The Princeton AI Club is thrilled to announce its next guest speaker – [ https://staff.fnwi.uva.nl/m.welling/ | Max Welling ] from the University of Amsterdam! His talk will focus on how we can apply machine learning to scientific discovery If you want to about new opportunities to apply machine learning to scientific discovery, sign-up for his virtual talk [ https://forms.gle/ms5hewGsMc8SUgaL7 | here ] , and join us on August 15 th , at 10 am Eastern Time (ET) ! The full details are below. Speaker: Prof. Max Welling (University of Amsterdam) Title: Deep Learning and the Natural Sciences: A Perfect Marriage? Date: Monday, August 15 at 4:00PM CEST (which is 10:00 AM EST.) Abstract: Artificial Intelligence has always had a deep connection with the natural sciences. Artificial neural networks were first conceived as abstract ions of biological neural networks, as do many subsequent algorithms such as reinforcement learning. Neural networks have also been inspired by many concepts in physics, for example Hopfield networks are modelled after Ising models. More recently, concepts from physics such as symmetries and the renormalization group have also been incorporated in the design of novel deep architectures. With these new tools, deep learning has completely revolutionized fields such as Natural Language Processing, and Computer Vision. Also in the reverse direction, machine learning has contributed tremendously to the analysis of data from experiments, such as particle collision events at CERN. However, recently, a new scientific paradigm is emerging where deep learning models are accelerating and improving scientific simulations, dubbed by some the fifth scientific paradigm. In this talk I will first give an introduction to the opportunities that lie ahead of us to apply machine learning to scientific discovery. In the second half of the talk, I will talk in more technical detail about symmetries in deep learning and our latest ideas on how to model the hidden layers of a deep neural network as a field described by a partial differential equation. Amazingly, this field-view of the hidden layers of an artificial cortex can be extended to a quantum field and mapped to an optical quantum computer design. How useful this intellectual exercise is in terms of practical quantum computers I will leave to the imagination of the audience. Bio: Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointm z ents include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft. Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).
