Hybrid Intelligence Crowdsourcing for Robust Interactive Intelligent Systems
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Intelligent systems are poised to become ubiquitous, but there is a snag: artificial intelligence is far from being able to understand (e.g., via natural language or vision) and reason about the world in general. Machine learning (ML) has had significant and wide-spread success for specific classes of problems, but generating the massive, tailored training data sets that are needed to make ML algorithms work reliably are hard to generate, and transferring that knowledge to new domains remains challenging. Crowdsourcing has provided a means of collecting data at scale, but is typically an offline process that takes days or weeks to generate data. In this talk, I will discuss my lab's work on real-time crowdsourcing and show how human insight can be brought to bear on novel problems when and where they are encountered by intelligent systems in the wild. The resulting "hybrid intelligence" systems can learn, on-the-fly, to perform tasks more reliably and more robustly than either humans or machines could alone.
Walter S. Lasecki is an Assistant Professor of Computer Science and Engineering at the University of Michigan — Ann Arbor, where he directs the Crowds+Machines (CROMA) Lab. He and his students create interactive intelligent systems that are robust enough to be used in real-world settings by combining both human and machine intelligence to exceed the capabilities of either. These systems let people be more productive, and improve access to the world for people with disabilities. Dr. Lasecki received his Ph.D and M.S. from the University of Rochester in 2015 and a B.S. in Computer Science and Mathematics from Virginia Tech in 2010. He has previously held visiting research positions at CMU, Stanford, Microsoft Research, and Google[x].