. So, my view is we have existing robust regulatory regime, it is sufficient for now. And if we understand, you know, that A I is different in some way, we should, we should evolve that, but not until we understand that Dawn. Does that sound like responsible A I? Ok. So I can, I, a lot of that was cribbed by the way, from a lot of that was cribbed from a paper that Don wrote by the way. So I think so. Ok, so, so first let me summarize what is the current lay of land, please do. So if you ask, if you ask this question to the policy makers, they essentially already, you know, speak with their act. So I called this year, we have a certain proliferation of A I belt. So if you look at the federal level, there are about 100 and 20 a belts in flight. And if you look at the state level, there are at least 45 states that are, have A I built in flight about around 600 of them. So it's crazy. So I would say that if you ask this question, there's a madness about A I regulation. Um and going, going back to what my T said, I think there is really um a lot of nuance that we need to look at when we do A I regulation if we don't do it. Well, then for example, if we do it in a way which is mostly what current A I regulation is we have a lot of problems we can have. Um uh we can have a lot of potential unforeseen negative consequences and also even worse, we can actually have a lot of to prevent disasters outcomes. And also we can have fragmentation in community, which is what we have seen unfortunately through, you know, the recent process of some of the bills that will. Um so that's why actually recently we launched a new proposal with a number of leading A I researchers called on the Path for Science and evidence space A I policy, which is excited to address this question. Should we regulate A I or not? How do we even answer this question? So here, then the answer is very simple. It should be science and evidence based. We don't just jump out of the hat whether we should today, we think we should regulate A I tomorrow. We think no, we shouldn't, we should have science and evidence to speak for us. And so that's what the key of the proposal is about is that a policy should be informed by science and evidence and we should do science and evidence based approach for A I policy. And also I strongly believe that this is the best approach forward for us to develop the best A I policy and also the best approach forward to unite the community to reduce recommendation and lead us to a safer A I world. Now, we we are sitting right now in the State of California, we three are residents of the State of California. So let me start at the state level and then we can talk about the federal level if you don't mind. Um, we obviously had SB 1047 come and go as a, a candidly, a flashpoint for the industry was that science and evidence based. Uh, and by the way, that's hardly the only bill that went through uh uh the California. So that's a very good question. So that's actually the reason, I mean, now the reason, but that's one of the motivation is from what we have seen in the process again, like I said, uh so currently most of this A I regulation effort is quite a hoc you can talk about right, the different thresholds. But a lot of these things, we need better, more rigorous approach that gives us the science and evidence and also to really analyze. So, for example, in the proposal that I mentioned, we actually proposed five priorities for A I policy and we propose how you should actually analyze A I risk, it should be risk. I actually I wrote them down. Don it's understand risks, increase transparency, develop early warning mechanisms, develop technical mitigation and defense mechanisms and trust to reduce fragmentation. Those are the five of the proposal, right? Great. Thank you. So, so I think I see SB 1047 is a great example for us to talk about because you know, there are so many policies. So why is this the one that got people up in arms? Right? And so I'll tell you why, which is um again, we have 30 years of discourse on regulation and policy in tech, right? And so we kind of understand basic principles, we kind of understand the basic paradigms by which we apply them. And SB 1047 was a paradigmatic shift in the following way, which it actually included something called developer liability and what developer liability means, which is like if I'm building a piece of software and somebody else uses it and that causes some catastrophic event that I am liable now in like 40 years or 30 years of tech policy, we've never done this and, and maybe, maybe it's something worth doing with A I maybe, but it's a paradigmatic shift that is divergent from 30 to 40 years of policy. And so we're like, well, wait, you know, time out, like, listen, if you want to do policy fine, we kind of understand policy. This is a discourse that's been going on for a long time. You've got a lot of experts, but you're not listening to any of that, you're doing something entirely new and, and based on, you know, as don, who's a world expert on this would say no evidence. And so I would say, listen, it's a very important problem A I policy. It's a very important problem and like, listen, we can even do spurious policies if like we want to because that's what policymakers do and they're going to do spurious policies, like we're going to definitely see that happen. But, but please, let's not create a new paradigm in policy that we don't understand the adverse effects of without actually understanding the risks. And, and the reason is, is we're going to set a precedent and we may end up like policing entirely the wrong thing. And so again, there's a lot of like, there's a lot of, um, new laws and policy being proposed all of the time, but certain ones of them are, are, are particularly pernicious and this was one of them for exactly this reason. And so it, you know, Governor Newsom vetoed it. So it's, it's, it's a moot point. But where do we go from here, Martin? I mean, do we think that we're actually going to take the big step back and do what you're saying? So, I, I think they are very legitimate efforts that are tangible and near term and like winding their way through the courts, right? Things like copyright, I think