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[🎬 Replay of Arthur Mensch, CEO of Mistral AI at the Adopt AI Summit by Artefact] Watch the interview of Arthur by Vincent LucIAni, CEO and co-founder of Artefact, on "bringing open AI models to the frontier". Arthur Mensch discussed Mistral AI’s rapid development pace, emphasizing their focus on optimizing speed to meet high market demand and attract users. Mistral AI aims to: - Provide developers with tools to modify and specialize models - Offer independence from major cloud providers - Ensure future-proof cloud strategies through on-premises and virtual private cloud deployments - Consolidate offerings with more specialized and high-performance models 👉 Discover the video of Arthur Mensch on the Vision stage at #AdoptAI: https://youtu.bye/km7k030DujI 🎯 Arthur highlighted Mistral AI's commitment to open source and collaboration, contributing to both the French community and the broader #AI landscape. Key points include: - Openness and Collaboration: Using #opendata and partnering with institutions like the BNF (Bibliothèque nationale de France) to incorporate French cultural knowledge into models, essential for shaping future cultural content. - Primary Use Cases: Focusing on developer productivity, knowledge management, and customer service, providing tools for interactive documentation and low-latency responses. - Enhancing Offerings: Planning to improve these tools, ensuring significant gains in efficiency and performance. Read more on our website: https://lnkd.in/eGP4Pfue Looking ahead, Arthur emphasized the challenge of integrating AI into #softwaredevelopment, highlighting the need for continuous improvement and model specialization. Mistral AI’s roadmap includes enhancing model #customization tools, increasing compute capacity, and expanding into #multimodal processing (images, videos, and audio). He stressed the importance of maintaining ambition and fostering bold, transformative adoption of AI, aligning with Mistral AI’s vision of a more open, efficient, and developer-focused AI landscape. 👉 Watch all the #replays of the Adopt AI keynotes and conferences: https://lnkd.in/eDxHcR47 ⭐️ Be also aware of this key announcement between Mistral AI, Artefact and Giskard: As part of the France 2030 initiative, Bpifrance launched a call for projects in October entitled "Digital Commons for Generative Artificial Intelligence". - This call aims to develop specialized language models (#LLMs) tailored to the needs of businesses. - The consortium comprised of Artefact, Giskard and Mistral AI (ArGiMi), in collaboration with the Institut national de l'audiovisuel (INA) and the Bibliothèque nationale de France (Bnf), was selected to lead this initiative.

Transcript

Hi, Arthur. Thanks for being with us today in this amazing event of Adopt I Archer will start by introducing yourself and then we'll ask you a few questions. So you graduated from Polytechnic and OS in 2015 and then you started your career in research. You completed a PhD at Inria in France, and then you started your career and worked three years at Deep Mine on a large language models. Then you started to you, you, you started your own company Mistral one year ago that probably everyone knows about today and you did one of the most amazing seed round that already existed 105 million in June 2023, so almost one year ago now and then a second one 385,000,000 funding in December. Since then you launch already three open source models and two closed. Mistral, small and large. You launch the application on Russia, which is our French charge PT and more recently Codes Hall, which is a function model for developers. So my first question, Archer, will be about this impressive speed. How do you maintain this cadence and for what reason are you such in a hurry and do you think you will maintain this high frequency over the time? Thank you. I mean, we go fast because it's a Fast Market. So that's the and and there's an enormous demand from developers to play around with new models and to to integrate them to make gains on efficiency, on performance. And so we effectively optimize for speed because as a young company it's very useful to get users. Now we are at the stage where we are consolidating a little bit the offer that we have with models that are spanning the entire space. For performance and efficiency and last week we just introduced our first model that is specialized and dedicated to to specific use case, which is developer productivity. So you should expect that we will be consolidating, we will be bringing new models that are even better and does take longer to train. So it will potentially reduce a little bit in the cadence, but the consolidation and the product is going to get better and better. Can you explain a bit more your, your vision and how you differentiate your, you differentiate yourself from the competition. We see the competition is really incredible. What is your positioning in the market and how you differentiate yourself? So we target the developer audience. So we really think that in order to build differentiated applications, the developers should do more than just prompting, should be able to take models, modify them, specialize them to their knowledge, their user base some. Concentration data that they may have produced by their employees and this is actually pretty hard to do. It requires to have some skills and those are the skills that we want to bring to the market. And you really believe in efficiency. So we've produced models that are on the frontier of efficiency. So for a certain size, certain