How Meta’s Chief AI Scientist Believes We’ll Get To Autonomous AI Models
TLDRMeta's Chief AI Scientist, Yan LeCun, discusses the latest advancements in AI, including the release of Meta's 8 billion parameter LLaMA model. He emphasizes the importance of open-source AI models for fostering innovation and avoiding monopolization of technology. LeCun also explores the concept of V-JEA, a potential solution to create AI systems with a better understanding of the physical world, which could lead to significant advancements in AI capabilities.
Takeaways
- 😲 Meta has released an 8 billion parameter AI model called LLaMA-3, which is believed to perform as well as the previous 270 billion parameter model.
- 📈 The model was trained on an impressive 15 trillion tokens, showcasing the massive scale of data used to enhance AI capabilities.
- 🔄 Yan LeCun, Meta's Chief AI Scientist, emphasized that the credit for such advancements belongs to a large team, not just him.
- 🌐 Open sourcing AI models is crucial for accelerating innovation, ensuring more secure systems, and avoiding redundant training costs.
- 🚀 The upcoming 750 billion parameter AI model will also be open source, indicating a significant commitment to collaborative progress in AI.
- 💰 The training of such massive AI models is so costly that it rivals historical projects like the Apollo moon mission in terms of R&D spending.
- 🔍 The limitations of current large language models (LLMs) were discussed, including their lack of understanding of the physical world, no persistent memory, limited reasoning, and poor planning abilities.
- 🔄 LeCun introduced the concept of JEA (Joint Embedding Architecture) as a potential solution to give AI systems a better understanding of the world.
- 📹 The idea behind V-JEA (Video Joint Embedding Architecture) is to train AI by predicting video sequences to develop an intuitive understanding of physics and dynamics.
- 🔮 The future of AI is expected to involve systems with common sense and the ability to learn quickly, potentially leading to advancements in robotics and autonomous vehicles.
- 🌐 There's a debate in the AI community about whether multimodal systems should use early or late fusion of data types like text, images, and video.
Q & A
What is the significance of the 8B and 70B models mentioned in the script?
-The 8B and 70B models refer to AI models with 8 billion and 70 billion parameters respectively. The script suggests that the 8B model performs as well as the older 270B model, indicating a significant improvement in efficiency and capability at a lower parameter count.
What does the speaker attribute the success of AI advancements to?
-The speaker attributes the success of AI advancements to a large collection of people and their collective contributions, rather than individual efforts. They also emphasize the importance of open-sourcing models to foster innovation and collaboration.
Why did Meta choose to train on 15 trillion tokens?
-Meta chose to train on 15 trillion tokens to leverage all available high-quality public data, fine-tuning and licensing data to create a robust AI model capable of understanding and generating human-like text.
What is the speaker's view on the open-sourcing of AI models?
-The speaker is a strong advocate for open-sourcing AI models, comparing it to the open-source nature of internet infrastructure software. They believe it leads to faster progress, more security, and a better ecosystem for innovation.
What is the estimated cost of training the AI models discussed in the script?
-The estimated cost of training the AI models is compared to the Apollo moon mission, suggesting a cost in the range of billions of dollars, with a specific mention of $30 billion worth of Nvidia chips.
What is the role of the 750B 'monster neural net' mentioned in the script?
-The 750B 'monster neural net' is an upcoming AI model that will also be open-sourced. It is expected to be a significant turning point for humanity and will be densely connected, not sparse, indicating a high level of interconnectivity between its neurons.
What are the limitations of current LLMs (Large Language Models) as discussed in the script?
-Current LLMs have limitations such as not understanding the physical world, lacking persistent memory, not being able to reason in the way humans do, and not being able to plan effectively in new situations.
What is the concept of VJEA (Video Joint Embedding Architecture) and how does it relate to AI advancement?
-VJEA is a concept aimed at training AI systems to understand the world by watching videos, similar to how humans and animals learn. It involves a joint embedding predictive architecture that trains a system to predict the representation of a video, potentially leading to systems with intuitive physics and planning capabilities.
