No Priors Ep. 40 | With Arthur Mensch, CEO Mistral AI

No Priors Podcast
9 Nov 202332:57

TLDRArthur Munch, CEO and co-founder of Mistral, discusses the impact of open-source AI models and the release of Mistral's 7B model. He shares insights on the company's focus on efficient algorithms, data quality, and the importance of open-source AI for scientific progress. Munch also addresses concerns about AI safety, the potential of agents, and the growing AI ecosystem in Europe, emphasizing the need for a European AI company to lead the global market.

Takeaways

  • 🌟 The French AI startup Mistra has made a significant impact with their 7B open-source model, challenging perceptions of what small models can achieve.
  • 🚀 Arthur Munch, CEO and co-founder of Mistra, left DeepMind to create a standalone company focused on frontier AI and open-source values.
  • 💡 Mistra's 7B model demonstrates the potential for highly efficient and cost-effective AI models, running fast and cheaply even on a MacBook Pro.
  • 🔍 Munch's research background in optimization and efficient algorithms at DeepMind has influenced Mistra's approach to AI model development.
  • 📈 The importance of data quality and annotations in pre-training AI models was emphasized, as it directly impacts the model's performance and usefulness.
  • 🔧 Mistra is actively working on improving inference efficiency to make AI models more accessible and cost-effective for various applications.
  • 🌐 Munch believes that Europe has a strong talent pool in AI, particularly in France, and sees potential for a major European AI company to emerge.
  • 🔒 Open-source AI is considered crucial for scientific progress and innovation, with Mistra aiming to maintain transparency and collaboration in the field.
  • 🛡️ Mistra's approach to safety involves creating modular architectures that allow for the application of guardrails, empowering developers to control model outputs.
  • 🌍 The conversation around AI safety, including physical risks and existential threats, is ongoing, with Munch advocating for a pragmatic and evidence-based approach.

Q & A

  • What was the motivation behind starting MISTR?

    -The motivation for starting MISTR was to create a standalone company in Europe focused on making AI better, specifically Frontier AI, and prioritizing open source AI as a core value.

  • How did Arthur's background in optimization influence his work at DeepMind?

    -Arthur's optimization background led him to focus on making algorithms more efficient and using data effectively to achieve good prediction performances, which was applied in his work on retrieval-augmented models and mixture of experts at DeepMind.

  • What is the significance of the Chinchilla scaling laws paper co-authored by Arthur?

    -The Chinchilla paper demonstrated that instead of training large models on few tokens, it's more effective to grow the number of tokens as the model size increases, leading to better performance and reduced serving costs.

  • How does MISTR's 7B model change the perception of what's possible with AI models?

    -MISTR's 7B model, being small, cheap to serve, and fast, yet still useful, challenges the mental model that smaller models cannot perform well, proving that high performance can be achieved without large-scale computation.

  • What is MISTR's approach to data and annotations for model training?

    -MISTR focuses on obtaining high-quality data from the open web and emphasizes the importance of data quality for model performance. They are also working on improving data annotations to align and instruct models effectively.

  • Why is being an open-source company important for MISTR?

    -MISTR believes that open-source AI fosters innovation, transparency, and community collaboration, which are crucial for advancing AI technology and addressing its challenges, as opposed to the closed and competitive approach of some large tech companies.

  • What are MISTR's thoughts on the safety and policy of open-source AI?

    -MISTR takes a pragmatic approach, arguing that open-sourcing AI models today does not significantly empower bad actors and that knowledge is not the bottleneck for misuse. They advocate for focusing on capabilities rather than pre-market compute limits.

  • How does MISTR plan to address the potential risks associated with AI models?

    -MISTR plans to build modular architectures that allow application makers to add filters and guardrails to control model outputs, ensuring that the models are used responsibly and safely.

  • What is MISTR's perspective on the potential for agents and broader AI applications?

    -MISTR sees potential in making models smaller and more efficient to facilitate agent-based applications. They are researching ways to improve models' reasoning capabilities and adaptability to different tasks' complexities.

  • What are MISTR's goals for their platform around the models they train?

    -MISTR aims to build a cost-efficient platform for hosting models, focusing on efficient inference and training architectures. They also offer time-sharing across customers for experimentation and APIs.

  • Why does Arthur believe there's an opportunity for a significant AI company based in France and Europe?

    -Arthur points to the talent pool in Europe, particularly in France, the UK, and Poland, as a strong foundation for AI development. He also notes the growing AI ecosystem in Europe, with a supportive community and investment, which could lead to the emergence of a major AI company.

Outlines

00:00

🤖 The Rise of Open Source AI Models

The conversation begins with a discussion on how open source AI models have significantly changed the tech landscape. The guest, Arthur Munch, CEO and co-founder of mistr, shares the journey of his team's development of the mistr 7B model, which has altered perceptions about the capabilities of smaller AI models. The dialogue delves into the challenges and motivations behind starting mistr, the importance of open source AI, and the team's approach to creating efficient and effective AI models.

05:02

📈 Scaling AI Models and Cost-Efficiency

This paragraph focuses on the importance of scaling AI models in relation to the number of tokens they are trained on. The guest explains the concept of proportionality between model size and token quantity, emphasizing the cost-effectiveness of their approach. The conversation also touches on the potential for further model compression and the impact of inference costs on the daily use of AI models. The guest outlines the company's strategy to make AI more accessible by reducing these costs.

