729: Universal Principles of Intelligence (Across Humans and Machines), with Prof. Blake Richards
Super Data Science: ML & AI Podcast with Jon Krohn
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Exploring Loss and Reward Functions in AI and Neuroscience
The chapter delves into the differences between mentally rolling out all possible outcomes and choosing actions based on estimated future rewards in AI and neuroscience. It discusses the significance of loss and reward functions in quantifying norms for intelligent systems and neural circuits, emphasizing the challenges and solutions involved in understanding neural circuits and optimizing brain functions through concepts like backpropagation and gradient descent.
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