«Fridays’ AI talks» από το Δ.Π.Μ.Σ. στην Τεχνητή Νοημοσύνη

Fridays’ AI talks are digital talks on emergent AI topics, organised by MSc in Artificial Intelligence. The series consist of talks given by scientists and researchers from academia and industry AI experts, and intends to cover the wide spectrum of topics regarding artificial intelligence. We plan to organize 1 talk per month and sporadically even including panel discussions. The target audience for the Fridays’ AI talks includes the current MSc in Artificial Intelligence students, our alumni, members from academia, researchers and practitioners from labs and industry.

Fridays’ AI talk: Human-centered Behavioral Machine Intelligence

Invited speaker: Shrikanth (Shri) Narayanan(link is external), Professor, University of Southern California, Los Angeles, CA, Director of the Signal Analysis and Interpretation Laboratory(link is external)

Friday 20 January 2023, 18.00, Online

“Converging technological advances in sensing, machine learning and computing offer tremendous opportunities for continuous contextually rich yet unobtrusive multimodal, spatiotemporal characterization of an individual’s behavior and state, and of the environment within which they operate. This in turn is enabling novel possibilities for understanding and supporting various aspects of human-centered applications notably in psychological health and well-being. This talk will highlight some of the advances, opportunities and challenges in gathering human-focused data and creating algorithms for machine processing of such cues. It will report efforts in Behavioral Signal Processing (BSP)—technology and algorithms for quantitatively and objectively understanding typical, atypical and distressed human behavior—with a specific focus on communicative, affective and social behavior. Examples will be drawn from health and well-being realms such as Autism Spectrum Disorder, Couple therapy, Depression, Suicidality, and work place behavior. It will also discuss the challenges and opportunities in creating trustworthy signal processing and machine learning approaches that are inclusive, equitable, robust, safe and secure e.g., with respect to protected variables such as gender/race/age/ability etc”.