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.
Chapters:
00:00 Introduction to Jan LeCun and AI's limitations
01:12 Why LLMs can't make scientific discoveries
05:40 Reasoning in AI systems: limitations of chain of thought
10:13 LLMs approaching diminishing returns and the need for a new paradigm
16:29 "A PhD next to you" vs. actual intelligent systems
21:36 Consumer AI adoption vs. enterprise implementation challenges
25:37 Historical parallels: expert systems and the risk of another AI winter
29:37 Four critical capabilities AI needs for true understanding
33:19 Testing AI's physics understanding with the paper test
37:24 Why video generation systems don't equal real comprehension
43:33 Self-supervised learning and its limitations for understanding
51:10 JEPA: Building abstract representations for reasoning and planning
54:33 Open source vs. proprietary AI development
58:57 Conclusion