Key insights

• 🦙 Release of Llama 3 with 8B and 70B versions, 15 trillion tokens and data for model training
• 🌐 Yan's influence on open source and its impact on technology development
• 🔜 Upcoming release of the 750b monster neural net as open source
• 💻 Challenges include supply and cost of GPUs, as well as scaling up learning algorithms
• 👥 Open sourcing AI is unprecedented and aims to encourage community collaboration and innovation
• 🧠 VJEA as a solution for creating a truly intelligent AI system
• 🔍 Need for new architectures for AI with understanding, persistent memory, reasoning, planning, and controllability
• 🖼️ Joint embedding predictive architecture (JEA) for reconstructing hidden details in images and videos

Q&A

• What topics are covered in the discussion regarding AI systems, their advancements, and potential future developments?

  The discussion covers topics such as late Fusion, specialized encoders, predicting the future of VJEA data, addressing missing elements in solving fundamental problems, and the potential advancements in AI systems.

• What is the Joint Embedding Predictive Architecture (JEA) and its potential implications?

  The Joint Embedding Predictive Architecture (JEA) is designed for reconstructing hidden details in images and videos, training systems with intuitive physics and predictive capabilities, improving common sense in AI, and debating on the approaches for developing AI systems.

• What are the limitations of current AI systems, and why is the development of new architectures essential?

  Limitations of current AI systems include a lack of understanding of the physical world, persistent memory, reasoning, planning, and controllability. It is crucial to develop new architectures to address these limitations and enable AI to have a deeper understanding of the world.

• How does open source infrastructure accelerate progress and affect the security and efficiency of AI?

  Open source infrastructure accelerates progress, enhances the security and efficiency of AI, fosters innovation, and contributes to the growth of the ecosystem.

• Why is open sourcing AI significant, and what is the logic behind it?

  Open sourcing AI is unprecedented and aims to encourage community collaboration and innovation. It addresses challenges such as the massive cost and supply of GPUs, and aligns with Meta's history of open sourcing infrastructure software.

• What is the impact of Yan's contributions to open source technology?

  Yan's influence on open source technology has played a significant role in accelerating progress, making AI more secure and efficient, fostering innovation, and contributing to ecosystem growth.

• What are the different versions of Llama 3 and the number of tokens and data for model training?

  Llama 3 is released with 8B and 70B versions, containing an impressive 15 trillion tokens and data for model training.

• 00:02 A conversation with Yan about the release of Llama 3, 15 trillion tokens, and the impact of open source on technology. Yan's contributions to open source technology and the potential impact of the 750b monster neural net.
• 03:03 Discussing the challenges and massive cost of training AI models using GPUs. Open sourcing AI is unprecedented, and the logic behind it is to encourage community collaboration and innovation.
• 05:57 Open source infrastructure accelerates progress, makes AI more secure and efficient. Collaboration fosters innovation and ecosystem growth. VJEA and the path to truly intelligent AI. Embracing open source and collaboration is key to AI advancement.
• 08:54 Discussing the limitations of current AI systems and the need to develop new architectures for AI to understand the world, have persistent memory, reason, plan, and be controllable. Exploring the challenge of getting AI systems to understand the world through observation and the failure in training systems to predict video content as a way to develop understanding of the physical world.
• 12:03 Developing joint embedding predictive architecture for understanding and predicting images and videos; potential to create mental world models and improve common sense in AI systems; debate about creating one massive foundation model or using a mixture of experts approach.
• 15:02 The discussion covers topics like late Fusion, specialized encoders, predicting the future of VJEA data, addressing missing elements in solving fundamental problems, and the potential advancements in AI systems.