BI 087 Dileep George: Cloning for Cognitive Maps

When a waiter hands me the bill, how do I know whether to pay it myself or let my date pay? On this episode, I get a progress update from Dileep on his company, Vicarious, since Dileep's last episode. We also talk broadly about his experience running Vicarious to develop AGI and robotics. Then we turn to his latest brain-inspired AI efforts using cloned structured probabilistic graph models to develop an account of how the hippocampus makes a model of the world and represents our cognitive maps in different contexts, so we can simulate possible outcomes to choose how to act. Special guest questions from Brad Love (episode 70: How We Learn Concepts) . Vicarious website - Dileep's AGI robotics company.Twitter: @dileeplearning.Papers we discuss:Learning cognitive maps as structured graphs for vicarious evaluation.A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model.Probabilistic graphical models.Hierarchical temporal memory. Time stamps: 0:00 - Intro 3:00 - Skip Intro 4:00 - Previous Dileep episode 10:22 - Is brain-inspired AI over-hyped? 14:38 - Compteition in robotics field 15:53 - Vicarious robotics 22:12 - Choosing what product to make 28:13 - Running a startup 30:52 - Old brain vs. new brain 37:53 - Learning cognitive maps as structured graphs 41:59 - Graphical models 47:10 - Cloning and merging, hippocampus 53:36 - Brad Love Question 1 1:00:39 - Brad Love Question 2 1:02:41 - Task examples 1:11:56 - What does hippocampus do? 1:14:14 - Intro to thalamic cortical microcircuit 1:15:21 - What AI folks think of brains 1:16:57 - Which levels inform which levels 1:20:02 - Advice for an AI startup

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Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.