Machine Learning Street Talk (MLST) artwork

Explainability, Reasoning, Priors and GPT-3

Machine Learning Street Talk (MLST)

English - September 16, 2020 13:34 - 1 hour - 79.6 MB
Technology Homepage Download Google Podcasts Overcast Castro Pocket Casts RSS feed
Previous Episode: SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (Mathilde Caron)
Next Episode: Kernels!

This week Dr. Tim Scarfe and Dr. Keith Duggar discuss Explainability, Reasoning, Priors and GPT-3. We check out Christoph Molnar's book on intepretability, talk about priors vs experience in NNs, whether NNs are reasoning and also cover articles by Gary Marcus and Walid Saba critiquing deep learning. We finish with a brief discussion of Chollet's ARC challenge and intelligence paper. 




00:00:00 Intro


00:01:17 Explainability and Christoph Molnars book on Intepretability


00:26:45 Explainability - Feature visualisation


00:33:28 Architecture / CPPNs


00:36:10 Invariance and data parsimony, priors and experience, manifolds


00:42:04 What NNs learn / logical view of modern AI (Walid Saba article)


00:47:10 Core knowledge


00:55:33 Priors vs experience 


00:59:44 Mathematical reasoning 


01:01:56 Gary Marcus on GPT-3 


01:09:14 Can NNs reason at all? 


01:18:05 Chollet intelligence paper/ARC challenge