ship, they they produce the best reasoning performance. And this is really core to our values. And I guess the last thing is that we propose and provide independence to our customers from the hyperscalers. Yeah, because it's a costly technology is very much tied to the existing cloud providers. It's great. But it's also great to have alternatives that are independent, that allows to do on Prem deployment, virtual private cloud deployment and basically be future proof in your cloud strategy. And the flexibility we provide to developers is guarantees that. Thank you. So you cannot speak about all the data you use to train your model because it's how we say in French Socrates affair. But can you give us a sense on how you can ensure your clients that you respect all laws regarding intellectual property, GDPR and of course AI act? And sub question to that, we see some players like open area doing some private deals with Lemon, for example. Is it something you want to do in the next coming months? So enrich with private private data. Yeah. So we train on the public domain, we train on, on the open web. We filter everything that is not that that has opted out. For instance, we, we obviously don't train on user data because this is useless when you when you when you train your large language model, you don't want it to be seeing any form of user data. You just wanted to know to have knowledge. And so this is typically something that we filter at the very beginning because this is low quality data. Now on the application part, the applications that we release obviously our GDPR. Compliant they have cookies, so you can see that with the banner and the on the second part of your question, I think there's there's a lot of synergies to be found in between application builders and take content providers and we're effectively in discussion with many of them to be able to bring the value of highly validated human curated content to the products that we sell. Also together Mistral artifact, I'm also Jiska, which is a French society. We receive an important sponsorship from France, the Militant, and we commit together to publish open source model using open data from inner, for example or BNF. How can you share a bit your feeling about that? And how is it important for you to contribute through open source to the French community as well? So I think the reason why we started the company is to bring the field to toward more openness and toward more sharing of information. This is really something that was disappearing from the field starting from 2022 and we wanted to bring it back. And I think to some extent we've managed to do it because many other large companies have actually followed us in that past. So it's very important because we are talking about the technology, which is which shapes culture. It produces content. And as a content producer, you do have an impact on what people read. So you shape the future. And in that respect, it's very important to be able to take some French knowledge, for instance, this is the reason why BNF is part of the consortium and pour it into the model so that it has a good understanding on how we should speak good French and how we should have the French culture. So that's the yeah, this cultural aspect and the and our ideological orientation toward, acceleration toward. Ownership toward having application builders own more of the technology, customized more of the technology. These are the reasons why we we will remain, we have been committed to open source from day one and we will remain committed to it. So we are here at adopted area let's talk a bit about adoption first maybe let's start with the use cases and what what are the most important use cases you see today at miss hall how are they implemented in their organization and what are the ones for you that create the most value. So the first use case and and the reason why we created a code specific model is really developer productivity. Software engineers were the first to adopt the technology and and if you look at the productivity gains that are made across functions, this is really where this is concentrated today. And in a couple of issues there is the the fact that you typically call these models many times over when you do a coding and that requires to have smaller models, so specialized models, that's the reason why we did Coastal. Then you have, I would say two other directions that are already producing productivity gains. The 1st is knowledge management and basically being able to interact with whatever knowledge and documentation your company has in an interactive way. And the second one is customer service. So that allows to have higher quality customer service that is lower latency, that is addressing more specific needs that you're, that is more granular and you are already seeing some very large gains in companies. That have been early adopters and so for these two aspects were effectively building the product that enables that. When you speak about knowledge management, it's mainly Iraq. Today you see many other application of knowledge. I mean, the hike is just a way to do it. There's different part of how you should pour the knowledge into the models. And if you have things that move very, very often, you typically connect the database to the model. If you have a corpus of knowledge that is very that is very extended, you don't want to do rag on it. You probably want to do what we call continuous training. So take your base model, the one that we produce for instance, and pour the specific knowledge that you want to build an application for