#036 - Max Welling: Quantum, Manifolds & Symmetries in ML

Today we had a fantastic conversation with Professor Max Welling, VP of Technology, Qualcomm Technologies Netherlands B.V. 

Max is a strong believer in the power of data and computation and its relevance to artificial intelligence. There is a fundamental blank slate paradgm in machine learning, experience and data alone currently rule the roost. Max wants to build a house of domain knowledge on top of that blank slate. Max thinks there are no predictions without assumptions, no generalization without inductive bias. The bias-variance tradeoff tells us that we need to use additional human knowledge when data is insufficient.

Max Welling has pioneered many of the most sophistocated inductive priors in DL models developed in recent years, allowing us to use Deep Learning with non-euclidean data i.e. on graphs/topology (a field we now called "geometric deep learning") or allowing network architectures to recognise new symmetries in the data for example gauge or SE(3) equivariance. Max has also brought many other concepts from his physics playbook into ML, for example quantum and even Bayesian approaches. 

This is not an episode to miss, it might be our best yet! 

Panel: Dr. Tim Scarfe, Yannic Kilcher, Alex Stenlake

00:00:00 Show introduction 

00:04:37 Protein Fold from DeepMind -- did it use SE(3) transformer? 

00:09:58 How has machine learning progressed 

00:19:57 Quantum Deformed Neural Networks paper 

00:22:54 Probabilistic Numeric Convolutional Neural Networks paper

00:27:04 Ilia Karmanov from Qualcomm interview mini segment

00:32:04 Main Show Intro 

00:35:21 How is Max known in the community? 

00:36:35 How Max nurtures talent, freedom and relationship is key 

00:40:30 Selecting research directions and guidance 

00:43:42 Priors vs experience (bias/variance trade-off) 

00:48:47 Generative models and GPT-3 

00:51:57 Bias/variance trade off -- when do priors hurt us 

00:54:48 Capsule networks 

01:03:09 Which old ideas whould we revive 

01:04:36 Hardware lottery paper 

01:07:50 Greatness can't be planned (Kenneth Stanley reference) 

01:09:10 A new sort of peer review and originality 

01:11:57 Quantum Computing 

01:14:25 Quantum deformed neural networks paper 

01:21:57 Probabalistic numeric convolutional neural networks 

01:26:35 Matrix exponential 

01:28:44 Other ideas from physics i.e. chaos, holography, renormalisation 

01:34:25 Reddit 

01:37:19 Open review system in ML 

01:41:43 Outro