Uploaded August 2023 | Updated September 2026, 1 hour ago
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Spotify:
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Music by: The New Year:
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Show notes:
braininspired.co/podcast/173
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.
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
Patreon for full episodes and Discord community:
patreon.com/braininspired
Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Apple podcasts:
itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify:
open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
Music by: The New Year:
thenewyear.net
Show notes:
braininspired.co/podcast/173
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.
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