is incredibly legitimate and we should understand it. Things like child protections are incredibly gate and we should understand it. And so everybody agrees on this and there's consensus and so we should focus on those. So let's focus on the tangible ones that we know are real. A lot of the SB 147 like the proponents are X risk people, like they're like, oh A I is going to take over the world. So we need to put in these precautions that don't make any sense, right? And so I would say, like, listen, let's like, put aside the science fiction, let's put aside like this kind of existentialist stuff, let's focus on stuff that we know is kind of near term and real and impacts people's day to day lives and, and, and by and large, I think that's actually what is happening. I think that we've kind of regained some sense. It's been five years in this journey. Like we as far as we can tell like this A I stuff is actually pretty safe and like we're actually focusing on tangible practical near term regulation. Um OK, so again, coming back, actually, we uh again in the proposal, we made five priorities and uh and the number one actually is to understand A I risk. And actually in there, we actually propose that we do need to understand A I risk in a very comprehensive way. So there are all the risks that my men, but there are also longer term risks. So I think people need to, right? I think it's helpful for the society to do comprehensive study, to analyze, to understand their risk. By now, I think as a as I mentioned that we want to be science and evidence based. So the first step is that we need to better understand the science and we need to collect the evidence. And so I think it is important that we study a wide ranging different types of risks, but we need to understand them. And then the policy needs to be essentially need to be appropriate to the, to the level of understanding that, that we have. I, I think it's actually, um so a lot of people that have been following this don't know how remarkable it is that dawns up here. And so one thing about like these policy wars in the past like 20 years ago is like the forefront was academia and hobbyists and we had these robust debates. And what's been very remarkable about the A I one is like when SB 104 and seven came, like, like academia was largely silent or hadn't weighed in and so forth. And so I think another thing that's been fantastic about this first wave of policy. It is start of the conversation and was brought in stakeholders that really, I would say are far more neutral than say myself, I'm a venture capitalist, right? So clearly, I've got a bias, right? So I should be a voice, but I should not drive the conversation though. In that case, I actually was driving the conversation, right? So I think we're now at a great place where we've got people like Don who are a real expert who are like, hey, listen, like this is a very important problem like, and I think academia is engaged and I think that's actually where we should start moving these discussions to. And so the very concrete next step, let's all fund don this out for us pro pro I get it. So, so let's um, I, I do want to come to the audience for questions in a minute, but II, I want to make the jump up to the federal level. OK. We're gonna have a change in the White House very imminently. The, the outgoing president outlined an A I framework. The incoming one is certainly planning to do just that. How are we going to stick to our science and evidence based guns in this environment? Don. Um That's a very good question, I think especially in the changing environment. I think it's even more important that we focus on like the science, evidence based approach. And uh in fact, we are discussing at all levels um including the federal level and even including like the National Academy and, and uh so, so far, actually, this proposal has received a huge positive feedback. We uh it's only last maybe about two months that we already have volunteer contributors from about 200 different institutions actually um volunteer to help uh with this uh with this effort. And also, yeah, we have received like a lot of positive huge support from civil society from a lot of different, different entities and so on. So we really hope that we can bring this as a whole community society effort. I think it's hugely important. It sounds like a plan. All right, let's go to the audience. Nobody knows what's going to happen under Trump when it comes to like a policy. It's like, nobody knows. Right? Like, I don't know, you know, and so like, on one hand, I'm cautiously optimistic because like what happened under Biden was terrible. It was like he had this executive order that made no sense. And then, you know, it was like this kind of all this kind of crazy A I policy stuff on the other side. Like let's be honest, like SB 1047, which I think is probably the worst, like probably the worst bill, I would say the United States personally because of the paradigmatic shift was supported by Elon, right? And so anybody that thinks that they know it's going to happen, I don't think that they really understand the players, it could go either way. So I'm cautiously optimistic. I think it's good to have a change. I think where we're coming from was actually very bad when it came to A I policy, but no idea, you know what it will actually look like. All right. That's, yeah. Yeah. Yeah, we should ask an A I agent about it. All right, let's let's go to the audience for questions, comments about regulation. And policy. Oh, we've stunned them. Look at that. Yes. Right over here, sir. Your name and who you're with? Uh John Durkan, chief technologist with ID A Ireland. Uh So I'd be very interested from the panel's uh point of view, like particularly looking at the EU A I act that came out recently and the perspective that that has and we saw with GDPR how that kind of set regulation around the world, but in the context of the EU A I Act, your kind of feelings on that, has it been too much focused on regulation or is it because it's took a risk based approach? And it's kind of looking at the idea of setting kind of this idea of being more based on research to make the informed decisions as it goes through. So just