From Intelligence Augmentation to Artificial Intelligence


Artificial intelligence advances over the last two decades have transformed computing. This has been achieved by identifying methods that scale more easily. E.g., instead of manually hand-crafting features for every task of interest to us, we use methods which automatically identify and extract features from data. Because those methods reduce the development cycles we are able to develop technology for increasingly complex tasks. For example, detecting faces and other objects in images is now seemingly easier than it has been two decades ago.This improved scalability has helped humanity to develop tools for intelligence augmentation (IA). I.e., tasks in many domains like agriculture, banking, education, medicine, robotics, etc. can now be addressed more reliably and more robustly than ever before.However, according to my opinion, developing solutions for individual domains doesn’t scale sufficiently. We can’t hope to reach artificial intelligence (AI) if we are discussing every task within a domain independently. The “future of computing” therefore has to find methods that scale even more effectively than possible at the moment.In this talk I will outline thoughts on techniques that may have the potential to improve scalability. I will also discuss the social implications of these computing transformations that we can’t ignore as researchers because they will be even more profound than what we currently experience.


Alex Schwing is an Assistant Professor at the University of Illinois at Urbana-Champaign working with talented students on computer vision and machine learning topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of computer vision and machine learning, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award. For additional info, please browse to