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Jacob Andreas: Language, Grounding, and World Models

The Gradient: Perspectives on AI

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Understanding World Models in AI

This chapter focuses on the concept of world models in artificial intelligence, examining their implications for decision-making and learning in systems like AteloGPT. It discusses the historical evolution of language models, the philosophical nuances of defining 'world models', and their relationship to meaning-making processes. Additionally, the chapter highlights the challenges of mechanistic interpretability and emphasizes the potential of methods like deductive closure training to enhance the factual accuracy of language models.

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