Mistral 7B and the Open Source Revolution With Arthur Mensch, CEO Mistral AI
Nov 9, 2023
32:57
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Arthur Mensch, CEO and co-founder of Mistral AI, discusses the open-source revolution in AI, Mistral 7B's release, scaling laws, starting a company in France, and their next projects. The podcast also covers optimization at DeepMind, dangerous capabilities of models, the bio weapon narrative, concerns and guardrails for AI models, and the emerging AI ecosystem in Europe.
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Quick takeaways
Open source AI models can revolutionize the industry by delivering impressive performance with limited resources.
Mistral's smaller, more efficient models challenge the dominance of large models, making AI more affordable and accessible.
Deep dives
Start of Mistral and the Inspiration Behind It
Mistral, an open-source AI model, was started by a team of X-DeepMind researchers in France. The inspiration behind starting Mistral came from the founders' desire to create a standalone company focused on making AI better, developing fairer AI, and promoting open-source AI as a core value. The team wanted to explore how to make good models with limited compute and financial resources, and they believed there was a need for more AI players in Europe. Their goal was to challenge the current trend of large models and demonstrate the potential of smaller, more efficient models.
The Achievements of Mistral 7B
Mistral made a significant impact with the release of Mistral 7B, a small and efficient open-source AI model. The 7B model garnered attention because it provided impressive performance while being more cost-effective to run compared to larger models. By reducing the cost of inference, Mistral changed the perception of what could be achieved with smaller models. This achievement showcased the potential for smaller models to be highly performant and opened up new possibilities for utilizing AI in various applications.
The Importance of Inference Costs and Model Size
In the realm of AI, there is a growing recognition of the significance of inference costs and model size. Mistral aims to make inference more affordable by focusing on smaller models that deliver strong performance. They believe that reducing the compute requirements can enable the development of more interesting agents and applications while making AI accessible for a wider range of use cases. While larger models may still be necessary for certain tasks, Mistral emphasizes the importance of finding a balance and exploring the potential of smaller, more efficient models.
The Value of Open Source AI and the Need for Transparency
Mistral takes a strong stance in favor of open-source AI and advocates for more transparency and collaboration in the field. They believe that openness and knowledge sharing have been instrumental in advancing the state of AI over the past decade. Mistral aims to ensure that the scientific community continues to have open access to AI advancements, enabling greater scrutiny and improvement. They reject the notion that open-source AI is inherently unsafe and argue that openness fosters innovation, scrutiny, and a broader understanding of AI technology, leading to safer and more reliable models.
Open Source fuels the engine of innovation, according to Arthur Mensch, CEO and co-founder of Mistral AI. Mistral is a French AI company which recently made a splash with releasing Mistral 7B, the most powerful language model for its size to date, and outperforming much larger models. Sarah Guo and Elad Gil sit down with Arthur to discuss why open source could win the AI wars, their $100M+ seed financing, the true nature of scaling laws, why he started his company in France, and what Mistral is building next.
Arthur Mensch is Chief Executive Officer and co-founder of Mistral AI. A graduate of École Polytechnique, Télécom Paris and holder of the Master Mathématiques Vision Apprentissage at Paris Saclay, he completed his thesis in machine learning for functional brain imaging at Inria (Parietal team). He spent two years as a post-doctoral fellow in the Applied Mathematics department at ENS Ulm, where he carried out work in mathematics for optimization and machine learning. In 2020, he joined DeepMind as a researcher, working on large language models, before leaving in 2023 to co-found Mistral AI with Guillaume Lample and Timothee Lacroix.