Yann LeCun, Pioneer of AI, Thinks Today's LLM's Are Nearly Obsolete

Summary

Ask Yann LeCun—Meta's chief AI scientist, Turing Award winner, NYU data scientist and one of the pioneers of artificial intelligence—about the future of large language models (LLMs) like OpenAI's ChatGPT, Google's Gemini, Meta's Llama and Anthropic's Claude, and his answer might startle you: He believes LLMs will be largely obsolete within five years."The path that my colleagues and I are on at [Facebook AI Research] and NYU, if we can make this work within three to five years, we'll have a much better paradigm for systems that can reason and plan," LeCun explains in the latest installment in Newsweek's AI Impact interview series with Marcus Weldon, describing his team's recent work on their Joint Embedding Predictive Architecture (JEPA). He hopes this approach will make current LLM-based approaches to AI outdated, as these new systems will include genuine representations of the world and, he says, be "controllable in the sense that you can give them goals, and by construction, the only thing they can do is accomplish those goals."His belief is so strong that, at a conference last year, he advised young developers, "Don't work on LLMs. [These models are] in the hands of large companies, there's nothing you can bring to the table. You should work on next-gen AI systems that lift the limitations of LLMs."The paradox is striking: One of the principal architects behind today's AI boom is also one of its most notable skeptics. While companies race to deploy ever more sophisticated conversational agents and investors pour billions into large language model startups and the data centers to power them, LeCun remains unimpressed by what many consider the cutting edge of artificial intelligence, despite his team producing one of the leading foundational models used today: Llama.For LeCun, today's AI models—even those bearing his intellectual imprint—are relatively specialized tools operating in a simple, discrete space—language—while lacking any meaningful understanding of th...