interested in your view, I know my answer, but I'll let you go. Um I think, right, I mean, eu in general, they do take a lead a lot of these regulatory efforts. Um and now actually still they have this code of practice after it's ongoing. So I think a lot of details are actually still in the, in the progress to figure out exactly like what the company needs to do to comply, what are the recommendations and so on. And I think they are taking a first step and I think it's leading in the, I mean, certainly it's helping making progress in the space but of course, as I mentioned earlier, there's a lot that we actually really do need to study, to figure out very diplomatic. So I, I come from a very, very poor part of Spain actually. Like, I've got it on the DM, which is like the, the Appalachia of Spain. I love it. It's a great place. Um, you know, uh, we have Alcaraz as a tennis player. He's great. We also have a phenomenal founder who started actually a great company in Ja I um and the company is called Manic. His name is Javier and he's done all of this great work. And today as we speak, he's back in Murcia, you know, this, this, you know, it's great part of Spain but like not known for its tech founders and entrepreneurs. Um and he, and, and by the way, magnific is in the gen A I pixel space. So it's like images and videos and stuff like that and he cannot access Sora. So Sora is the video model that was just released by Open A I yesterday, right? And so like you tell me, but from my standpoint, like when you have like a a, you know, a great founder who can't even engage in his own area because you can't access to something that like a company that Open air release. I think that's a strictly bad. So I think that the eu is totally bundling it. I think it's putting itself backwards and I think it's got to change its tune or it's going to get in trouble. But how do you really feel that more questions, please? Yes, we have one way in the back. Your name and who you're with? Hi, I'm Alana Con, uh, with, uh, hacker one and Admission. I'm a former federal regulator. So I understand the instinct to say we don't need anything. We're already regulated. We've got this unlock. Um, but the federal regulation process, specifically the rule making process, it does take science and evidence into consideration because of course, there has to be notice and comment and you have to be able to provide that information which they take into consideration when they do those rules in the absence while we're off here saying we don't need anything. The Eu Singapore, everyone else is essentially regulating for us. So like privacy, we don't have anything here in the US. We've been fighting it for years and so the eu essentially runs our regulation. So is that the right approach from your perspective? And it doesn't make sense to continue to say we don't need anything and we'll let all these other countries do it for us. Great question. Thank you. Um So first, I don't think we are saying that we don't need anything right? I mean, the the proposal for a science and evidence based approach for a policy does not mean that we don't need anything. Actually, in fact, in the proposal, we actually talk about certain priorities for regulation, including for example, transparency, average event reporting. So we can write to like for post deployment harm and do this average event reporting and so on. So nowhere we say that no means no, no regulation. I think it's more just providing a more grounded uh approach that as grounded in science and evidence. And also again, like I said, and also actually in the end, we actually have um additional cultural action proposal is actually to have the community come together to discuss and propose what we call the candidates um air policies for we call the conditional response so that the community can discuss and what uh conditions, what policy would be appropriate. It's just in this way that we can have um you have multi hold open dialogue discussion in a low stake environment. So we hope we hope that this approach actually can lead to better consensus building process for better policy. Understood, Martin, can I take a quick, I really appreciate this question because I do think it gets to the heart of it. So um I think that our posture as a nation shouldn't change and it's changing. And I want to give you an analog which is like the internet. So I was in the national lab during basically the advent of the web and the internet, right? And we had this new stuff and like very quickly, like it was clear this was marginally different. We had computer worms, which we didn't have before. We had this notion of asymmetry, which means we are more vulnerable than countries that didn't use it. So clearly, it was marginally different, which we don't have for a I. So what do we do as a nation? We invested heavily in understanding it and building out capability. Like we became the leaders of the internet. We turn it into a discipline and like, and yes, of course, there's an entire cybersecurity. You know, like I've known Don for 20 years, he's like one of the preeminence researchers in cybersecurity. So it's not saying that we ignore it and ignore policy since we've become the leaders, we understand it the best and we make the most practical solutions that's not what's happening with A I I mean, the eu is doing whatever it's doing right. It's like, oh, there's this new thing we're regulate it and for us, for me, which has been so shocking is like academia wasn't even involved in the discussions. They've been silent. And so I'm saying yes, we need to regulate, yes, we need to understand, but we need to change our posture to engage research, invest understand the marginal differences. And then yes, let's do like we've done in the past, which I think is the right thing for the country to wrap this up and thank you for that to wrap this up. How are we feeling about where we go from here in in, in uh in one or two words, yours is cautiously optimistic. Yeah, Dawn Yes. So we need to focus on science and evidence and we need to advance our scientific understanding. Got it. All right. A round of applause, please. Thank you very much. I appreciate it. Thank you. Thank you. Thanks folks.

