Artificial Intelligence and Machine Learning have rapidly developed in recent years. Large language model AI took the stage last year, demonstrating how AI can be trained to understand us. Yet, there is still something missing from the current AI and human interaction: emotion.
One of the limiting factors in AI is that, by default, it is merely a machine trained to respond to whatever it was trained upon. Even AI with highly advanced machine learning is limited to only providing what is asked without fully understanding why. This lack of connection still separates man from machine, and why man feels something missing when interacting with AI.
To address this missing element, in 1995, researchers at MIT started looking at "Affective Computing." Then MIT Media Lab professor Rosalind Picard described Affective Computing as "computing that relates to, arises from, or deliberately influences emotions." Now, it is also known as Affective AI and Emotional AI.
Emotional AI, simply put, is a subset of AI that analyzes human emotion. It utilizes various data like language, voice/ audio, and other cues to determine how to react to certain things. Emotional AI is already part of our world, with its most common use found in marketing.
Emotional AI is used for product review analysis and recommendation tailoring. They're among the AI tools behind some of your shopping algorithms and even video recommendations.
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