Yann LeCun: Limits of LLMs, AGI & the Future of AI

Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast (YouTube)

Yann LeCun’s Background and Contributions
Yann LeCun has made significant contributions to AI with his groundbreaking work on convolutional neural networks. His work at Bell Labs in 1989 on the practical use of backpropagation in neural networks paved the way for advancements in handwriting recognition. It demonstrated the effectiveness of neural networks in real-world applications. As Meta’s chief AI scientist and a professor at NYU, LeCun continues to influence the field with his pioneering research and thought leadership.

Limitations of Large Language Models (LLMs)
Yann LeCun argues that large language models (LLMs) are not a sufficient path to artificial general intelligence (AGI) because they miss essential capabilities for intelligent beings, such as understanding and reasoning about the physical world.

He acknowledges the impressive feats of LLMs, such as their ability to translate languages, write various types of creative content, and provide informative answers to your questions. One of the keys to their success is how they are trained by leveraging self-supervised learning. You train a gigantic neural net to predict the next word in a sequence based on the previous words. By doing this, you create a model of the meaning of the text.

However, he argues that true intelligence requires an embodied understanding of the world and the ability to reason and plan using this understanding. LLMs do not possess these capabilities.

Here are some of the limitations of LLMs he discussed:

• LLMs are autoregressive, meaning they predict the next word in a sequence based on the previous words. This differs from how humans think, where we plan our thoughts before speaking or writing. When hallucinations occur, LMs can drift further away from accurate responses due to the auto-regressive prediction process.
• LLMs cannot anchor their understanding in reality: They cannot perform actions in the real world or learn through embodied experiences.
• current LLMs lack the capability for hierarchical planning, a crucial element for understanding and interacting with the world at multiple levels of abstraction.
• current LLMs lack persistent memory

Embodied Understanding and Hierarchical Planning
According to LeCun, genuine intelligence requires an embodied understanding of the world and the ability to reason and plan using this understanding. Humans process information visually more than textually, a significant difference from current AI models. The lack of embodied experiences means that LLMs cannot interact with the physical world or learn from such interactions, which is crucial for developing a more comprehensive understanding and reasoning ability.

Hierarchical planning is another critical aspect that LLMs currently lack. Humans think and plan at multiple levels of abstraction, allowing for more sophisticated and contextually relevant interactions. This capability is vital for AGI to enable AI systems to understand and operate in complex, dynamic environments.

Joint Embedding Predictive Architectures (JEPAs)
LeCun discusses Joint Embedding Predictive Architectures (JEPAs) as a potential advancement over LLMs. JEPAs process data from images and videos in addition to text, aiming to predict an abstract representation of inputs rather than every detail. This approach allows JEPAs to extract relevant information while filtering out unnecessary details, potentially leading to a more robust understanding and interaction capability.

For example, consider a self-driving car using a JEPA. The JEPA would process visual data from cameras, spatial data from LIDAR, and contextual data from traffic signs. It would abstract these inputs into a coherent understanding of the driving environment, allowing the car to navigate safely without needing to account for every detail.

Energy-Based Models (EBMs)
Energy-Based Models (EBMs) are another area LeCun explores. EBMs are machine learning models that define a probability distribution over a set of variables using an energy function. They provide a flexible framework for modeling relationships in data.

Imagine an EBM for recognizing handwritten digits. The landscape represents all possible images. Deep valleys correspond to images that resemble digits. Hills and high points represent images that don’t look like digits. Training the model involves shaping the landscape so that real digit images fall into deep valleys. The goal is to find the deepest valleys (lowest energy states) that most closely represent the true data distribution.

Future AI Dialogue Systems
Looking ahead, LeCun envisions AI dialogue systems that think and plan their responses before converting them into text. He believes techniques like Reinforcement Learning with Human Feedback (RLHF) are inefficient and inadequate. Future AI systems may optimize their answers in an abstract representation space before generating textual responses, allowing for more thoughtful and accurate interactions.

Ethical Considerations and Safety in AI
LeCun addresses ethical concerns and safety in AI, rejecting the notion that intelligent AI systems inherently desire to dominate or harm humans. On the contrary, there are a lot of incentives to make AI systems submissive to us. He argues that AI progress will be gradual, providing time to establish safety measures and oversight. The development of good AI to counteract bad uses of AI is essential. He suggests optimizing a set of objectives during inference, not just training. This approach allows for the inclusion of guardrails, making AI systems safer and more aligned with human values.

The Importance of Open-Source AI
A significant concern LeCun raises is the concentration of power in proprietary AI systems, which could threaten democracy by placing control of information in the hands of a few companies. LeCun states, “The danger of concentration of power in proprietary AI systems is a much bigger danger than everything else.”

He champions open-source AI to promote wider collaboration and the creation of diverse AI systems, which benefits society. Open-source AI promotes transparency and innovation.

Conclusion
Yann LeCun’s insights highlight the current limitations of large language models and the challenges in achieving artificial general intelligence. His vision for the future of AI includes embodied understanding, hierarchical planning, energy-based models, and advanced dialogue systems, all contributing to more sophisticated and safer AI systems. LeCun’s advocacy for open source AI underscores the importance of collaboration and diversity in AI development.