Yann Lecun on Llama 3 open source model | Yann LeCun and Lex Fridman
TLDRYann LeCun discusses the upcoming Llama 3 open source model and expresses excitement about its potential for human-level intelligence. He mentions that future versions of Llama will be larger and more capable, with an emphasis on understanding the world and planning. LeCun highlights the importance of training systems from video, which is a step towards creating world models. He also discusses the need for hardware innovation to make AI ubiquitous, noting the significant power efficiency gap between current GPUs and the human brain. LeCun's enthusiasm for the direction of AI and machine learning is evident, as he believes there is a path towards systems that can understand, remember, plan, and reason.
Takeaways
- 🚀 **Llama 3 Anticipation**: Yann LeCun is excited about the future versions of Llama models, including Llama 3 and beyond, which are expected to be larger and more capable with multimodal capabilities.
- 🧠 **Understanding and Planning**: Future systems will be focused on developing a world model that understands how the world works and is capable of reasoning and planning.
- 📈 **Research Progress**: Yann mentions that progress can be monitored through published research, indicating that the community will be able to follow developments in training systems from video.
- 🔬 **Collaborative Work**: There is ongoing collaboration in the field, with significant contributions from researchers at DeepMind, UC Berkeley, and others, including work by Dan Hafner on models for planning and reinforcement learning.
- ⏱️ **Timeline for Advancements**: While there is no specific timeline, breakthroughs are necessary before the advanced systems can be realized, and the research direction is promising.
- 🔥 **Excitement for AI**: Yann expresses great excitement about the direction of machine learning and AI, seeing a path towards potentially achieving human-level intelligence.
- 💻 **Hardware and Software**: Yann acknowledges the importance of both hardware and software in achieving these goals, noting that while hardware has improved, there is still a need for innovation to match human brain efficiency.
- 🌐 **Open Sourcing AI**: There is a sense of beauty in training a sophisticated AI model and then open sourcing it, symbolizing a collaborative effort in advancing technology.
- 🔋 **Power Efficiency**: A significant challenge lies in power efficiency, with current GPUs consuming much more power than the human brain, indicating a need for more efficient hardware.
- 🏗️ **Architectural Innovation**: Much of the current progress is due to architectural innovation in AI models, combining elements of Transformers and convolutional neural networks.
- ⛽️ **New Principles Needed**: To further advance, new principles and possibly new fabrication technologies will be required, moving beyond classical digital semiconductors.
Q & A
What is Yann LeCun excited about regarding the future of open source models like Llama?
-Yann LeCun is excited about the future of open source models, particularly the improvements in versions like Llama 2 and the potential of future versions such as Llama 3, 4, 5, 6, and 10. He is particularly interested in systems capable of planning and understanding how the world works, possibly trained from video, which could lead to human-level intelligence in AI.
What is the significance of the recent publication of the Via work in the context of AI development?
-The Via work represents a first step towards training systems from video, which is a crucial part of developing more advanced AI models that have a better understanding of the world. This is a significant milestone on the path to creating AI systems with more sophisticated reasoning and planning capabilities.
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How does Yann LeCun view the role of hardware in the advancement of AI?
-Yann LeCun acknowledges that while hardware improvements are necessary, they are not sufficient on their own. He points out that we are still far from matching the compute power and power efficiency of the human brain, indicating that significant progress is needed in hardware innovation, including new principles, fabrication technology, and components.
What are the current limitations in terms of computational power and efficiency for AI systems?
-Current AI systems, such as GPUs, consume much more power than the human brain. A GPU can use between half a kilowatt to a kilowatt, whereas the human brain operates on about 25 watts. To match the human brain's computational power, we would need thousands or even millions of GPUs, highlighting the need for more efficient hardware.
What is the importance of open sourcing AI models like Llama?
-Open sourcing AI models like Llama allows for broader collaboration and innovation within the AI community. It enables researchers and developers around the world to access, use, and build upon these models, accelerating the pace of AI development and democratizing access to advanced AI technology.
What are the potential future directions for AI systems according to Yann LeCun?
-Yann LeCun envisions AI systems that are not just general models but have the ability to understand the world, remember, plan, and reason. He anticipates that future systems will likely be trained on video, creating a world model that can be used for planning and learning tasks, potentially through reinforcement learning.
What is the current state of research on training AI systems from video?
-There is ongoing research on training AI systems from video at various institutions, including Meta, DeepMind, and UC Berkeley. This work is significant because it is expected to contribute to the development of world models that can lead to more advanced reasoning and planning in AI.
