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

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

Machine Learning Street Talk (MLST)

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