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BI 106 Jacqueline Gottlieb and Robert Wilson: Deep Curiosity

BI 106 Jacqueline Gottlieb and Robert Wilson: Deep Curiosity

FromBrain Inspired


BI 106 Jacqueline Gottlieb and Robert Wilson: Deep Curiosity

FromBrain Inspired

ratings:
Length:
92 minutes
Released:
May 27, 2021
Format:
Podcast episode

Description

Jackie and Bob discuss their research and thinking about curiosity.



Jackie's background is studying decision making and attention, recording neurons in nonhuman primates during eye movement tasks, and she's broadly interested in how we adapt our ongoing behavior. Curiosity is crucial for this, so she recently has focused on behavioral strategies to exercise curiosity, developing tasks that test exploration, information sampling, uncertainty reduction, and intrinsic motivation.



Bob's background is developing computational models of reinforcement learning (including the exploration-exploitation tradeoff) and decision making, and he behavior and neuroimaging data in humans to test the models. He's broadly interested in how and whether we can understand brains and cognition using mathematical models. Recently he's been working on a model for curiosity known as deep exploration, which suggests we make decisions by deeply simulating a handful of scenarios and choosing based on the simulation outcomes.



We also discuss how one should go about their career (qua curiosity), how eye movements compare with other windows into cognition, and whether we can and should create curious AI agents (Bob is an emphatic yes, and Jackie is slightly worried that will be the time to worry about AI).





Jackie's lab: Jacqueline Gottlieb Laboratory at Columbia University.Bob's lab: Neuroscience of Reinforcement Learning and Decision Making.Twitter: Bob: @NRDLab (Jackie's not on twitter).Related papersCuriosity, information demand and attentional priority.Balancing exploration and exploitation with information and randomization.Deep exploration as a unifying account of explore-exploit behavior.Bob mentions an influential talk by Benjamin Van Roy:Generalization and Exploration via Value Function Randomization.Bob mentions his paper with Anne Collins:Ten simple rules for the computational modeling of behavioral data.



Timestamps:



0:00 - Intro
4:15 - Central scientific interests
8:32 - Advent of mathematical models
12:15 - Career exploration vs. exploitation
28:03 - Eye movements and active sensing
35:53 - Status of eye movements in neuroscience
44:16 - Why are we curious?
50:26 - Curiosity vs. Exploration vs. Intrinsic motivation
1:02:35 - Directed vs. random exploration
1:06:16 - Deep exploration
1:12:52 - How to know what to pay attention to
1:19:49 - Does AI need curiosity?
1:26:29 - What trait do you wish you had more of?
Released:
May 27, 2021
Format:
Podcast episode

Titles in the series (99)

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.