#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 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