Liz Reid, Vice President, Search, Google Interviewer: Jeremy Kahn, Fortune

Brainstorm AI 2024: Google’s Strategies For Search

Liz. Thanks for being here with us. Thank you for having me. It's a great room. I can't see you at the back. But um I want to warn everyone, I'm gonna go to questions pretty early in the session. So please think of your questions. Um But first I want to ask you was, there's been a lot of reporting out there in the last few weeks that young people in particular are starting to Google things less. They're not going to traditional Google search. They're using some kind of A I chatbot or they're using these new A I enabled search engines, things like perplexity. What, how are you viewing this trend at Google? And how do you sort of want to trying to combat that potential loss of market share? Yeah. So I mean, there's always been different options with search and we welcome that. Um I think what we're really focused on with search is the user um and really helping that user find information. Um The truth is that there are not just billions of questions that people come to Google for. There's billions of questions people don't even ask and they don't ask because it's too hard, they don't ask because they're not confident they can get that answer. Um And so we really think about and search, how do we make it so that you can really ask anything you're curious about? And then it really feels effortless that it's so easy to do that. You don't think twice before you do that. And we think there's a huge opportunity ahead on that. Interesting. You mentioned, you know, people come to search because they need answers and they want quality information. And there's a lot of concern right now about what's happening to the information ecosystem on the web. There was already a problem sort of even before ja I exploded about search engine Optim A and whether that was degrading the quality of information that was available on the internet, then there's a, you know, a doubling of that concern with generative A I and the fear that's producing a lot of, you know, a I quote unquote swap that's out there. And how are you, how are you sort of seeking to address this problem at Google? Is it, is that our concern now that you're OK? You know, we have a great search tool, but it's got to be able to surface information that's, that's of value to people. So ensuring that we surface high quality information is, is definitely paramount and it's a huge reason that people have trusted search. Um This, this challenge has existed since the beginning of search, right? Um People have constantly created amazing content for the web and they've created poor quality um content in many ways. It's a, it's a cat and mouse game with those who wish to create um spam across. Um I would say A I generated content isn't by itself intrinsically bad, right? You can use A I um to really help people write and create and produce amazing information. Um But there's also this challenge of scale content abuse. Um And this is something we take very seriously. It's something we've continued to work on for many years. That's the level of spam and search is very low. Um And we'll stay focused on it because it's absolutely paramount. Um You've now rolled out these A I interviews, sorry A I overviews pretty widely that's created a lot of concern on publishers, the part of people and other businesses that really depend on search traffic coming from Google, you know, to their own websites where they seek to monetize that often through advertising. What, what are you saying to publishers about where this technology is heading? Because there's a lot of concern in the industry about, about what the effect of this is going to be? Yeah. So I think we have a long history in search and having a value exchange with the ecosystem and we've sent billions of clicks for a long time. Um But one of the things that there's sort of this common misconception is that people either want answers or they want links. Um That's not actually what we find from research. Um People wanna get started, but they actually really want to hear from other people. They're interested in their perspective, they're interested in their understanding of the space. And so we really tried to build A I overviews and continue to iterate them on a way that enables people to get started, but then continue to connect to the web. Um And we see that that's one of the things people love um about A I overviews is this ability to have that hybrid to understand and then to continue on in your journey. And I think that's only going to continue. We see that people, especially younger users really want to hear from the authentic voices, they want to hear from the people they trust and that's part of what makes it special, connecting to the web. And so that's at the heart of how we think about in the A I transformation still enabling that mission of connecting people to the best of the web. And how can actually A I make that even better in allowing people to connect to truly the best of the web? Are people still clicking links with A I overviews? We do see people click both within the A I overviews as well as on the rest of the page. And in fact, when we see A I overviews, we see that the clicks that go out to the web are of higher quality. People spend more time on the sites they go because these are really giving the information that they want. All right questions from the audience who's got a question right here. This gentleman has a question that hand went up very quickly. Um Wait till the mic gets to you if you can let us know who you are. Yeah, Josh Legasse with insignia. I didn't even know that was a thing. It just started popping up and I love using this. I'm glad to hear that. That's great to hear. Uh agree with the clicking back and forth. My question is, is it learning from me and feeding back answers to me or is that learning from everyone and feeding back