Abstract

Multimodal machine intelligence offers enormous possibilities for helping understand the human condition and in creating technologies to support and enhance human experiences [1, 2]. What makes such approaches and systems exciting is the promise they hold for adaptation and personalization in the presence of the rich and vast inherent heterogeneity, variety and diversity within and across people. Multimodal engineering approaches can help analyze human trait (e.g., age), state (e.g., emotion), and behavior dynamics (e.g., interaction synchrony) objectively, and at scale. Machine intelligence could also help detect and analyze deviation in patterns from what is deemed typical. These techniques in turn can assist, facilitate or enhance decision making by humans, and by autonomous systems. Realizing such a promise requires addressing two major lines of, oft intertwined, challenges: creating inclusive …

Date

2020

Authors

Shrikanth Shri Narayanan

Book

Proceedings of the 2020 International Conference on Multimodal Interaction

Pages

4-5