43 min listen
BI 173 Justin Wood: Origins of Visual Intelligence
FromBrain Inspired
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Length:
96 minutes
Released:
Aug 30, 2023
Format:
Podcast episode
Description
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In the intro, I mention the Bernstein conference workshop I'll participate in, called How can machine learning be used to generate insights and theories in neuroscience?. Follow that link to learn more, and register for the conference here. Hope to see you there in late September in Berlin!
Justin Wood runs the Wood Lab at Indiana University, and his lab's tagline is "building newborn minds in virtual worlds." In this episode, we discuss his work comparing the visual cognition of newborn chicks and AI models. He uses a controlled-rearing technique with natural chicks, whereby the chicks are raised from birth in completely controlled visual environments. That way, Justin can present designed visual stimuli to test what kinds of visual abilities chicks have or can immediately learn. Then he can building models and AI agents that are trained on the same data as the newborn chicks. The goal is to use the models to better understand natural visual intelligence, and use what we know about natural visual intelligence to help build systems that better emulate biological organisms. We discuss some of the visual abilities of the chicks and what he's found using convolutional neural networks. Beyond vision, we discuss his work studying the development of collective behavior, which compares chicks to a model that uses CNNs, reinforcement learning, and an intrinsic curiosity reward function. All of this informs the age-old nature (nativist) vs. nurture (empiricist) debates, which Justin believes should give way to embrace both nature and nurture.
Wood lab.
Related papers:
Controlled-rearing studies of newborn chicks and deep neural networks.
Development of collective behavior in newborn artificial agents.
A newborn embodied Turing test for view-invariant object recognition.
Justin mentions these papers:
Untangling invariant object recognition (Dicarlo & Cox 2007)
0:00 - Intro
5:39 - Origins of Justin's current research
11:17 - Controlled rearing approach
21:52 - Comparing newborns and AI models
24:11 - Nativism vs. empiricism
28:15 - CNNs and early visual cognition
29:35 - Smoothness and slowness
50:05 - Early biological development
53:27 - Naturalistic vs. highly controlled
56:30 - Collective behavior in animals and machines
1:02:34 - Curiosity and critical periods
1:09:05 - Controlled rearing vs. other developmental studies
1:13:25 - Breaking natural rules
1:16:33 - Deep RL collective behavior
1:23:16 - Bottom-up and top-down
In the intro, I mention the Bernstein conference workshop I'll participate in, called How can machine learning be used to generate insights and theories in neuroscience?. Follow that link to learn more, and register for the conference here. Hope to see you there in late September in Berlin!
Justin Wood runs the Wood Lab at Indiana University, and his lab's tagline is "building newborn minds in virtual worlds." In this episode, we discuss his work comparing the visual cognition of newborn chicks and AI models. He uses a controlled-rearing technique with natural chicks, whereby the chicks are raised from birth in completely controlled visual environments. That way, Justin can present designed visual stimuli to test what kinds of visual abilities chicks have or can immediately learn. Then he can building models and AI agents that are trained on the same data as the newborn chicks. The goal is to use the models to better understand natural visual intelligence, and use what we know about natural visual intelligence to help build systems that better emulate biological organisms. We discuss some of the visual abilities of the chicks and what he's found using convolutional neural networks. Beyond vision, we discuss his work studying the development of collective behavior, which compares chicks to a model that uses CNNs, reinforcement learning, and an intrinsic curiosity reward function. All of this informs the age-old nature (nativist) vs. nurture (empiricist) debates, which Justin believes should give way to embrace both nature and nurture.
Wood lab.
Related papers:
Controlled-rearing studies of newborn chicks and deep neural networks.
Development of collective behavior in newborn artificial agents.
A newborn embodied Turing test for view-invariant object recognition.
Justin mentions these papers:
Untangling invariant object recognition (Dicarlo & Cox 2007)
0:00 - Intro
5:39 - Origins of Justin's current research
11:17 - Controlled rearing approach
21:52 - Comparing newborns and AI models
24:11 - Nativism vs. empiricism
28:15 - CNNs and early visual cognition
29:35 - Smoothness and slowness
50:05 - Early biological development
53:27 - Naturalistic vs. highly controlled
56:30 - Collective behavior in animals and machines
1:02:34 - Curiosity and critical periods
1:09:05 - Controlled rearing vs. other developmental studies
1:13:25 - Breaking natural rules
1:16:33 - Deep RL collective behavior
1:23:16 - Bottom-up and top-down
Released:
Aug 30, 2023
Format:
Podcast episode
Titles in the series (99)
BI 100.1 Special: What Has Improved Your Career or Well-being?: Brain Inspired turns 100 (episodes) today! To celebrate, my patreon supporters helped me create a list of questions to ask my previous guests, many of whom contributed by answering any or all of the questions. Ive collected all their responses into separ by Brain Inspired