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Unlocking the Brain's Mysteries: Chris Eliasmith on Spiking Neural Networks and the Future of Human-Machine Interaction

Unlocking the Brain's Mysteries: Chris Eliasmith on Spiking Neural Networks and the Future of Human-Machine Interaction

FromMachine Learning Street Talk (MLST)


Unlocking the Brain's Mysteries: Chris Eliasmith on Spiking Neural Networks and the Future of Human-Machine Interaction

FromMachine Learning Street Talk (MLST)

ratings:
Length:
110 minutes
Released:
Apr 10, 2023
Format:
Podcast episode

Description

Patreon: https://www.patreon.com/mlst
Discord: https://discord.gg/ESrGqhf5CB
Twitter: https://twitter.com/MLStreetTalk

Chris Eliasmith is a renowned interdisciplinary researcher, author, and professor at the University of Waterloo, where he holds the prestigious Canada Research Chair in Theoretical Neuroscience. As the Founding Director of the Centre for Theoretical Neuroscience, Eliasmith leads the Computational Neuroscience Research Group in exploring the mysteries of the brain and its complex functions. His groundbreaking work, including the Neural Engineering Framework, Neural Engineering Objects software environment, and the Semantic Pointer Architecture, has led to the development of Spaun, the most advanced functional brain simulation to date. Among his numerous achievements, Eliasmith has received the 2015 NSERC "Polany-ee" Award and authored two influential books, "How to Build a Brain" and "Neural Engineering."

Chris' homepage:
http://arts.uwaterloo.ca/~celiasmi/

Interviewers: Dr. Tim Scarfe and Dr. Keith Duggar

TOC:

Intro to Chris [00:00:00]
Continuous Representation in Biologically Plausible Neural Networks [00:06:49]
Legendre Memory Unit and Spatial Semantic Pointer [00:14:36]
Large Contexts and Data in Language Models [00:20:30]
Spatial Semantic Pointers and Continuous Representations [00:24:38]
Auto Convolution [00:30:12]
Abstractions and the Continuity [00:36:33]
Compression, Sparsity, and Brain Representations [00:42:52]
Continual Learning and Real-World Interactions [00:48:05]
Robust Generalization in LLMs and Priors [00:56:11]
Chip design [01:00:41]
Chomsky + Computational Power of NNs and Recursion [01:04:02]
Spiking Neural Networks and Applications [01:13:07]
Limits of Empirical Learning [01:22:43]
Philosophy of Mind, Consciousness etc [01:25:35]
Future of human machine interaction [01:41:28]
Future research and advice to young researchers [01:45:06]

Refs:
http://compneuro.uwaterloo.ca/publications/dumont2023.html 
http://compneuro.uwaterloo.ca/publications/voelker2019lmu.html 
http://compneuro.uwaterloo.ca/publications/voelker2018.html
http://compneuro.uwaterloo.ca/publications/lu2019.html 
https://www.youtube.com/watch?v=I5h-xjddzlY
Released:
Apr 10, 2023
Format:
Podcast episode

Titles in the series (100)

This is the audio podcast for the ML Street Talk YouTube channel at https://www.youtube.com/c/MachineLearningStreetTalk Thanks for checking us out! We think that scientists and engineers are the heroes of our generation. Each week we have a hard-hitting discussion with the leading thinkers in the AI space. Street Talk is unabashedly technical and non-commercial, so you will hear no annoying pitches. Corporate- and MBA-speak is banned on street talk, "data product", "digital transformation" are banned, we promise :) Dr. Tim Scarfe, Dr. Yannic Kilcher and Dr. Keith Duggar.