LLM和AGI之间缺少什么——Vishal Misra和Martin Casado
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Vishal Misra returns to explain his latest research on how LLMs actually work under the hood. He walks through experiments showing that transformers update their predictions in a precise, mathematically predictable way as they process new information, explains why this still doesn't mean they're conscious, and describes what's actually required for AGI: the ability to keep learning after training and the move from pattern matching to understanding cause and effect
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Acquired Podcast (United States) | Mar 17, 2026
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The transition from pattern matching to cause and effect understanding is critical but still far from being realized in AI development.
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Advancements in LLMs should not overshadow the ethical implications of creating systems that mimic human understanding.
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Believing LLMs can lead to AGI misrepresents the complexity of consciousness and understanding in machines.
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Continuous learning post-training is essential for AGI; LLMs must evolve beyond static models to truly replicate human intelligence.
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The precise, predictable nature of LLMs showcases their potential as foundational tools for achieving AGI.
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