What is the speaker's vision for the future of AI in terms of understanding the world?
-The speaker envisions AI systems that can understand the world with a level of common sense, starting with capabilities similar to that of a cat. This would enable AI to perform tasks like domestic robots or driving cars with ease.
What is the importance of the open-sourcing philosophy according to the speaker?
-The speaker emphasizes that open-sourcing AI models prevents the waste of resources and promotes a shared substrate for innovation. It aligns with the historical shift towards open-source infrastructure, which has been beneficial for progress and security.
Outlines
🤖 Introduction to AI Advancements
The speaker expresses gratitude to Yan for his contributions to AI, particularly for championing open-source models. They discuss the recent release of 'llama 3' with 8 billion parameters, trained on an impressive 15 trillion tokens. The speaker reminisces about how Yan's work on optical character recognition and convolutional neural networks influenced his own career. The conversation highlights the significance of open-sourcing AI models, allowing for the creation of companies and innovations that would otherwise be unattainable.
🌐 Open Sourcing AI: A Game Changer
The discussion delves into the rationale behind open-sourcing AI models. The speaker explains that open-sourcing infrastructure software is a prevalent practice at Meta, as it fosters community contributions, enhances security, and accelerates progress. The analogy of the internet's evolution in the 90s is used to emphasize the importance of open-source for infrastructure. The speaker also touches on the upcoming release of a 750 billion parameter neural network, which will also be open source, and the potential of this approach to democratize AI innovation.
🚀 Vision for Advanced Machine Intelligence (AMI)
Yan shares his vision for AMI, outlining the limitations of current LLMs (Large Language Models) in understanding the physical world, reasoning, and planning. He introduces the concept of JEA (Joint Embedding Architecture) as a potential solution to these limitations, explaining how it could enable AI systems to develop an intuitive understanding of the world. The conversation also explores the challenges of training such models and the potential for AI to eventually match the common sense and learning abilities of a human or an animal.
🔮 Predicting the Future of AI
The speaker invites Yan to predict the future of AI, focusing on the potential of incorporating VJEA data into massive models. They discuss whether this could lead to solving complex problems like those in physics and biology. Yan expresses optimism that AI could reach the level of common sense possessed by a cat, which would be a significant advancement. The conversation also briefly touches on the social aspects of AI development, including the speaker's experience at an event in Davos and the spectrum of opinions on AI's future impact.
Mindmap
Keywords
💡Autonomous AI Models
💡LLM (Large Language Models)
💡Open Source
💡Parameters
💡Neural Networks
💡GPUs (Graphics Processing Units)
💡Training
💡VJEA (Video Joint Embedding Architecture)
💡Intuitive Physics
💡Common Sense
💡Foundation Model
Highlights
Meta's Chief AI Scientist discusses the release of the 8 billion parameter LLaMA 3 model.
The new model is said to perform as well as the previous 270 billion parameter LLaMA.
The model was trained on an impressive 15 trillion tokens.
The importance of open-sourcing AI models for collaborative development and innovation.
The upcoming release of a 750 billion parameter neural network,预示着未来AI的巨大潜力。
Meta's investment in AI, including the purchase of 500,000 Nvidia chips for model training.
The challenge of scaling up learning algorithms to parallelize across many GPUs.
The philosophy behind open-sourcing massive AI models and its potential impact on society.
The current limitations of LLMs in understanding the world, reasoning, and planning.
The vision for Advanced Machine Intelligence (AMI) and the path towards it.
The concept of Joint Embedding Architecture (JEA) for training AI systems to understand the world.
The potential for JEA to give AI systems a more human-like understanding of physics and the world.
The debate between early and late fusion in multimodal AI systems.
The future of AI and the potential for systems to solve complex problems like those in physics and biology.
The hope for AI systems that can be trained quickly, like a human teenager learning to drive.
The importance of community and collaboration in the advancement of AI.