Bridging The Gap Between Machine Learning And Affective Computing
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Bridging the Gap between Machine Learning and Affective Computing
Affective computing refers to computing that relates to, arises from, or influences emotions, as pioneered by Rosalind Picard in 1995. The goal of affective computing is to bridge the gap between human and machines and ultimately enable robots to communicate with human naturally and emotionally. Recently, the research on affective computing has gained considerable progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing mainly focuses on estimating of human emotions through different forms of signals, e.g., face video, EEG, Speech, PET scans or fMRI. Inferring the emotion of humans is difficult, as emotion is a subjective, unconscious experience characterized primarily by psycho-physiological expressions and biological reactions. It is influenced by hormones and neurotransmitters such as dopamine, noradrenaline, serotonin, oxytocin, GABA… etc. The physiology of emotion is closely linked to arousal of the nervous system with various states and strengths relating, apparently, to different particular emotions. To understand “emotion” or “affect” merely by machine learning or big data analysis is not enough, but the understanding and applications from the intrinsic features of emotions from the neuroscience aspect is essential.
Applied Affective Computing
Affective computing is a nascent field situated at the intersection of artificial intelligence with social and behavioral science. It studies how human emotions are perceived and expressed, which then informs the design of intelligent agents and systems that can either mimic this behavior to improve their intelligence or incorporate such knowledge to effectively understand and communicate with their human collaborators. Affective computing research has recently seen significant advances and is making a critical transformation from exploratory studies to real-world applications in the emerging research area known as applied affective computing. This book offers readers an overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches to affective computing systems and social signal processing. It provides in-depth case studies of applied affective computing in various domains, such as social robotics and mental well-being. It also addresses ethical concerns related to affective computing and how to prevent misuse of the technology in research and applications. Further, this book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered. For researchers and practitioners new to affective computing, this book will serve as an introduction to the field to help them in identifying new research topics or developing novel applications. For more experienced researchers and practitioners, the discussions in this book provide guidance for adopting a human-centered design and development approach to advance affective computing.
Using Machine Learning to Detect Emotions and Predict Human Psychology
In the realm of analyzing human emotions through Artificial Intelligence (AI), a myriad of challenges persist. From the intricate nuances of emotional subtleties to the broader concerns of ethical considerations, privacy implications, and the ongoing battle against bias, AI faces a complex landscape when venturing into the understanding of human emotions. These challenges underscore the intricate balance required to navigate the human psyche with accuracy. The book, Using Machine Learning to Detect Emotions and Predict Human Psychology, serves as a guide for innovative solutions in the field of emotion detection through AI. It explores facial expression analysis, where AI decodes real-time emotions through subtle cues such as eyebrow movements and micro-expressions. In speech and voice analysis, the book unveils how AI processes vocal nuances to discern emotions, considering elements like tone, pitch, and language intricacies. Additionally, the power of text analysis is of great importance, revealing how AI extracts emotional tones from diverse textual communications. By weaving these systems together, the book offers a holistic solution to the challenges faced by AI in understanding the complex landscape of human emotions.