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Daniel S. Weld is Thomas J. Cable / WRF Professor of Computer Science & Engineering at the University of Washington and an Entrepreneurial Faculty Fellow. After formative education at Phillips Academy, he received bachelor's degrees in both CS and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab in 1988, received a Presidential Young Investigator's award in 1989, an Office of Naval Research Young Investigator's award in 1990, was named AAAI Fellow in 1999 and deemed ACM Fellow in 2005. Dan was a founding editor for the Journal of AI Research, was area editor for the Journal of the ACM, guest editor for Computational Intelligence and Artificial Intelligence, and was Program Chair for AAAI-96. Dan has published two books and scads of technical papers.
Dan is an active entrepreneur with several patents and technology licenses. He co-founded Netbot Incorporated, creator of Jango Shopping Search (acquired by Excite), AdRelevance, a monitoring service for internet advertising, (acquired by Media Metrix), Nimble Technology, a data integration company (acquired by Actuate). Dan is a Venture Partner at the Madrona Venture Group, and a member of the Technical Advisory Boards for the Allen Institute for Artificial Intelligence, Context Relevant, Spare5, and Madrona.
Crowd-sourcing labor markets (e.g., Amazon Mechanical Turk) are booming, because they enable rapid construction of complex workflows that seamlessly mix human computation with computer automation. Example applications range from photo tagging to audio-visual transcription and interlingual translation. Similarly, workflows on citizen science sites (e.g. GalaxyZoo) have allowed ordinary people to pool their effort and make interesting discoveries. Unfortunately, constructing a good workflow is difficult, because the quality of the work performed by humans is highly variable. Typically, a task designer will experiment with several alternative workflows to accomplish a task, varying the amount of redundant labor, until she devises a control strategy that delivers acceptable performance.
Fortunately, this control challenge can often be formulated as an automated planning problem ripe for algorithms from the probabilistic planning and reinforcement learning literature. I describe our recent work on the decision-theoretic control of crowd sourcing and suggest open problems for future research. In particular, I discuss:
Ed H. Chi is a Staff Research Scientist at Google, leading a team in social computing and recommender research. With over 30 patents and over 90 publications, he is known for research in Web and online social sites, and the effects of social signals on user behavior, as well as past research on web data mining and user modeling, information visualization, and crowdsourcing. Previous to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Area. He completed three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. He was recently recognized as an ACM Distinguished Scientist. In his spare time, Ed is an avid photographer and snowboarder.
Search and recommendation engines are increasingly more intelligent. They have become more personalized and social as well as more interactive. No longer just offering ten blue links, search engines have increasingly been integrated with task and item recommenders directly, for example, to offer news, movie, music, and dining suggestions. Vice versa, recommendation systems have increasingly became more search-like by offering capabilities that enable users to tune and direct recommendation results instantly.
As the two technologies evolve toward each other, there is increasingly a blurring of the boundary between these two approaches to interactive information seeking. On the search side, this is driven by the merging of question answering capabilities with search, led by systems like Google Now and Apple Siri that move search toward intelligent personal assistants. On the recommendation side, there has been a merging of techniques from not just keyword search but also faceted search, along with user-based and item-based collaborative filtering techniques and other more proactive recommenders.
This blurring has resulted in both critical re-thinking about not just how to architect the systems by merging and sharing backend components common to both types of systems, but also how to structure the user interactions and experiences.