answers to me or is there something else that's happening? Um So I think there's different levels of, of your question, there's sort of um what is the level of contextualization that we think about in these spaces? Um And there's some understanding of like, how do we interpret the query that can be more contextual. Um But in general, we use um throughout search, not just in A I reviews but broadly, um we try and learn from very privacy safe way um giving people controls, but we can learn a lot. This is how people learn these are the best sites to go to, right, is often based on user feedback. In addition to our high quality signals. And so we try to do it in a way that's very clear that gives people agency. Um but that we can actually improve based on the usage of the product. Other questions from the audience. Oh How about this woman back here? There we go, wait for the panel. Hi, I'm Julia Martinson with service. Now, um I mean, Google search has obviously been, you know, the best for a long time in terms of search and, and also providing the right results, you know, you know, ads kinda maybe, you know, for some more cumbersome, for more, some more enjoyable. Um I'm wondering with seeing how LL MS went also into more domain specific small language models. I wonder if um maybe, you know, a road map question, would it be ever, could we ever see a domain specific research basically where you're also kinda going down that level and, and making it more relevant for that specific domain you're in. Um I think we've continued to look at um uh different domains in different ways. I mean, if you come to search today, we have um a mode that enables you to do images, right? If you really want to go deep in images, um we have the ability to go deeper on shopping or on local or on others. Um And I think um that will continue, there will be cases where what really people want is a specialized um understanding of information, I also think with A I the ability to just um personalize the, the responses um evolves in ways that we don't traditionally think a lot of people previously with personalization with ranking, think of it as like, OK, I'm gonna re rank just the site. OK. Um But actually there's a lot of different ways um in which personalization can be useful. OK? Um it starts with basics like some people love video results and some people really do not. OK. Um Can you understand that? Um what's the level of understanding of what you want and responses? Are you an expert in that domain? And we should assume so or are you, you know, sort of trying to help your five year old with a concept that you don't actually know very much and explain it to her? And how can you do that? Um Are you on the middle of a long journey or on a shorter journey? Do you like to scan information quickly? Are you an audio learner? And then we should make it easier to understand information in that form? Um And so I think besides getting better within domains, there's just a huge opportunity to start to understand like what does it really mean to make information effortless if you go back to the mission? Um Most people like if they repeat the Google mission kind of start stop halfway through, they go organize the world's information and that's kind of where they stop. But there's this part at the end about universally accessible and useful. Like if we show you information that's organized, but it is not useful to you. It's not really that useful, right overall. And so I think there's an opportunity with A I to rethink what it means to give information in a way that's truly useful to you that truly feels effortless to consume and interact with. There's a lot of people really like the A I overviews and a lot of business is use Google search either on their websites to allow customers to search for things or they use it some internally on their own information. Is there the ability to kind of take the A I overview kind of technology and have that work on your own site or on your own information? Yeah. Um So there's a bunch of different offerings that Google Cloud has. Um uh And I will confess that I'm not the expert in Google Cloud. So you may want to get additional information on this. Um But at a high level, um we both have services that allow you to have your own data um basically ground a response in the data on your site. Um There's also a service that allows grounding with search so that you can get the best of sort of search his understanding of what's high quality information in your product. Um And so cloud offers a variety of different surfaces so that we can actually take some of the, the key underlying LL MS and understanding about how to get high quality information and make it available for others. I know there was at least one more audience question this gentleman right there. Let's get him in. Um, and then maybe I'll have time for one more of my own. But hi, my name is Matt. So I'm involved in running Ja I at mcdonald's, I'm curious, Google's famously brought search together in like one bar that you can do everything and, and yet now you're sort of splintering it again. You've got your Gemini product, you've got your Gemini app. Where do you see this going? Do you see it sort of coming back to one interface or do you see sort of the chat interface and the search interface? That's a great question. I swear. I didn't set you up for that because that was the other question I was going to ask is are the products going to blur together? You have Gemini as a separate product from search is that, are those going to be kind of recombined? I don't think we honestly know what the future has. We're pretty early in the journey. I do think that one of the things I think about a lot is how do you meet users where they are? And so, so if they come to search, can we give them the information the form they want if they choose to use Gemini. Can they do that? Um If you actually go back? And like long enough