I am grateful for have been invited as speaker by
- Mount St. Mary’s University for an ACM distinguished speaker talk. March 2022.
- The Universidad Autonoma del Estado de Morelos (Mexico) for the International Conference on Optimization and Software. October 2021
- Gettysburg College for an ACM distinguished speaker talk. April 2021.
- the Universidad del Chimborazo (Ecuador) for the Second International Conference on Advances in Emerging Trends and Technologies (ICAETT). October 2020.
- the Universidad de Guadalajara, Tonala campus (Mexico) for their 2019 Science Day. November 2019.
- the Universidad Panamericana, Guadalajara campus (Mexico) for their 2019 Programmer Day. September 2019.
A great conversation about Emotion AI and interesting questions from the attendees.
Thank you all !
Summary
Emotion AI is a subset of artificial intelligence that studies and develops systems and devices to recognize, interpret, process, simulate, and react to human emotions. It is an interdisciplinary field spanning computer science, psychology, and cognitive science and represents a step forward in human-computer interaction. Imagine a world in which machines have emotional intelligence, i.e., they can understand the emotional state of humans and adapt their behavior to give appropriate (empathetic) responses to those emotions. Consider these examples: (1) an intelligent tutor detecting students’ affect can realize and respond to a student’s need for support, such as to provide encouraging comments, alter the level of feedback and hints, or adjust task difficulty; (2) a video game can become more compelling by using players’ affect as input to alter and adjust the gaming environment, such as lighting, music, colors, complexity, or level of companionship; (3) an avatar in a virtual world can mirror a human’s affective expressions and become more believable, likable, trustable, and enjoyable; and, (4) a healthcare application can provide empathetic interventions and motivational support to offer assistance and empower patients to improve their quality of life. The key questions to be answered include: What data is used? What pre-processing is needed? What models would be best for each type of data? How can diverse channels be fused? And, what are the challenges for training and testing models?
Video (English version)
This is from Gettysburg College talk.
Slides (English version)
This is from Gettysburg College talk.
Video (Spanish version)
This is from Universidad Autonoma del Estado de Morelos talk.
Slides (Spanish version)
This is from Universidad del Chimborazo talk.