Affective Computation
Field within AI that focuses on the design of systems and devices capable of recognizing, interpreting, processing, and simulating human emotions.
Affective computation seeks to bridge the gap between human emotions and machine interactions, enabling computers to respond to users' emotional states in a more intuitive and human-like manner. This involves emotion detection through various inputs like facial expressions, voice tone, physiological signals, and text, followed by appropriate reactions or adjustments in system behavior. Applications include human-computer interaction (HCI), mental health assessment tools, personalized learning systems, and customer service bots that adjust based on user sentiment. By incorporating emotional intelligence into machines, affective computation enhances the user experience and allows for more personalized, empathetic interactions.
The term "affective computing" was first introduced by Rosalind Picard in her 1997 book Affective Computing, which laid the groundwork for the field. The concept gained wider attention in the early 2000s as interest in human-computer interaction and emotional AI grew, especially with the rise of machine learning techniques that allowed for better emotion recognition.
Rosalind Picard, a professor at MIT, is the primary figure associated with the development of affective computing, having coined the term and led foundational research in the area. Picard’s work, along with contributions from the MIT Media Lab, played a crucial role in shaping the direction and evolution of the field.