Yann Lecun on Llama 3 open source model | Yann LeCun and Lex Fridman

Lex Clips
11 Mar 202406:44

TLDRYann LeCun discusses the future of the open-source Llama models, expressing excitement about their potential to reach human-level intelligence. He mentions ongoing research and development, including training systems from video, which is a significant step towards creating world models. LeCun also highlights the importance of hardware innovation to make AI ubiquitous, noting the current power consumption gap between GPUs and the human brain. He emphasizes the need for new principles, fabrication technology, and components to match the human brain's computational power efficiently.

Takeaways

  • 🚀 **Llama 3 Anticipation**: Yann LeCun expresses excitement for the upcoming versions of Llama, which will be larger and more capable, with future models possibly being trained from video for a better world understanding.
  • 🔍 **Research Transparency**: Progress can be monitored through published research, indicating a commitment to openness and collaboration in the field.
  • 📈 **Training Systems from Video**: A significant step towards training AI systems from video has been taken, which is crucial for developing world models and enhancing reasoning capabilities.
  • 🤖 **Non-Generic Models**: LeCun bets that future systems will not be generic but will have specialized architectures tailored for specific tasks and functions.
  • 🧠 **Human-Level Intelligence**: There is a clear direction towards achieving human-level intelligence in AI systems, which can understand, remember, plan, and reason effectively.
  • 🔬 **Collaborative Research**: LeCun mentions collaboration with various researchers and institutions, highlighting the importance of collective efforts in advancing AI.
  • 🏆 **Excitement in AI Direction**: LeCun shares his enthusiasm for the current trajectory of machine learning and AI, which he finds more exciting than in the past decade.
  • 💻 **Hardware Improvements**: While hardware has improved, there is still a long way to go to match the computational power and efficiency of the human brain.
  • ⚙️ **Architectural Innovation**: Much of the progress in AI is coming from architectural innovations, combining elements like Transformers and CNNs.
  • 🌐 **Global Computing Power**: There is a recognition of the immense computing power that humans have built, which is used to train advanced AI models.
  • ♻️ **Sustainability Concerns**: There is an acknowledged need for more power-efficient hardware to make AI ubiquitous, reducing the significant gap between GPU and human brain power consumption.

Q & A

  • What is Yann LeCun excited about regarding the future of AI and machine learning?

    -Yann LeCun is excited about the direction of machine learning and AI, particularly the potential for systems to reach human-level intelligence with the ability to understand the world, remember, plan, and reason.

  • What is the significance of the Llama 3 open source model?

    -The Llama 3 open source model represents an evolution of AI systems with improvements over previous versions, such as being bigger, better, and multimodal. It is part of a progression towards systems capable of planning and truly understanding the world.

  • What are the future generations of systems that Yann LeCun is referring to?

    -Future generations of systems that Yann LeCun refers to are those that are capable of planning and have a deep understanding of how the world works, possibly trained from video to create a world model.

  • What is the current status of the research in training systems from video?

    -The research in training systems from video is ongoing, with the Via work being a first step published recently. The next step involves developing world models based on video training.

  • How does Yann LeCun perceive the collaboration in the field of AI?

    -Yann LeCun sees a lot of good work and collaboration happening in the field, with significant contributions from various individuals and institutions like Deep Mind, UC Berkeley, and others.

  • What is the role of hardware in achieving human-level AI?

    -Hardware plays a crucial role as it needs to support the computational power required to match the human brain. Innovations in hardware are necessary to make AI ubiquitous and to reduce power consumption to levels comparable to that of the human brain.

  • What are the current limitations of hardware in relation to the human brain?

    -Current hardware, such as GPUs, consumes significantly more power (half a kilowatt to a kilowatt) compared to the human brain (about 25 watts). There is a need for hardware innovations to increase power efficiency and computational power.

  • What is the importance of open-sourcing AI models like Llama 3?

    -Open-sourcing AI models like Llama 3 allows for the broader AI community to access, contribute to, and build upon the technology, fostering innovation and collaboration.

  • What are the challenges in training AI systems on large-scale compute systems?

    -Challenges include building the necessary infrastructure, managing hardware like GPUs, and addressing issues related to cooling and power efficiency.

  • How does Yann LeCun view the future of neural networks and AI systems?

    -Yann LeCun is optimistic about the future, seeing a path towards potentially achieving human-level intelligence with systems that can understand and interact with the world more effectively.

  • What is the current state of research on world models from video?

    -There is active research on world models from video, with significant work being conducted at institutions like Deep Mind and UC Berkeley, indicating a promising direction in AI development.

  • Why is it important to monitor the progress of AI research?

    -Monitoring progress is important because it allows the community to understand the current state of the technology, anticipate future developments, and contribute to the ongoing advancements in the field.

Outlines

00:00

🚀 Anticipating the Evolution of AI: Llama 3 and Beyond

Mark discusses the upcoming release of Llama 3, part of a series of AI advancements. He expresses enthusiasm for the future of open-source AI, including improvements on existing models and the development of multimodal capabilities. The conversation also touches on future systems that can plan and understand the world, possibly trained from video, which is a current research focus. Mark mentions recent research publications and collaborations, indicating that progress in this field is being actively monitored and shared with the community. He also highlights the potential for human-level intelligence in AI systems, showing optimism for significant breakthroughs in the near future.

