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BI 007 Daniel Yamins: Infant AI and CNNs

BI 007 Daniel Yamins: Infant AI and CNNs

FromBrain Inspired


BI 007 Daniel Yamins: Infant AI and CNNs

FromBrain Inspired

ratings:
Length:
62 minutes
Released:
Sep 2, 2018
Format:
Podcast episode

Description

Mentioned in the show:


Dan’s Stanford Neuroscience and Artificial Intelligence Laboratory:


The 2 papers we discuss


Performance-optimized hierarchical models predict neural responses in higher visual cortex


Learning to Play with Intrinsically-Motivated Self-Aware Agents




ImageNet as one of the most important things to stimulate research in AI, developed by these folks.


Ventral visual stream (as opposed to the Dorsal stream).


Retinotopy


Convolutional neural networks were inspired by Kunihiko Fukushima’s Neocognitron


2 modern of approaches to solve the ImageNet database:




Google’s NASNet architecture, with about 12 layers


Microsoft’s super deep ResNet.




Object permanence and some video examples


The distinction between the intrinsic motivation of Dan and colleagues’ AI agent and the reinforcement learning motivation of the OpenAI 5 team
Released:
Sep 2, 2018
Format:
Podcast episode

Titles in the series (100)

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.