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Temporal Aspects of AI Reasoning and Predictive Approaches
The chapter dives into the temporal aspects of AI reasoning, exploring the effectiveness of models in predicting short versus long temporal tasks. It discusses the importance of focusing on prediction over generation for building goal-driven agents and delves into the complexity and efficiency of predictive models. The conversation also touches on the use of multiple levels of abstraction in hierarchical structures and the challenges of learning from perceptual signals to build a grounded understanding of the world.