What is emotional AI, and how is it used today?
Emotional AI is a subset of artificial intelligence that recognizes human emotions using biometrics technologies.
It is becoming increasingly popular in various industries, such as finance, healthcare, and security.
Companies use AI algorithms to create targeted ads, call centres, psychological assistance, and automotive systems.
Emotional AI (sometimes referred to as affective computing) is a subset of artificial intelligence that recognizes human emotions. For this task, biometrics technologies — face recognition and natural language processing. According to recent forecasts, this segment of AI may grow from $37.8 billion in 2021 to $620 billion in 2030.
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Biometrics technologies are becoming increasingly popular in various industries, including finance, healthcare, and security. With the rise of AI-powered biometrics, businesses can enhance their security measures and improve customer experience by offering personalized services.
Advertising. Using emotional AI algorithms, companies create targeted ads that increase customer engagement. One of the main companies in this market is Affectiva. The startup is developing software that analyses the smallest changes in a person’s behaviour and emotional state.
Call centres. Here, AI is used to analyse the work of the operator and customer satisfaction. For example, Cogito has developed a service that analyses the voice of the client and the topics of conversation and prompts the operator on how to behave better.
Psychological assistance. Some believe that AI can more accurately determine the emotional and mental state of a patient than a professional doctor. For example, Twill has developed the Intelligent Healing platform, which analyses a person’s condition and can develop a personal therapy course for him.
Automotive. Already today, there are systems that analyse the state of the driver during the trip and adapt to it. Harman’s system can, at the first sign of stress, play soothing music or change lighting settings to reassure the driver.
The Cons of Using Emotional AI
The main problem with these algorithms is how adaptive they are. A poor-quality model may not recognize the emotional state or facial expressions of a person. For example, there are cases when algorithms, for no apparent reason, recognize an elderly person driving as tired. Algorithm errors can be especially critical when it comes to medical applications. In medical applications, algorithm errors can lead to misdiagnosis or incorrect treatment recommendations, which can have serious consequences for patients. Therefore, it is crucial to thoroughly test and validate algorithms before deploying them in such critical settings.
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