Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast #416

Added: Mar 8, 2024

Limitations of LLMs

LLMs are trained on vast amounts of text data from the internet, but Lecun argues that language alone is not sufficient to build a deep understanding of the world. He highlights the importance of sensory input and interaction with the physical world in shaping human knowledge and intelligence.

Despite the success of auto-regressive language models in tasks like translation and text generation, Lecun remains skeptical about their ability to achieve a deep understanding of the world. He argues that while these models excel in fluency and language manipulation, they lack the common sense reasoning and experiential knowledge necessary for human-level intelligence.

Lecun explains that LLMs operate through auto-regressive prediction, where they predict the next word in a sequence of text. This method limits their ability to plan and reason ahead of time, as humans do when engaging in tasks like driving a car or solving a problem. He emphasizes the need for AI systems to be grounded in reality and possess a deep understanding of the physical world.

The Challenge of AI to Understand Visual Data

Lecun discusses the challenges of training AI systems to understand and predict visual data, such as images and videos. Traditional methods of training vision systems through reconstruction have not been successful in capturing the richness and complexity of visual information. Lecun introduces the concept of joint embedding architectures, which focus on learning abstract representations of inputs rather than reconstructing them pixel by pixel. These architectures aim to extract relevant information from inputs while filtering out unnecessary details.

Lecun compares joint embedding architectures to LLMs, noting that the former operate at a higher level of abstraction and focus on predicting abstract representations rather than generating detailed outputs. He explains that joint embedding architectures can help AI systems develop a deeper understanding of the world by capturing essential information while discarding noise. This approach is crucial for building intelligent systems that can reason, plan, and interact effectively with their environment.

Lecun explains how a joint embedding predictive architecture can be used to predict the state of the world based on actions taken, enabling machines to plan sequences of actions and optimize outcomes. He emphasizes the need for hierarchical planning to navigate intricate tasks like traveling from New York to Paris or understanding the state of global politics.

The Importance of Self-Supervised Learning

Lecun emphasizes the importance of training AI systems in a self-supervised manner, where they learn from the data without explicit labels. Self-supervised learning allows AI models to extract meaningful representations from input data and develop a nuanced understanding of the world. He believes that joint embedding architectures have the potential to advance AI towards more advanced machine intelligence by enabling systems to learn abstract representations and reason effectively.

Lecun discusses the potential of combining self-supervised training on visual and language data to enhance AI systems. While there is a wealth of knowledge in both types of data, merging them too early may lead to cheating and hinder the development of a deeper understanding of the world. He cautions against relying on language as a crutch to compensate for deficiencies in vision systems and emphasizes the importance of first focusing on how to teach machines to learn about the world.

Non-Contrastive Techniques in Self-Supervised Learning

Lecun explains the use of non-contrastive techniques in self-supervised learning, such as distillation and predictive modeling. These methods involve corrupting input data, training a system to predict the original data from the corrupted version, and fine-tuning the network to prevent collapse. He introduces techniques which have shown success in learning representations from images and videos.

Sensory Data and Understanding

Lecun explains that sensory data provides a much richer source of information for understanding the world compared to text-based data. He mentions the extensive amount of time young children spend observing and learning from their environment, accumulating knowledge through sensory experiences. This early learning process is crucial for developing a deep understanding of concepts such as gravity, inertia, and object distinction.

Hallucinations in Large Language Models

Lecun delves into the concept of hallucinations in large language models, attributing them to the auto-regressive prediction process used in these models. He explains that with each token generated, there is a probability of producing nonsensical answers, leading to a drift away from accurate responses. This exponential increase in error probability poses a challenge for LLMs to maintain coherence in their outputs.

Optimization in Abstract Representation Space

Lecun proposes a blueprint for future dialogue systems that involve optimizing answers in abstract representation space. By minimizing the output energy of a function that measures answer compatibility with a given prompt, the system can generate more coherent responses. This approach allows for efficient planning and reasoning in language generation tasks.

Open Source and Censorship

Lecun emphasizes the importance of open-source models in addressing bias and censorship issues in AI systems. He suggests that diverse sources of information and feedback are essential for mitigating bias and ensuring a balanced representation of different perspectives. By promoting transparency and diversity in AI development, the industry can address concerns related to censorship and bias effectively.

Lecun acknowledges the fear and skepticism surrounding AI, especially in terms of its potential to be controlled by a centralized power. He argues that open-source platforms can mitigate this concern by allowing a diverse range of people to build AI systems that reflect different cultures and values. He believes that AI can empower individuals by providing them with smart AI assistants that enhance their capabilities.

AI Mediated Interactions

Lecun envisions a future where every interaction with the digital world will be mediated by AI systems. He mentions the availability of smart glasses that can provide information and translations in real-time, showcasing the potential of AI assistants in enhancing human interactions with technology.

Business Models

When discussing the financial viability of open-source AI systems, Lecun explains that companies can still derive revenue from services and business customers while offering the base models as open source. By providing open-source models, companies can encourage innovation and collaboration while still maintaining a profitable business model.

Challenges in AI Development

Lecun acknowledges the challenges in developing AI systems, such as the need for hardware advancements to match the computational power of the human brain. He also emphasizes the complexity of intelligence, which encompasses a wide range of skills and abilities that cannot be measured by a single metric like IQ.

Gradual Progress Towards AGI

Contrary to popular beliefs, Lecun argues that the development of artificial general intelligence (AGI) will not be a sudden event but a gradual process. He explains that achieving human-level intelligence in AI will require advancements in various areas such as understanding the world, reasoning, planning, and learning hierarchical representations.

AI Doomsday Scenarios

Lecun addresses the concerns raised by AI doomsayers who fear the emergence of superintelligent AI that could pose a threat to humanity. He refutes these claims by emphasizing that AI development will be incremental and involve the implementation of guardrails to ensure safety and control. He also dismisses the notion that intelligent AI systems would inherently seek to dominate or harm humans, as intelligence does not equate to malevolence.

The Future of Robotics

Lecun discusses the advancements in robotics, particularly in the development of humanoid robots. He mentions companies like Tesla and Boston Dynamics that are making progress in this field. He sees potential for millions of humanoid robots to be integrated into society in the next decade. He highlights the challenges of automating tasks like loading the dishwasher and navigating complex environments.

Hope for the Future

Despite the concerns surrounding AI, Lecun remains optimistic about the future of humanity. He believes that AI has the potential to make people smarter and enhance their capabilities. He draws parallels between the impact of AI and the invention of the printing press, which transformed society by increasing access to knowledge and information. He sees AI as a tool that can empower individuals and drive positive change in the world.