How does Yann LeCun perceive the progress made in the field of neural networks over the past few decades?
-Yann LeCun has been working on neural networks for over 30 years and is currently more excited about the direction of machine learning and AI than he has been in a decade. He sees promising progress towards potentially achieving human-level intelligence in systems that can understand, remember, plan, and reason.
What is the role of collaboration in advancing AI research?
-Collaboration plays a crucial role in AI research. Yann LeCun mentions collaborations with researchers like Dan Hafner and others at institutions like NYU and UC Berkeley. These collaborations, both academic and through industry partnerships like Meta, contribute to the exchange of ideas and accelerate the development of new AI technologies.
What are the challenges in achieving human-level intelligence in AI systems?
-Achieving human-level intelligence in AI systems involves overcoming significant challenges in both software and hardware. On the software side, there is a need for new principles and architectures that can support more complex reasoning and planning. On the hardware side, there is a need for innovations that can provide the necessary computational power with greater efficiency.
How does Yann LeCun view the future of AI in terms of its potential impact on society?
-While the transcript does not explicitly address the societal impact, Yann LeCun's excitement about the direction of AI implies a belief in its potential to bring about significant positive change. The development of more advanced AI systems could lead to breakthroughs in various fields, improving the quality of life and solving complex problems.
Outlines
🚀 Anticipating the Evolution of AI: Llama 3 and Beyond
Mark discusses the upcoming release of Llama 3, an advancement in AI technology, though no specific release date is mentioned. He expresses enthusiasm for the current Llama 2 and the potential of future versions, which are expected to be more advanced with multimodal capabilities. The conversation delves into the future of open-source AI, with a focus on systems capable of comprehensive world modeling and planning based on video training. Mark highlights the importance of breakthroughs in research and the publication of findings to track progress. He also mentions collaborative work with various entities, including Deep Mind, UC Berkley, and individuals like Dan Hafner, on developing world models and learning representations for planning and reinforcement learning tasks. Mark's excitement is not just for the theoretical aspects but also for the practical implications of training such systems on massive computational infrastructure, which he views as a significant milestone for humanity in the field of AI.
💡 The Necessity of Hardware Innovation for Advanced AI
The discussion shifts towards the need for hardware innovation to support the development of advanced AI systems. While there have been improvements in silicon technology and architectural innovation, Mark believes there is still a long way to go before reaching the computational power and efficiency of the human brain. He emphasizes that to make AI ubiquitous, power consumption must be significantly reduced, as current GPUs consume much more power than the human brain. Mark suggests that new fabrication technologies and components based on different principles may be necessary to achieve the required advancements. He acknowledges that while progress is being made, there is a substantial gap to bridge before hardware can match the capabilities of the human brain, which is a critical step towards potentially achieving human-level intelligence in AI systems.
Mindmap
Keywords
💡Llama 3
💡Open Source
💡Multimodal
💡World Model
💡Reasoning
💡Planning
💡Training Systems from Video
💡GPUs
💡Hardware Innovation
💡Computational Power
💡Neural Networks
Highlights
Llama 3 is an upcoming open-source model by Meta, 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 anticipated to have advanced planning capabilities and a deeper understanding of the world.
Training systems from video is a current research focus, which could lead to the development of world models.
Yann LeCun is excited about the direction of machine learning and AI, seeing a path towards potentially human-level intelligence.
The research in training systems from video is expected to be published, allowing the public to monitor progress.
DeepMind and UC Berkeley are also working on world models from video, indicating a collaborative effort in the field.
Danar Hafner's work on models that learn representations for planning or reinforcement learning is highlighted.
Collaborations between Meta, NYU, and other institutions are driving advancements in AI and machine learning.
Hardware innovations are necessary for the widespread adoption of AI, as current GPUs consume significantly more power than the human brain.
The current focus is on architectural innovation and more efficient implementation of popular AI architectures like Transformers and CNNs.
Yann LeCun expresses his excitement about the potential for systems that can understand, remember, plan, and reason.
The development of an open-source brain trained on a gigantic compute system is seen as a significant milestone.
The challenge of building infrastructure, hardware, and cooling systems for such powerful AI models is acknowledged.
Yann LeCun used to be a hardware guy, and he acknowledges the significant improvements in hardware over the past decades.
There is still a long way to go in terms of compute power and power efficiency to match the human brain's capabilities.
New principles, fabrication technology, and basic components may be required to achieve the next level of AI advancement.
Yann LeCun is optimistic about the future of AI and the possibility of achieving human-level intelligence.