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

In this AI video ...

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 paradigm 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 trade-off tells us that we need to use additional human knowledge when data is insufficient.

Max Welling has pioneered many of the most sophisticated 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

Pod version: https://anchor.fm/machinelearningstreettalk/episodes/036—Max-Welling-Quantum–Manifolds–Symmetries-in-ML-eogoe8

Ilia Karmanov, Senior Engineer, Qualcomm Technologies Netherlands B.V.:
https://www.linkedin.com/in/ilia-karmanov-09aa588b/
Professor Max Welling, VP of Technology, Qualcomm Technologies Netherlands B.V.:
https://www.linkedin.com/in/max-welling-4a783910/

Probabilistic Numeric Convolutional Neural Networks (Marc Finzi, Roberto Bondesan, Max Welling)
https://arxiv.org/abs/2010.10876

Quantum Deformed Neural Networks
https://arxiv.org/abs/2010.11189 (Roberto Bondesan, Max Welling)

Qualcomm AI Research is hiring for several machine learning openings, so please check out the Qualcomm careers website if you’re excited about solving big problems with cutting-edge AI research — and improving the lives of billions of people.

https://www.qualcomm.com/company/careers

We used a clip from Qualcomm’s official video on Gauge Equivariant Convolutional Networks with permission: https://www.youtube.com/watch?v=x1WRwq4tLlg

The drone footage is from my friend Marcus White — https://www.youtube.com/watch?v=_fG0uY0fhf8 and used with his permission

Intro music: https://soundcloud.com/beatskim/homeward

Disclaimer: We have had official approval from Qualcomm to publish this video, and they have not paid us anything!

#machinelearning #deeplearning

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