46 min listen
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
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)
BI 005 David Sussillo: RNNs are Back!: Mentioned in the show: David’s Twitter account Papers we discuss: Sussillo, D.S. & Abbott, L. F. (2009). Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron 63(4). Sussillo, D. (2014) Neural circuits as computational by Brain Inspired