Published Feb 28, 2022

#66 ALEXANDER MATTICK - [Unplugged / Community Edition]

Alexander Mattick and Tim Scarfe dive into the complexities of causal learning in machine learning, dissecting philosophical challenges and exploring neural network theories, while highlighting the importance of knowledge transfer for scientific advancement.

Episode Highlights

Topics covered

Episode Highlights

Related Episodes

#65 Prof. PEDRO DOMINGOS [Unplugged]
Answers 383 questions
#71 - ZAK JOST (Graph Neural Networks + Geometric DL) [UNPLUGGED]
Answers 383 questions
#64 Prof. Gary Marcus 3.0
Answers 383 questions
Robert Lange on NN Pruning and Collective Intelligence
Answers 383 questions
#82 - Dr. JOSCHA BACH - Digital Physics, DL and Consciousness [UNPLUGGED]
Answers 383 questions
#035 Christmas Community Edition!
Answers 383 questions
Kernels!
Answers 383 questions
#83 Dr. ANDREW LAMPINEN (Deepmind) - Natural Language, Symbols and Grounding [NEURIPS2022 UNPLUGGED]
Answers 383 questions
#74 Dr. ANDREW LAMPINEN - Symbolic behaviour in AI [UNPLUGGED]
Answers 383 questions
#041 - Biologically Plausible Neural Networks - Dr. Simon Stringer
Answers 383 questions
MLST #78 - Prof. NOAM CHOMSKY (Special Edition)
Answers 383 questions
Dr. Paul Lessard - Categorical/Structured Deep Learning
Answers 383 questions
Unlocking the Brain's Mysteries: Chris Eliasmith on Spiking Neural Networks and the Future of Human-Machine Interaction
Answers 383 questions
#107 - Dr. RAPHAËL MILLIÈRE - Linguistics, Theory of Mind, Grounding
Answers 383 questions
#72 Prof. KEN STANLEY 2.0 - On Art and Subjectivity [UNPLUGGED]
Answers 383 questions

Dexa/Machine Learning Street Talk (MLST)

#66 ALEXANDER MATTICK - [Unplugged / Community Edition]

Topics covered

Popular Clips

Logic Program Induction

Knowledge Transfer Efficiency

Broad Knowledge Exploration

Neural Network Abstraction

Causality in Machine Learning

Abstract Representations Debate

Complex Systems Challenge

Discrete vs. Continuous

Duality of Concepts

Neural Network Semantics

Search and Interestingness

Universe's Discrete Nature

Causal Learning Challenges

Problem-Solution Dynamics

Episode Highlights

Causality Exploration

Knowledge and Exploration

Neural Network Theory

Related Episodes

#65 Prof. PEDRO DOMINGOS [Unplugged]

#71 - ZAK JOST (Graph Neural Networks + Geometric DL) [UNPLUGGED]

#64 Prof. Gary Marcus 3.0

Robert Lange on NN Pruning and Collective Intelligence

#82 - Dr. JOSCHA BACH - Digital Physics, DL and Consciousness [UNPLUGGED]

#035 Christmas Community Edition!

Kernels!

#83 Dr. ANDREW LAMPINEN (Deepmind) - Natural Language, Symbols and Grounding [NEURIPS2022 UNPLUGGED]

#74 Dr. ANDREW LAMPINEN - Symbolic behaviour in AI [UNPLUGGED]

#041 - Biologically Plausible Neural Networks - Dr. Simon Stringer

MLST #78 - Prof. NOAM CHOMSKY (Special Edition)

Dr. Paul Lessard - Categorical/Structured Deep Learning

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

#107 - Dr. RAPHAËL MILLIÈRE - Linguistics, Theory of Mind, Grounding

#72 Prof. KEN STANLEY 2.0 - On Art and Subjectivity [UNPLUGGED]

#66 ALEXANDER MATTICK - [Unplugged / Community Edition]

Topics covered

Popular Clips

Episode Highlights

Causality Exploration

Knowledge and Exploration

Neural Network TheoryAlexander Mattick discusses the intricacies of neural network theory, focusing on spline theory, abstraction, and the discrete versus continuous debate. He provides insights into how these models function and their limitations in replicating human cognition.

Neural Network Theory

Related Episodes