05:01

💡 Hardware Innovations as a Catalyst for AI Development

The discussion shifts to the role of hardware in advancing AI capabilities. It is acknowledged that while current silicon technology and architectural innovations have improved efficiency, there is still a considerable gap in power consumption between AI systems and the human brain. The need for hardware innovation to make AI ubiquitous is emphasized, with a focus on reducing power consumption to levels comparable to that of the human brain. The speaker suggests that new fabrication technologies and components based on different principles may be necessary to achieve this goal, indicating that the field is still in the early stages of development.

Mindmap

Keywords

💡Llama 3

Llama 3 refers to an upcoming open-source model developed by Meta (formerly Facebook). The term is significant as it represents the future of AI and machine learning, with potential improvements over its predecessor, Llama 2. In the transcript, Yann LeCun expresses excitement about the future versions of Llama, including Llama 3, which are expected to have enhanced capabilities such as better multimodal functionality and an improved understanding of the world.

💡Open Source

Open source in the context of the video script refers to the practice of making the source code of a product available to the public, allowing anyone to view, use, modify, and distribute it. Yann LeCun discusses the future of open-source models under Meta, indicating a commitment to sharing advancements in AI technology with the broader community, which fosters innovation and collaboration.

💡Multimodal

Multimodal in AI refers to systems that can process and understand information from multiple different types of input, such as text, images, and sound. The transcript mentions that future versions of Llama, including Llama 3, will be 'bigger, better' and 'multimodal,' suggesting that these models will be able to integrate and interpret various forms of data, enhancing their functionality and versatility.

💡Planning

In the context of AI, planning refers to the ability of a system to use its understanding of the world to make decisions and solve problems. Yann LeCun talks about future AI systems that are 'capable of planning,' indicating a level of sophistication where the AI can not only process information but also use it to make strategic decisions and predictions.

💡World Model

A world model in AI is a representation of the environment that an AI system uses to understand and interact with the world. The transcript discusses the development of systems that are trained from video, which could lead to the creation of world models. These models would help AI systems to reason and plan more effectively by providing a framework for understanding complex real-world scenarios.

💡Reasoning

Reasoning in AI is the process by which an AI system uses logic and existing knowledge to draw conclusions or make decisions. Yann LeCun's excitement about the direction of AI research is partly due to the potential for systems that can 'reason,' which means they can make inferences and judgments based on their understanding of the world.

💡Training Systems from Video

Training systems from video involves using video data as a source of information to teach AI models how to recognize and interpret visual and contextual information. The transcript mentions that Meta published work on 'Via,' which is a step towards training systems using video data, suggesting a shift towards more dynamic and realistic training methods for AI.

💡GPUs

GPUs, or Graphics Processing Units, are specialized electronic hardware used for accelerating the creation of images in a frame buffer intended for output to a display device. In the context of the video, Yann LeCun talks about the vast number of GPUs involved in training AI models, highlighting the computational power required for developing sophisticated AI systems.

💡Human-level Intelligence

Human-level intelligence in AI refers to the goal of creating machines that can perform tasks at a level equal to or better than that of humans. Yann LeCun expresses his excitement about the potential for AI to reach human-level intelligence, indicating a long-term aspiration in the field of AI research.

💡Hardware Innovation

Hardware innovation refers to advancements in the physical technology that underpins computing systems. The transcript discusses the need for hardware innovation to make AI more ubiquitous and efficient. Yann LeCun suggests that future progress in AI may depend on new hardware technologies that can handle the computational demands of advanced AI systems.

💡Power Efficiency

Power efficiency is the measure of how well a device uses energy relative to the amount of work it performs. In the context of AI and computing, power efficiency is critical because it affects the practicality and sustainability of using AI systems. The transcript points out that current GPUs consume significantly more power than the human brain, indicating a need for more power-efficient hardware to advance AI technology.

Highlights

Llama 3 is an upcoming open-source model with no specific release date announced yet.

Llama 2 is already released, with future versions expected to be bigger and better with multimodal capabilities.

Future generations of Llama systems are expected to be capable of planning and truly understanding how the world works.

Systems may be trained from video, implying the development of a world model for enhanced reasoning and planning.

Yann LeCun is unable to provide a timeline for when these advancements will be integrated into the Llama product line.

Research progress can be monitored through published works, such as the recent Via work which is a step towards training systems from video.

Collaborative efforts with other institutions like Deep Mind and UC Berkeley are contributing to the development of world models from video.

Danar Hafner's work on models that learn representations for planning or reinforcement learning tasks is highlighted.

Yann LeCun expresses great excitement about the direction of machine learning and AI, hinting at potential human-level intelligence.

The interview discusses the necessity of hardware innovation to match the computational power and efficiency of the human brain.

Current GPU power consumption is significantly higher than that of the human brain, indicating a need for more efficient hardware.

Yann LeCun anticipates that reaching human brain compute power may occur in the next couple of decades.

The development of new principles, fabrication technology, and components based on different principles may be required for AI advancement.

The current progress in hardware is largely attributed to architectural innovation and efficient implementation of popular architectures like Transformers and CNNs.

Yann LeCun reflects on his past as a hardware guy and acknowledges the significant improvements in hardware over the decades.

The excitement around the training process on large compute systems and the open-sourcing of such models is discussed.

The challenge of building infrastructure, hardware, and cooling systems for training large models is acknowledged.

Yann LeCun shares his enthusiasm for the good direction of AI research and the potential to succeed in achieving human-level intelligence before his retirement.