BI 032 Rafal Bogacz: Back-Propagation in Brains

BI 032 Rafal Bogacz: Back-Propagation in Brains

BI 032 Rafal Bogacz: Back-Propagation in Brains

 
 
00:00 / 01:15:44
 
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Rafal and I discuss many of the ways back-propagation could be approximated in brains as detailed in his recent Trends in Cognitive Sciences review. We also cover how brains and machines learn, the free energy principle with its predictions and implications related to back-prop and understanding brains in general, and more.

BI 031 Francisco de Sousa Webber: Natural Language Understanding

BI 031 Francisco de Sousa Webber: Natural Language Understanding

BI 031 Francisco de Sousa Webber: Natural Language Understanding

 
 
00:00 / 01:44:47
 
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Francisco and I discuss language and brains, his company cortical.io that uses his Semantic Folding Theory about how brains process language to perform natural language processing on text for many purposes, and the world of making and running companies like his own.

BI 030 Jay McClelland: Mathematical Reasoning and PDP

BI 030 Jay McClelland: Mathematical Reasoning and PDP

BI 030 Jay McClelland: Mathematical Reasoning and PDP

 
 
00:00 / 01:04:40
 
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Jay and I discuss his rather ambitious project to implement mathematical reasoning in an AI agent. Plus his prominent role in and experience of the history of parallel distributed processing and neural network architectures taking over symbolic “good ol’ fashioned artificial intelligence” in the 1980s, and lots more.

BI 029 Paul Humphreys & Zac Irving: Emergence & Mind Wandering

BI 029 Paul Humphreys & Zac Irving: Emergence & Mind Wandering

BI 029 Paul Humphreys & Zac Irving: Emergence & Mind Wandering

 
 
00:00 / 01:44:23
 
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Paul, Zac, and I discuss the philosophy of emergence, the neuroscience and philosophy of mind wandering and spontaneous thought, and how both of these may fit in the realm of artificial intelligence and consciousness. Plus, we talk about the role of philosophy in neuroscience and AI, and more.

BI 028 Sam Gershman: Free Energy Principle & Human Machines

BI 028 Sam Gershman: Free Energy Principle & Human Machines

BI 028 Sam Gershman: Free Energy Principle & Human Machines

 
 
00:00 / 01:14:09
 
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Sam and I talk about the Free Energy Principle and how it relates to the Bayesian Brain Hypothesis, all under the realm of predictive coding. We also discuss some of the ingredients currently missing from deep learning approaches if we want machines to learn and think more like humans.