Ep. 24 Why GenAI Models Don’t Continuously Learn and How It Can Be Fixed | AI Insights & Innovation

This video delves into why large language models (LLMs) lack continuous learning and ongoing efforts to address this. Challenges like catastrophic forgetting, resource constraints, and data privacy hinder progress. Catastrophic forgetting occurs when models lose prior knowledge with new training. Efforts are underway to develop continual learning algorithms, meta-learning for adaptability, and techniques for handling non-stationary environments. Researchers focus on allowing models to learn from their environment continuously, similar to reinforcement learning. While significant progress is being made, achieving fully continuous learning in generative AI requires further advancements in algorithms and privacy solutions.

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Will it ever be possible to separate intelligence from data, and would that be useful?