**The Limitations of Large Language Models**
Yann LeCun, the Chief AI Scientist at Meta and a Turing Award winner, recently highlighted that large language models are not the be-all and end-all of artificial intelligence. Their capabilities, he argued, are highly dependent on textual training and lack a direct understanding of the physical world. While they can handle language and even take law exams, they still fall short of a ten-year-old child when it comes to tasks in the physical world.
**Self-Supervised Learning and JEPA**
Self-supervised learning is the key to building stronger, more versatile AI, with LeCun metaphorically comparing it to the dark matter of physics, forming the foundation of intelligence. JEPA (Joint Embedding Predictive Architecture) represents a new paradigm of self-supervised learning, achieving predictions of complex real-world scenarios through the construction of mental models.
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**Beyond Autoregressive Large Language Models**
Current AI systems, including large language models, still lack human-like intuition, common sense, and reasoning abilities, especially in understanding the physical world. The pursuit of human-level AI must transcend autoregressive large language models and explore architectures capable of handling complex environments and the physical world.
**Misconceptions About AGI**
The concept of General Artificial Intelligence (AGI) is misleading, as human intelligence is highly specialized. The future should focus on specific domain AI (AMI) that can achieve human-level performance in particular fields.
**AI Safety**
AI safety is not achieved by proving its safety but through engineering design, constructing goal-driven systems that align with human values and objectives. AI development should focus on goal-driven approaches, avoiding assumptions based on “spontaneous desire for domination,” and aligning it with human values in a controlled manner.
**Interview Transcript**
During the interview, LeCun and Professor Brian Keating delved into topics such as the limitations of large language models, the JEPA architecture, self-supervised learning, and AI safety.
**The Limitations of Large Language Models**
LeCun pointed out that while large language models excel in language processing, they lack an understanding of the physical world. They cannot plan actions like a cat nor propose new laws of physics like Einstein.
**The Rise of JEPA**
The JEPA architecture, through joint embedding and prediction, can learn better image and video representations and achieve predictions of complex real-world scenarios. LeCun believes that JEPA is essential to understanding the world and could be one of the possible paths to a theory of everything.
**The Importance of Self-Supervised Learning**
Self-supervised learning is the cornerstone of intelligence and the core idea of the JEPA architecture. LeCun likens it to the dark matter of AI, considering it crucial for the future development of artificial intelligence.
**AI Safety**
LeCun argued that AI systems do not spontaneously generate desires for domination but require engineering design to ensure alignment with human values. Goal-driven AI systems can achieve safety and control through optimization of objectives.
**The Future of AI**
LeCun is optimistic about the future of AI, believing it will have a profound impact on society similar to the printing press. He trusts that AI will help humans accomplish more tasks and facilitate the spread and democratization of knowledge.
**Impact on Education**
AI will change the way knowledge is transferred but will not replace education. LeCun believes that AI will help facilitate better interaction between teachers and students and promote the dissemination of knowledge.
**Summary**
LeCun’s interview offers deep insights into the current state and future development of AI. He emphasizes that self-supervised learning, the JEPA architecture, and goal-driven AI systems are the keys to the future. AI will assist humans in accomplishing more tasks and promote the spread and democratization of knowledge.
