What's Missing Between LLMs and AGI - Vishal Misra & Martin Casado

The podcast explores how large language models (LLMs) function, focusing on Vishal Misra's mathematical modeling of their processes. Misra details his work on using GPT-3 for translating natural language into a domain-specific language for querying a cricket database, which led him to investigate the underlying mechanisms of LLMs. He introduces the concept of a vast matrix representing all possible prompts and their corresponding probability distributions for the next token, explaining how LLMs approximate this matrix. The discussion covers in-context learning as Bayesian updating, where LLMs refine their predictions with new evidence. Misra also differentiates between human and LLM learning, noting that LLMs lack the continual learning and causal understanding inherent in human cognition, which limits their ability to achieve true AGI.