After studying the tribe, which was still living in the preliterate state it had been in since the Stone Age, Ekman believed he had found the blueprint for a set of universal human emotions and related expressions that crossed cultures and were present in all humans. A decade later he created the Facial Action Coding System, a comprehensive tool for objectively measuring facial movement. Ekman's work has been used by the FBI and police departments to identify the seeds of violent behavior in nonverbal expressions of sentiment. He has also developed the online Atlas of Emotions at the behest of the Dalai Lama. And today his research is being used to teach computer systems how to feel.
Artificial intelligence that reads and responds to our emotions is the killer app of the digital economy. It will make customers and employees happier--as long as it learns to respect our boundaries. When psychologist Dr. Paul Ekman visited the Fore tribe in the highlands of Papua New Guinea in 1967, he probably didn't imagine that his work would become the foundation for some of the latest developments in artificial intelligence (AI). After studying the tribe, which was still living in the preliterate state it had been in since the Stone Age, Ekman believed he had found the blueprint for a set of universal human emotions and related expressions that crossed cultures and were present in all humans. A decade later he created the Facial Action Coding System, a comprehensive tool for objectively measuring facial movement.
Many people get frustrated with technology when it malfunctions or is counterintuitive. The last thing people might expect is for that same technology to pick up on their emotions and engage with them differently as a result. All of that is now changing. Computers are increasingly able to figure out what we're feeling--and it's big business. A recent report predicts that the global affective computing market will grow from $12.2 billion in 2016 to $53.98 billion by 2021.
By Natalia Modjeska, MBA, PhD, helps organizations make sense of AI/ML. Recently I had the opportunity to attend the inaugural Emotion AI Conference, organized by Seth Grimes, a leading analyst and business consultant in the areas of natural language processing (NLP), text analytics, sentiment analysis, and their business applications. The conference was attended by about 70 people (including presenters and panelists) from industry and academia in the US, Canada, and Europe. Given the conference topic, what is emotion AI, why is it relevant, and what do you need to know about it? Read on to find out (warning: this is a long-ish article), but first, some background. We humans are highly emotional beings, and emotions impact everything we do, even if we are not, for the most part, aware of it.
What did you think of the last commercial you watched? Would you buy the product? You might not remember or know for certain how you felt, but increasingly, machines do. New artificial intelligence technologies are learning and recognizing human emotions, and using that knowledge to improve everything from marketing campaigns to health care. These technologies are referred to as "emotion AI." Emotion AI is a subset of artificial intelligence (the broad term for machines replicating the way humans think) that measures, understands, simulates, and reacts to human emotions.