ago, people ask this question about youtube and Google's video search. They asked this question about Google Maps and um local search on search. Like, wait, where should I search for a restaurant on search or maps? Should we just merge search and maps together? Really? What you want to do is make it e like whatever is easiest for the user to come forth. Um The other thing that I would add in that space is that I think um you'd mentioned the keyword box, OK? Actually, search is evolving. So the keyword box is only a component, right? We introduce voice, we introduce hopefully all of you know what lens is. If not, you should come find me afterwards, that ability to search for what you see. And I think the space of information is going much more multimodality in its input. And the reason is because, you know, we're humans that existed a long, long time before technology and the way that we understand the world and we think about the world is based on a lot of different senses, right? It's based on what we see and what we hear and how we talk across. And so if you want to make information effortless, we're going to continue and search to think about what is the most natural way you would express if you had no constraints. If you assume tech was not a barrier. And you just asked your friend as easy as possible, how can you have that experience with search? So, so the tech really comes to you instead of you having to translate your need to the tech. Well, you've got this Google has, as everyone knows this department of justice case that's out there and there's just been this remedy phase and some of the suggested remedies involve the separation of search from other products. Are you guys actually sort of planning internally for, if you had to implement one of those remedies, what that would look like? So I don't have more really that I can comment on regards to this. What I will say about search is that we have consistently found that users choose search because they really find it the most helpful. And I think we're going to continue to really focus from, from my point of view on how do we help users? Got it. There's a lot of talk about sort of agentic A I and people may be using other agents increasingly, you might have people coming to Google search that are not actually human. It's, you know, you have an agent coming. How are you thinking about that? The idea that the user may not actually be a person, I think in general, you know, our, our goal at the end of the day is to help the user um as people interface or as as agents interface with search in different ways. Um I think we will always try and evolve it to the question about like, OK, how do you help the agent? So the agent helps the user. But at the end of the day, we're really focused on helping people. Um and we'll continue to innovate in that space, right? And can you give us any sense of, you know, road map is that we're going to see more multimodality? What are we going to see from search going forward? Um I think you will definitely see more multimodality. Um I think in general you will continue to see this evolution such that it feels like accessing information, feels just much easier, it feels more effortless, it feels like the question that you actually have in your mind. Um Is the question you get to ask. You don't have to break it down. You don't have to translate. You don't have to worry about what language you speak. You don't have to worry about how you learn best. Um And that search just becomes much more accessible in a new and exciting way. Great. Fantastic. That's all the time we have for uh for it today. But uh thank you to Liz and thank you all for listening.

Manohar Paluri, Vice President, Engineering for Gen AI, Meta Interviewer: Jason Del Rey, Fortune  

Brainstorm AI 2024: The Challenge of Defining AI Transparency

 Manohar Paluri, Vice President, Engineering for Gen AI, Meta Interviewer: Jason Del Rey, Fortune  

You're gonna help me with this pivot today after that performance. It's hard to match up, man. Yeah, we're gonna, we're gonna, we're gonna do our best here. So let, let's jump right in. Um Meta with Lama has a very distinct point of view on this topic. Can you explain why open your version of open? We won't get into the, is it really, really open? But um we could have those conversations outside but why your version of open and how do you think about the strength of open versus what some people see as the risks? It's a very often question that you get when you lead Lama and uh uh last year, when we faced this question, the answer wasn't super clear to us, but philosophically, uh we always believed open is the way to go all the way from open compute porch. And many of the things that meta has built uh Lama being the latest incarnation of it. I want to answer it in two ways. There is a particular way of thinking about A I development over the past four or five decades. Every time we overestimate what we can achieve with these models. And with the progress in A I, and we feel it's something to be enclosed and built upon and that's where the value is. But every time we hit a wall in unlocking General Intelligence, uh unlocking A I that actually need not be customized can be used out of the box. If you really look at the progress of A I today. In fact, even the 3.3 launch that we just did on Friday, there are multiple variations of those models uh and customizations of that that have already launched. And part of that reason is because it's open because people are able to build on top of it, customize it. So it's ridiculous for me to actually think of a world where we enclose A I and say you have an API for intelligence. Instead we, I genuinely believe the progress in A I over the coming decades is only going to happen if we actually go the open route where everybody can customize it, access it, um deploy various applications on top of it all the way from using an API but also customizing