into that model. So for instance, if you want to. If you want your model to know about the French law, you probably don't want to do rag on it. You want to do this continuous training aspect. It's a it's hard to do, you need science for that, but we're exposing it so that we make it easy. So today we are adopting higher and when I introduce the conference, I said that only one company out of 10 has actually managed successfully to implement AI at scale. Do you share this vision? This is constant. And what would be your key advice to increase the adoption of AI within companies? No. So it's a constant that we share. And the, I would say the, what stands is the in the way is that to get the most value of AI, you need to build software with AI. And when you build software, you need to comply with the constraints of software. So you need to be able to continuously integrate to improve your software over time. And this is actually hard to do because how do you improve an AI system? Well, you need to figure out where it fails. You need to get the data to solve where it fails. And so there's an entire field of managing the lifecycle of model. Starting from a base model, making it specific to your your task, reduce the latency, deploy it with alpha customers, get some feedback on it and improve it over time. And that's the only way you can actually be confident when you deploy your software to your end users. And so I think what really stands is the way in the way is the specialization aspect. This improvement over time aspect is actually unsolved problem. It's very hard to do again like this is a scientific problem and that's what the platform that we are building is going to bring to customers. So in addition to that, can you share a bit your high level road map today you you produced a lot of models which are foundation related to what you just said. Do you intend also to help being? Docking station layer. Something to measure the performance, something to measure the trust of the models themselves. Yeah. So we have very interesting tools that are coming at 4:00 PM today for a better model customization connected. So that's the that's the very immediate road map. Last week we released this code trial model. We're going to be heavy on that software engineering part as well. And then more generally, we now have much more compute than what than the compute we started with. And so we are training models that are going to reach the frontier in the coming quarter. Cooler and and maybe to finish with, can you share also your kind of a more high level long term road map? Today it's mainly about text. Do you have something in mind like more multimodal and then we see a lot of things happening on large action model as well. Is it something you're working on? And in general, my question would be what should be for you the next one year, two years going ahead? Yeah, I think the future of Mistral involved being able to process images, videos, audio. These things are actually useful in an enterprise. Meeting and it's A and more effectively working on those. When you mention action models, what I think what you really mean is models that can activate some tools and use APIs and be used to have what we call agentic behavior. So the model is not used to produce text that is read by a human. It's rather used to reason about the tools it should be calling to produce a more integrated product and software. I think this is starting to work very well. Again, you have this issue of being able to evaluate over time. This creates a lot more new challenges in how do you, yeah, how do you ensure the quality of the output. But this is something that we're investing in. We are our latest models can do function calling. So that's the first step. You provide them with the tools that can be called and they call it for you. The emphasis we put on the, on the efficiency part, on the fact that small models can actually be very clever. It's really important when you build agents because when you build agents, the Elm isn't going to be read by humans. It's going to be, it's going to be running on the background. And so you still need to have a low latency for your user base in order for the UX to be good enough. And so you want to have small models able to figure out what to call, what tools to call under the hood running sufficiently fast so that. End user application as an acceptable latency. And so that's really this problem of efficiency that we are still we're good at and that we are still focusing on. So lots of still research challenges going ahead. So actually I would like on behalf of all the community I would like to congratulate you for your success. We are very proud to have you in France and Europe. Would you rather have a final word maybe for the ecosystem to encourage to adopt their. Yeah. I think it's, it's been very useful for us to see the AI ecosystem of startups start building on our platform. And so we are still encouraging it. We have a program to accelerate that. I would say as a CEO of a large enterprise, the one thing to have in mind is that. It's not only about productivity gains, it's about changing the core business, changing the software that are bringing value to your enterprise. And so that means rethinking entirely the processes and assuming that agents, for instance, are going to get better and better and we'll be able to change the cost structure of of your core business. So you need to be ambitious because you need to assume that the technology isn't going to stop improving. So we'll stay ambitious. Thank you very much. Thank you. Thank you.

