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Self-Supervised Learning and LLMs
- Self-supervised learning revolutionized NLP through pre-training transformers, enabling them to predict missing words.
- Large language models, built on this, generate text by predicting the next word but struggle with uncertainty and lack a real-world model.
World Models
- Large language models lack a world model, hindering their understanding of reality and leading to errors.
- Yann LeCun suggests self-supervised learning could enable machines to learn world models like humans, improving planning and reasoning.
Data vs. Architecture
- Yann LeCun believes enough video data exists; the challenge lies in architecture, training paradigms, and mathematical principles.
- He suggests abandoning five machine learning pillars, including generative models and contrastive learning, for joint embedding architectures and energy-based models.