the weights. And that's where Lama uh vision stems from. And it actually has a tremendous amount of benefit for meta as well. So it has, it benefits the world, it benefits the developers, it benefits meta. So that's to me is the reason why we are open. So it's II I can understand seeing a A no brainer especially in the competitive landscape. And I and I get the, the speed to improvement and speed to innovation. Um We haven't really talked about the downsides and um you know, there, there are many out there that, you know, depending on your point of view or if you're a government official, you might see. Right. And so, you know, there's a report report in the fall that researchers linked uh to the Chinese military, we're building on top of an early version of L A. Um Again, I think the response was that's against our policy or our rules um still happening. So why should the world trusts meta with this open source approach with such powerful technology? When we know whether you know whatever your views of Chinese military are there, there could be actors out there who might not use it in the ways designed or intended. Um This particular one. so first and foremost, we should be very grounded on, on how we see the world uh with the data that we have and with the facts that we have, right, the competitive landscape of A I is pretty amazing in terms of the progress we have been able to make across the world. This is not a particular country. This is basically uh if you if you really look at the success of building the best models today, it has four components to it, this talent that you need to be able to build these models, there is compute which is flops that you put into these models, especially the flagship models are the largest large scale models that are actually used to distill and build very efficient models. And there is data, uh Lama at least has been built on publicly available data and license data. So unique data, the higher the quality, the better. And finally, the distribution which is aligning these models for various applications. If you have these four things, you are actually able to build the best technology and unlock value and the competitive nature is across uh the whole landscape across countries and continents. There is a huge uh competition in terms of building these best models. And to me taking a step back, if you want to play the long game of actually making sure this value accrues to everybody in the world. Again, you go back to how many people are building on top of it. How fast are you building on it and how responsibly and safely are you building it. And if you look at the operating systems that are available today, the safest operating systems are the open systems. So similarly, if you put out a model, there are thousands of people who are redeeming these models, there are thousands of customizations of these models that are much more efficient for hardware, much more safer for various applications. How is that going to happen if it's done by a single company or a single entity, it's going to only happen if everybody, all of the talent across the world are customizing this for applications and building a safer uh future and future that is more accessible to everyone. So that's, that's the real. So, so is the point that they, they're gonna always be use cases that we, we, we don't love but uh we're together, we're gonna build guard rails around that or am I, am I misinterpreting? I have a fundamental way of thinking about it, which is if you, if your router doesn't work, you actually don't go to the, you don't go to Linus Walters to actually complain about the router. You basically go to the manufacturer of the company to complain about the router. What is working. What is not similarly when I think about A I and these foundation models, that layer is not where you actually control or where you think about what is working or what is not what applications you actually enable on top of these models is where you have to have checks and balances. And to me that is the real place where you actually have significant value, both upside and downside. And we need to look at it from a responsibility and ethics point of view. That's why when you think about Lama, what we did is at a model level, it's so hard to compress all of the policies of the world into one model. It's not possible. So at a system level, when you build a system, you have this input and output guards, what does that enable you? That what enables you is that you can have the model but you have certain questions that you want to answer, you have certain answers that you want to curb. And that flexibility is what you need to give at the application level. And then the policies that are more regional cultural geopolitical can kick in and say, OK, this is how we are going to deploy these models and this is how we think about safety and responsibility. Let's shift gears real quick. I want to talk about some of the consumer applications of wa and meta A I so um meta A I started, you, you would know the exact timeline started showing up in all our apps, Facebook, Instagram, whatsapp, right in the search bar. Um What have you learned from? You know, I think what some people saw is just, you know, slap, slapping that on there and see seeing what happened like what, what is the feedback been? And then I wanna talk about the scale of, of that use case. So we've been blown away by the adoption of me. We are close to 600 million monthly actives at this point across all of the surfaces that you talked about. But at the heart of what we are, what we are trying to do is Lama is our engine that enables me, I I which is a universal assistant, but it also enables business A is wearables A I which is a Rayan meta glass that you can wear and you can invoke me I I, the assistant and with have a hands free intelligence experience uh created A I and many of the other A I that people can customize towards