Pushing the boundaries of AI research at Qualcomm - Max Welling (University of Amsterdam & Qualcomm)

Conference Website: https://saiconference.com/IntelliSys

Abstract: Artificial Intelligence (AI) is revolutionizing industries, products, and core capabilities by delivering dramatically enhanced experiences. However, this is just the start of the AI revolution. The field of AI, especially deep learning, is still in its infancy with tremendous opportunity for exploration and improvement. For instance, deep neural networks of today are rapidly growing in size and use too much memory, compute, and energy. To make AI truly ubiquitous, it needs to run on the end device within a tight power and thermal budget. New approaches and fundamental research in AI, as well as applying that research, is required to advance machine learning further and speed up adoption. In this talk, I’ll discuss select research topics that Qualcomm AI Research is investigating, including:
AI model optimization research for power efficiency, including our latest quantization research
Applied AI research, such as using deep learning for improved radar functionality
Fundamental AI research, such as source compression and quantum AI

About the Speaker: Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a VP Technologies at Qualcomm. He has a secondary appointment as a fellow at the Canadian Institute for Advanced Research (CIFAR). Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015. He serves on the 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. He is a founding board member of ELLIS. Max Welling is recipient of the ECCV Koenderink Prize in 2010. 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). He has over 300 publications in machine learning and an h-index of 66.