10:02

🌐 Open Source AI and the Future of Machine Learning

The discussion highlights the historical progression of machine learning and the role of open source in accelerating advancements. The guest argues against the closed-off approach of some companies, advocating for transparency and collaboration to drive innovation. The conversation also addresses the importance of safety and policy considerations in the development of AI, with the guest asserting that open sourcing does not inherently pose a greater risk than existing technologies like search engines.

15:02

🔍 Addressing AI Safety Concerns

This section delves into the concerns surrounding AI safety, particularly the potential for misuse in areas like bioweapons. The guest challenges the narrative that AI models could significantly enhance the capabilities of malicious actors, arguing that the current models do not provide marginal improvements over traditional search engines. The conversation also explores the arbitrary nature of compute limits proposed for AI models and the need for a focus on capabilities rather than pre-market conditions.

20:03

🛡️ Building Guardrails for AI Models

The guest discusses the need for guardrails in AI models to prevent the generation of harmful content. The approach involves creating a modular architecture that allows application developers to implement filters and moderation systems. The conversation emphasizes the importance of not assuming AI models should be inherently well-behaved and instead focusing on empowering developers to create well-guarded applications through modular solutions.

25:03

🌟 The European AI Ecosystem

The conversation concludes with a discussion on the potential for a significant AI company to emerge from Europe. The guest shares his views on the advantages of the European talent pool and the growing AI ecosystem, particularly in France. He highlights the importance of creating a supportive environment for startups and the potential for Europe to become a major player in the global AI landscape.

Mindmap

Keywords

💡Open Source AI Models

Open Source AI Models refer to artificial intelligence systems whose source code is made available for anyone to view, modify, and distribute. This concept is central to the video, as it discusses the impact of open source on the AI landscape and the release of the mistr 7B model by the company mistr. The open source approach is seen as a way to democratize AI technology and foster innovation by enabling collaboration and transparency among researchers and developers.

💡DeepMind

DeepMind is a leading artificial intelligence company known for its advancements in machine learning and neural networks. In the context of the video, Arthur Munch, a former DeepMind researcher, shares his experience and the inspiration behind starting mistr. DeepMind's influence is evident in the discussion of AI research directions and the development of efficient algorithms.

💡Mixture of Experts

A Mixture of Experts (MoE) is a machine learning architecture where a set of specialized models, or 'experts,' each handle different parts of the input data. This concept is relevant to the video as it discusses the evolution of AI models and the work done by Arthur Munch on optimizing the scaling laws for such systems. The goal is to improve model performance by distributing tasks among experts.

💡Optimal Transport

Optimal Transport is a mathematical theory that deals with the most efficient way to move resources from one place to another. In the context of AI, it can be applied to tasks like assigning tokens to devices or optimizing the distribution of data in a model. The video mentions Arthur Munch's work on applying optimal transport to the mixture of experts framework.

💡Chinchilla Scaling Laws

Chinchilla Scaling Laws refer to the empirical findings that show the relationship between the number of tokens a model is trained on and its performance. The video highlights the importance of this research, which suggests that increasing the number of tokens proportionally with the model size leads to better performance and cost efficiency.

💡Model Compression

Model Compression is the process of reducing the size of a machine learning model without significantly affecting its performance. This is a key focus for mistr, as discussed in the video, where the goal is to create smaller, more efficient models that are cheaper to run and serve, making AI more accessible and practical for a wider range of applications.

💡Inference Cost

Inference Cost refers to the computational resources required to use a trained AI model to make predictions or perform tasks. The video emphasizes the importance of minimizing inference cost to enable widespread adoption of AI models, as it directly impacts the feasibility of deploying AI in various applications.

💡Data Annotation

Data Annotation is the process of labeling and categorizing data to improve the training and performance of machine learning models. In the video, the importance of high-quality data and annotations is discussed as a critical factor in developing effective AI models.

💡AI Safety

AI Safety refers to the measures taken to ensure that AI systems do not cause harm or unintended consequences. The video discusses the pragmatic approach to AI safety, emphasizing the need for open communication and scrutiny to address potential risks and biases in AI models.

💡Agents

In the context of AI, agents refer to autonomous entities that can interact with their environment and perform tasks. The video discusses the potential of agents and the technological innovations that could enable more widespread use of AI agents in various applications.

Highlights

Open source AI models have revolutionized the tech landscape.

A small team in France has made a significant impact with the release of MISTR 7B, an open source model.

The MISTR 7B model challenges perceptions of what can be achieved with smaller AI models.

Arthur Munch, CEO of MISTR, discusses the inspiration behind starting the company.

Munch's background in optimization and machine learning spans over a decade.

Munch's early work at DeepMind involved retrieval-augmented models and mixture of experts.

The importance of data quality and efficiency in machine learning models.

Munch's involvement in the influential Chinchilla scaling laws paper.

MISTR's approach to model compression and cost-effectiveness.

The potential for smaller, more performant models and the future of larger models like GPT-4 or GPT-5.

MISTR's focus on making AI better and open source AI as a core value.

The impact of open source AI on the field and the importance of community and transparency.

Munch's views on the safety and potential misuse of AI, challenging the narrative around bioweapons.

The pragmatic approach to safety in open source AI and the importance of guardrails.

MISTR's platform development for efficient model hosting and inference.

The potential for a major AI company to emerge from Europe, leveraging local talent.

Munch's perspective on the future of AI and the need for new techniques and innovation.

The role of open source in driving advancements in AI and the importance of avoiding regulatory capture.

MISTR's commitment to building a platform that allows for safe and efficient use of AI models.

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