세미나안내

세미나안내

Human Helping Machine Helping Human: Building Hybrid Intelligent Systems to Improve Human-Machine Collaboration

2023-05-24
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송진영 교수(DGIST) / 2023.05.24

[Abstract]

With the rapid development of software and hardware technologies over the last decade, AI-infused systems have become increasingly prevalent in real-life applications. However, despite their growing popularity, these systems still face a number of challenges in terms of usability, safety, reliability, accountability, transparency, and fairness, among other issues. To address these challenges, it is important to consider the interactions between AI systems and both developers and users at the design stage. By designing interactions that enhance the collaborative performance of humans and machines, we can create AI-infused systems that are not only more effective, but also more secure and trustworthy. In this talk, I will introduce studies that have implemented human-machine hybrid systems, and we will discuss how these systems have contributed to improving the usability of AI services. Overall, this talk aims to shed light on the challenges and opportunities associated with designing effective AI-infused systems, and to provide insights into how we can create more collaborative and productive interactions between humans and machines in the years to come.

[Biography]

Jean Young Song is an Assistant Professor in the Electrical Engineering and Computer Science Department at DGIST. She received her Ph.D. in the Electrical Engineering and Computer Science Department from the University of Michigan in 2019, and her B.S. and M.S. in Electrical and Electronic Engineering from Yonsei University in 2009 and 2011, respectively. Prior to joining DGIST, she worked as a Research Assistant Professor in the School of Computing at KAIST. Her primary research area is human-computer interaction (HCI), with a particular focus on human-AI interaction and crowdsourcing. Her work involves developing techniques to more efficiently and accurately collect machine learning datasets for computer vision and natural language processing. The main goal of her research is to apply HCI methodologies to solve challenging and interesting AI problems. She has received several best paper awards, including from ACM IUI (2018), ACM CSCW (2019), ACM AAMAS (2020), and ACM IUI (2021).

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