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Opening Keynote by Christophe Périllat, CEO of Valeo
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[Artefact Data & Al #Digest June edition | Adopt AI Summit REPLAYS & synthesis REPORT - Month of PRIDE at Artefact: « Fierté » GenAI assistant & culture] 👉 Read the newsletter here: https://lnkd.in/e4SXYDYZ Here is what you’ll learn about in our monthly global newsletter: 1️⃣ Adopt AI Summit by Artefact - best of video. 🎥 Discover now the best moments of the summit: https://lnkd.in/eu7g7NZa 2️⃣ Adopt AI Report - Replays & Synthesis of CEOs keynotes and discussion panels. 📄 Read the Adopt AI Report: https://lnkd.in/e9i4_Xyi 3️⃣ 🎥 Subscribe to view all the replays of the 80 sessions at Adopt AI: https://lnkd.in/eDxHcR47 📰 Access all the videos and synthesis on our website: https://lnkd.in/gwRvFGyB 4️⃣ Month of Pride at Artefact 🌈 Discover and watch our product demo called Fierté AI: The open-source GenAI assistant that protects against micro-aggressions and reduces bias: https://lnkd.in/eRCSHRYP Read employees’ articles telling about our company’s #diversity & #inclusivity initiatives, because at Artefact, we act: 📗 Embracing Diversity and Ethics: Unleashing the Power of Inclusion: https://lnkd.in/gsyMq6Rk 📘 AI For Inclusion: Promoting Diversity and Equity in the Technology Sector by Paolo Gozdzink 🏳️🌈: https://lnkd.in/erq2u7Ta 📙 The Impact of LGBTQIAPN Representation in Companies by Paolo Gozdzink: https://lnkd.in/esVYfAac Stay informed, stay inspired! 📰 Sign up for our Artefact #Data & #Al Digest newsletter: https://lnkd.in/dbbC-3Jr

Artefact Data & AI Digest | Adopt AI Summit Replays & Report of keynotes | Pride Month: Fierté GenAI Assistant & Articles for Diversity & Inclusivity

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🚀 [Watch the Closing Speech of Jacques Attali] at the Adopt AI Summit by Artefact! We were immensely honored by the presence of the esteemed Jacques Attali, Writer and President of Attali Associates, sharing his vision of a new world with AI. 👉 Discover Jacques Attali’s message at #AdoptAI: https://lnkd.in/g48QNCBJ 💼 Jacques Attali is a French economist, writer, and senior civil servant. He served as a special advisor to French President François Mitterrand from 1981 to 1991. Attali founded and led the European Bank for Reconstruction and Development and has authored numerous books on diverse subjects, including economics, history, and sociology. He is also the founder of Positive Planet, an organization focused on promoting sustainable development worldwide. Attali is renowned for his influential thoughts on globalization, technology, and future societal trends. 💡 His key insights about #AI are very inspiring for the future of economics, education, and the planet. 🗣 Jacques Attali says: “AI, like all technologies, is a tool that can be used for good or ill. It can enhance harmful activities such as oil drilling and the creation of addictive products, or it can support the economy of life, including health, education, renewable energy, sustainable agriculture, recycling, and more. The potential for positive impact in these areas is immense.” #AI is a precursor to even more transformative technologies that will shape the future, such as digital biology, biomimicry, and #quantumcomputing. Digital biology uses AI in genetics, heralding significant health transformations. Biomimicry, which imitates nature, holds vast potential for innovation. Quantum computing also promises to revolutionize thinking, speed, and human-machine interactions in the coming decades. 🔎 Two key dimensions of AI are particularly noteworthy: - The first is #predictivemaintenance, which enhances our ability to predict events based on historical #data. This capability is invaluable in industrial applications, where analyzing data can prevent failures, breakdowns, leaks, and anomalies, thus transforming industrial systems and significantly reducing energy consumption. - The second dimension is #generativeAI. While it has promising applications in entertainment, translation, and FAQs, today it is still a bit less reliable for critical tasks requiring high accuracy. The use of AI should focus on beneficial applications. 👉 Read more on our website: https://lnkd.in/ggxMgZvq In summary, Jacques Attali explains that understanding the historical context of AI, focusing on its beneficial uses, and fostering talent with ethical standards are key to leveraging AI for a better future. 👉 Watch all the replays of the keynotes and conferences: https://lnkd.in/eDxHcR47 Stay tuned for more replays of the Adopt AI Summit!

Jacques Attali, Writer & President of Attali Associates, on his vision for a new world with AI - Adopt AI Summit
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