their liking, whether it's a particular fan following they have on a particular character and so on and so forth. So the goal for us with this foundation models is to enable all of these product experiences. So humans, human connection is stronger, human agent connection is stronger. And that's kind of like the at the heart of what we are trying to achieve building the future of human connection and the technology needed for it. And Lama becomes the centerpiece and med is our universal assistant experience. So it's been pretty amazing to see how med I gets used across all of this. How do you, I mean, because I, I've struggled to figure out what is the right U use case when I'm, you know, looking for, I don't know, an old high school friend or something. Um And that's probably not the right use case, but it's in that same search bar, right? And so um what, what are the use cases that have been most common or, or maybe surprising. So, so many of the uh so II I kind of have two or three that come to my mind because I always believe what you use is the best way to actually think about the product. Uh imagine is one of the experiences that you can use for in Medi. I, so you can actually imagine anything. And recently like three months ago, we had a birthday cake that I designed with my four year old daughter. You can imagine it. You can add a princess, you can add an Eiffel Tower, You can make it a strawberry or chocolate flavor. And then you have this custom cake and you're like, OK, this is pretty awesome. And then your building and then your cookie had. Yes. No, I didn't cook it. I had some, let's cook it. I wanted the daughter to be happy on birthday. Uh uh So there was one, you can actually invoke my I I in a group. Uh So if you think about how you uh uh use these models and interact with our assistants, you're all, you're, you're naturally in a conversation in a group and you can actually uh invoke me. I I to ask a particular latest score. So you're talking about a whatsapp group in whatsapp group. Uh So I'm an Indian cricket huge cricket fan. We're always talking about what's the latest score stuff? This is this group with, with uh my buddies and then we can ask me for the latest score. It will give you the latest information we can actually have uh some really fun roasting sessions uh for the groups on uh for the people on the group. So these are things that are now on me. You could open uh and you could definitely do it. Yeah, I see an editor who would gladly roast me right now. But these are some exact. So you want social plus this intelligence come together at the heart of it and you're already on whatsapp, you're already on Instagram, you're already on Facebook. So basically, we're bringing me I I and all of these A I in that place. So interactions with friends, interactions with businesses is natural and intelligent. Um I was curious about that 600 million number that I keep hearing about active users. So I asked meta A I like, can you like explain like what is an active user mean or what is an engaged user? And so I won't go through all the, we, we had a long back and forth kind of, you know, insightful. And at the end, uh meta, I told me that perhaps more than half of the 600 million might be not explicit uh engagements where I know I'm engaging A I, but maybe a meta A I is working behind the scenes on a result uh on a search result. And um I was just curious is, is that accurate? And um because I, I know, you know, all the companies are very proud of these active user numbers. And so Um Yeah, I'm, I'm curious, I'm curious for your take on what, whether, whether that sounds about right? That maybe half of that number is sort of not explicit uses, but A I working behind the scenes, let me answer that. Uh So the two answers to it, one is uh technology when it works should be seamless. So when you are actually doing a search query or when you're actually asking a particular answer, you're actually trying to do a thing, whether it's on our app, any of the surfaces, whether it's whatsapp Facebook, you're not necessarily trying to say I want to use search, I want to use Medii, I want to use this. You're trying to get a particular job done. You're trying to solve a particular problem from that context. We're trying to make sure Medi I can understand what your query is and if it requires a language model or if it requires a real time query or if it requires a frame graph access, we are able to do that seamlessly for you. So you can actually get the job done in a much better way than before. That's how we think about it. And going back to the question that you are asking, uh it's really hard to break down which particular intent was search only which particular intent was accessing a frame graph or looking at a particular subs string or asking the language model for latest information and so on. So all of this together is what we are trying to unlock with me. I was going to say when you said two different parts, I thought, I thought that was going to be a really wrong non answer. But we're really saying we should just trust meta A I's answer. Maybe we're, we're, we're actually, the technology is definitely improving day by day. And that's one of the other things if you look at the launches, we have done over the last year, we basically did five launches all the way from Lama three family to 3.3. And if you look at where we started the beginning of the year and towards the end of the year, we have more than 600 million variants of a Lama that people have built on the 600 million monthly actives that we talked about. And me, I was in the fledgling uh part, we just started thinking about me. I, I, so going from uh product experience that doesn't exist to a product experience that's used by these many people. It's pretty amazing. I think we're all at the time. Thank you so much, Mano. Thank you. Thank you for having me.

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