Home > Ming Yin - Making Better Use of the Crowd to Enhance AI

Ming Yin - Making Better Use of the Crowd to Enhance AI

Start:

10/29/2020 at 3:55PM

End:

10/29/2020 at 5:10PM

Location:

(This seminar is a virtual talk)

Host:

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Taeho Jung

Taeho Jung

VIEW FULL PROFILE Email: tjung@nd.edu
Phone: 574-631-8322
Website: https://sites.nd.edu/taeho-jung/
Office: 351 Fitzpatrick Hall

Affiliations

College of Engineering Assistant Professor
Big data security, user privacy, privacy-preserving computation, accountability
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574-631-8322
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 Artificial Intelligence (AI) has made remarkable progress in the past decade. Intelligent systems that surpass human performance in areas like object detection and speech recognition have been developed, and innovative machine learning technologies have shown tremendous potential in various domains from finance, to healthcare, to criminal justice. Underlying the recent success of AI is a growing partnership between humans and machine; for example, human intelligence is solicited via crowdsourcing to enhance machine intelligence. While the crowd has long been contributing to the development of AI in the role of data generators, in this talk, I’ll present a few projects on how to make better use of the crowd to enhance AI. Specifically, I’ll discuss how to increase the efficiency of crowdsourcing systems in generating high-quality data, how to leverage the wisdom of the crowd to discover potential problems underlying an AI system, and how to conduct behavioral studies with human subjects to inform the design of AI systems.

 

 

 

Seminar Speaker:

Ming Yin

Ming Yin

Purdue University

Ming Yin is an assistant professor in the Department of Computer Science, Purdue University. Her research broadly connects to the fields of human-computer interaction, applied artificial intelligence and machine learning, computational social science, and behavioral sciences. Ming's research focuses on using both experimental and computational approaches to examine how to better utilize the wisdom of crowd to enhance machine intelligence (e.g., crowdsourcing), and how to better design intelligent systems that people can understand, trust and engage with effectively (e.g., human-AI interaction). Ming has received Best Paper Honorable Mention Award at the ACM Conference on Human Factors in Computing Systems (CHI), in 2016 and 2019. Prior to Purdue, Ming spent a year at Microsoft Research New York City as a postdoctoral researcher in the Computational Social Science group. Ming completed her Ph.D. in Computer Science at Harvard University, and received her bachelor degree from Tsinghua University, Beijing, China.