TY - GEN
T1 - Artificial intelligence for HCI
T2 - 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI EA 2020
AU - Li, Yang
AU - Kumar, Ranjitha
AU - Lasecki, Walter S.
AU - Hilliges, Otmar
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/4/25
Y1 - 2020/4/25
N2 - Artificial intelligence (AI) and Human Computer Interaction (HCI) share common roots and early work on conversational agents has laid the foundation for both fields. However, in subsequent decades the initial tight connection between the fields has become less pronounced. The recent rise of deep learning has revolutionized AI and has led to a raft of practical methods and tools that significantly impact areas outside of core-AI. In particular, modern AI techniques now power new ways for machines and humans to interact. Thus it is timely to investigate how modern AI can propel HCI research in new ways and how HCI research can help direct AI developments. This workshop offers a forum for researchers to discuss new opportunities that lie in bringing modern AI methods into HCI research, identifying important problems to investigate, showcasing computational and scientific methods that can be applied, and sharing datasets and tools that are already available or proposing those that should be further developed. The topics we are interested in including deep learning methods for understanding and modeling human behaviors and enabling new interaction modalities, hybrid intelligence that combine human and machine intelligence to solve difficult tasks, and tools and methods for interaction data curation and large-scale data-driven design. At the core of these topics, we want to start the conversation on how data-driven and data-centric approaches of modern AI can impact HCI.
AB - Artificial intelligence (AI) and Human Computer Interaction (HCI) share common roots and early work on conversational agents has laid the foundation for both fields. However, in subsequent decades the initial tight connection between the fields has become less pronounced. The recent rise of deep learning has revolutionized AI and has led to a raft of practical methods and tools that significantly impact areas outside of core-AI. In particular, modern AI techniques now power new ways for machines and humans to interact. Thus it is timely to investigate how modern AI can propel HCI research in new ways and how HCI research can help direct AI developments. This workshop offers a forum for researchers to discuss new opportunities that lie in bringing modern AI methods into HCI research, identifying important problems to investigate, showcasing computational and scientific methods that can be applied, and sharing datasets and tools that are already available or proposing those that should be further developed. The topics we are interested in including deep learning methods for understanding and modeling human behaviors and enabling new interaction modalities, hybrid intelligence that combine human and machine intelligence to solve difficult tasks, and tools and methods for interaction data curation and large-scale data-driven design. At the core of these topics, we want to start the conversation on how data-driven and data-centric approaches of modern AI can impact HCI.
KW - Algorithms and tools
KW - Artificial intelligence
KW - Crowdsourcing
KW - Data-driven design and modeling
KW - Deep learning
KW - Design guidelines
KW - Human computer interaction
KW - Sensing
UR - http://www.scopus.com/inward/record.url?scp=85090247895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090247895&partnerID=8YFLogxK
U2 - 10.1145/3334480.3375147
DO - 10.1145/3334480.3375147
M3 - Conference contribution
AN - SCOPUS:85090247895
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI EA 2020 - Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 25 April 2020 through 30 April 2020
ER -