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Harri Valpola: System 2 AI and Planning in Model-Based Reinforcement Learning

Machine Learning Street Talk (MLST)

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System 1 and System 2 Thinking in AI

This chapter explores the distinctions between System 1 and System 2 thinking in both human cognition and reinforcement learning frameworks. It highlights the advantages of explicit understanding and internal simulations in enhancing decision-making processes, particularly in AI systems like AlphaGo. The discussion further examines the evolution of AI, emphasizing the need for a balance between instinctive responses and reflective reasoning in developing intelligent technologies.

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