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Brain Inspired

BI 173 Justin Wood: Origins of Visual Intelligence

Aug 30, 2023
In this podcast, Justin Wood discusses his work comparing the visual cognition of newborn chicks and AI models. He uses controlled-rearing techniques to understand visual intelligence and build systems that emulate biological organisms. They explore topics like object recognition, reverse engineering, collective behavior, and the potential of transformers in cognitive science.
01:35:45

Podcast summary created with Snipd AI

Quick takeaways

  • Testing whether machine learning systems can develop similar capacities as newborn animals by comparing the visual cognition of newborn chicks and AI models.
  • The significance of slowness and smoothness in learning, where newborn chicks require objects to move slowly and smoothly to develop high-level visual capabilities.

Deep dives

Comparison of machine learning systems and newborn animal capabilities

The podcast episode discusses the goal of testing whether machine learning systems, given the same input as newborn animals, can develop similar capacities. The focus is on understanding the gap between animals and machines and the potential role of critical periods in AI research. The guest, Justin Wood, runs the Wood Lab at Indiana University and compares the early development of natural organisms to artificial agents. The lab conducts controlled rearing experiments with chicks, controlling their visual experiences, and builds AI models trained on the same visual data. The experiments explore visual capabilities such as view-invariant recognition, and present findings on how the trained AI models compare to the newborn chicks in performing tasks.

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