Why Can't AI Make Its Own Discoveries? — With Yann LeCun
Mar 19, 2025
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Yann LeCun, Chief AI Scientist at Meta and Turing Award winner, discusses the intriguing limitations of AI in making original discoveries. He explains why current AI models, despite their vast access to knowledge, struggle with true innovation and requires deeper understanding. LeCun highlights the key differences between human reasoning and AI capabilities, emphasizing the need for advanced architectures. The conversation also touches on the importance of open-source innovation and the potential pitfalls for investors in the AI landscape.
Current AI models excel at information retrieval but struggle to make original discoveries due to their reliance on existing knowledge.
To achieve genuine scientific reasoning, AI must evolve beyond text-based learning and develop abstract mental models of the world.
Open-source AI development fosters rapid innovation and collaboration, outpacing proprietary systems in enhancing AI capabilities and applications.
Deep dives
Challenges of Generative AI in Scientific Discovery
Generative AI has access to vast amounts of human knowledge but struggles to make original scientific discoveries. This limitation stems from the nature of large language models (LLMs), which are primarily designed to regurgitate existing text rather than generate new insights. Unlike a human, who can connect disparate ideas to propose new hypotheses, current AI algorithms lack the capability to formulate novel questions or explore concepts beyond their training data. Experts argue that to emulate true scientific reasoning, AI must evolve beyond its current text-based learning models.
The Need for New AI Architectures
The conversation underscores the diminishing returns of scaling up LLMs, leading to a call for new architectural paradigms in AI development. Current models, while proficient in information retrieval, often fail at problem-solving and reasoning, which are critical for meaningful innovation. Introducing architectures capable of searching for actionable solutions and building abstract mental models is essential for future AI advancements. Researchers are optimistic that, with a shift in focus, AI can eventually learn to formulate the right questions and develop new solutions.
Limitations of Current AI Reasoning Capabilities
Despite efforts to enhance reasoning abilities in AI, significant limitations remain in how these systems understand and manipulate information. The reasoning frameworks applied to LLMs struggle to replicate human-like cognitive processes, which involve forming mental models and conceptualizing scenarios. Current methodologies often rely on generating plausible outputs rather than genuine reasoning, meaning AI may produce technically correct but contextually meaningless results. A more nuanced approach, which incorporates the capacity to plan and learn from experiences, is necessary for achieving true reasoning capabilities.
Open Source Innovations vs. Proprietary Technologies
The discussion highlights the advantages of open-source AI developments over proprietary systems, noting that open platforms foster more rapid innovation and contributions from diverse talents. With advancements occurring more frequently in the open-source community, proprietary companies reap the benefits of these developments while struggling to keep pace with new ideas. Open-source models tend to be more cost-effective and secure, making them increasingly appealing for implementation in practical applications. This competitive landscape poses challenges for proprietary firms relying on traditional closed systems and methodologies.
Future Directions for AI Learning and Reasoning
Emerging frameworks such as the Joint Embedding Predictive Architecture (JEPA) represent promising avenues for improving how AI systems understand the world. By focusing on predicting outcomes based on corrupted data rather than attempting to recreate it, these models aim to capture essential underlying principles of physics and common sense. This approach enables systems to recognize physically impossible scenarios and to reason about potential actions, opening up new possibilities for AI applications. Researchers are hopeful that integrating this understanding can lead to more reliable and capable AI agents in the future.
Yann LeCun is the chief AI scientist at Meta. He joins Big Technology Podcast to discuss the strengths and limitations of current AI models, weighing in on why they've been unable to invent new things despite possessing almost all the world's written knowledge. LeCun digs deep into AI science, explaining why AI systems must build an abstract knowledge of the way the world operates to truly advance. We also cover whether AI research will hit a wall, whether investors in AI will be disappointed, and the value of open source after DeepSeek. Tune in for a fascinating conversation with one of the world's leading AI pioneers.
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