Emotion AI, also known as affective computing, is a wide range of technologies used to learn and sense human emotions with the help of artificial intelligence (AI). Capitalizing on text, video, and audio data, Emotion AI analyzes several sources to interpret human signals. For instance:
- Natural language processing and sentimental analysis are used for textual data.
- Voice AI is used for processing audio.
- Facial motion detection and gait analysis for videos.
Recently, Emotion AI is experiencing a greater demand due to its numerous practical applications that can reduce the gap between humans and machines. In fact, a report by MarketsandMarkets Research suggests that the emotion detection market size is expected to surpass $42 billion by 2027, compared to $23.5 billion in 2022.
Let’s explore how this amazing sub-category of AI works.
How Does Emotion AI Work?
Like any other AI technique, Emotion AI needs data to improve performance and understand users’ emotions. The data varies from one use case to another. For instance, activity on social media, speech and actions in video recordings, physiological sensors in devices, etc., are used to understand the emotions of the audience.
Afterward, the process of feature engineering takes place where relevant features impacting emotions are identified. For facial emotion recognition, eyebrow movement, mouth shape, and eye gaze can be used to determine if a person is happy, sad, or angry. Similarly, pitch, volume, and tempo in speech-based emotion detection can deduce if a person is excited, frustrated, or bored.
Later, these features are pre-processed and used to train a machine learning algorithm that can accurately predict the emotional states of users. Finally, the model is deployed in real-world applications to improve user experience, increase sales, and recommend appropriate content.
4 Important Applications of Emotion AI
Companies leverage Emotion AI models to determine user emotions and use knowledgeable insights to improve everything from customer experience to marketing campaigns. Various industries make use of this AI technology. Such as:
The aim of devising Emotion AI-driven solutions in the advertising industry is to create more personalized and rich experiences for customers. Often, the emotional cues of customers help in developing targeted ads and increasing engagement and sales.
For instance, Affectiva, a Boston-based Emotion AI company, captures users’ data such as reactions to a particular advertisement. Later, AI models are employed to determine what caused the strongest emotional response from viewers. Finally, these insights are incorporated into ads to optimize campaigns and increase sales.
2. Call Centers
Inbound and outbound call centers are always dealing with customers over calls for different services and campaigns. By analyzing the emotions of agents and customers during calls, call centers evaluate agents’ performances and customers’ satisfaction. Moreover, agents make use of Emotion AI to understand the mood of customers and communicate effectively.
A leading health insurance provider, Humana has been using Emotion AI in its call centers for quite some time now to deal with its customers efficiently. With the aid of an Emotion AI-empowered digital coach, agents in the call center are prompted in real-time to adjust their pitch and conversation according to the customers.
3. Mental Health
According to a report by the National Institute of Mental Health, more than one in five U.S. adults live with a mental illness. This means that millions of people aren’t either self-aware of their emotions or not capable of handling them. Emotion AI can help people by increasing their self-awareness and helping them learn coping strategies to reduce stress.
In this space, Cogito’s platform CompanionMx has been helping people to detect mood changes. The application tracks the voice of the user via his phone and performs analysis to detect signs of anxiety and mood changes. Similarly, there are specialized wearable devices available as well to recognize the stress, pain, or frustration of users through their heartbeats, blood pressure, etc.
There are roughly 1.446 billion vehicles registered in the world. The automotive industry in the United States alone made $1.53 trillion in revenue in 2021. Despite being one of the largest industries in the world, the automotive industry carves for improvement in road safety and reduction in accidents to prevail. According to a survey, there are 11.7 deaths per 100,000 people in motor vehicle crashes in the United States. Therefore, for the industry’s sustainable growth, Emotion AI can be employed to reduce preventable accidents.
Several applications are available to monitor the driver’s state using sensors. They can detect signs of stress, frustration, or fatigue. Particularly, Harman Automotive has developed an Emotion AI-powered adaptive vehicle control system to analyze a driver’s emotional state through facial recognition technology. Under certain circumstances, the system adjusts the car’s settings to comfort the driver such as providing calming music or ambient lighting to prevent distractions and accidents.
Why Does Emotion AI Matter?
Psychologist Daniel Goleman explained in his book “Emotional Intelligence: Why It Can Matter More Than IQ” that Emotional Intelligence (EQ) matters more than Intelligence Quotient (IQ). According to him, EQ can have a greater influence on a person’s success in life than his IQ. This shows that control over emotions is necessary to take sound and informed decisions. As humans are prone to emotional bias that can affect their rational thinking, Emotion AI can assist daily life chores by exercising mindful judgment and making the right call.
Moreover, given the current realm of the technological world, the use of technology by people is increasing globally. As people become more interconnected and technology continues to advance, the reliance on technology to deal with all sorts of matters increases. Therefore, for making interactions with people more personalized and empathetic, artificial empathy is vital.
Emotion AI incorporates artificial empathy into machines to build smart products that can understand and respond to human emotions effectively. For instance, in healthcare, using artificial empathy, an application is developed by a research team at RMIT University. This application is programmed to analyze the voice of a person and detect if he’s suffering from Parkinson’s disease. In gaming industries, developers are using artificial empathy to create lifelike characters that respond to the player’s emotions and enhance the overall gaming experience.
Although the advantages of Emotion AI are unmatched, there are several challenges in implementing and scaling emotion-based applications.
Ethical Considerations & Challenges of Emotion AI
Emotion AI is in a nascent phase at the moment. Numerous AI labs are starting to develop software that can recognize human speech and emotion to harvest practical benefits. As its development and growth increase, several risks have been discovered. According to Accenture, the data needed for training such AI models is more sensitive than other information. The primary risks with the data are as follows:
An Emotion AI model requires highly profound data related to personal feelings and private behaviors for training. This means that the person’s intimate state is well known to the model. It’s possible that just based on micro-expressions, an Emotion AI model might predict emotions several seconds before a person himself can detect them. Hence, this presents a serious privacy concern.
The data needed for Emotion AI is not simple as compared to other applications of AI. Data representing the state of mind is different and complex. Hence, the emergence of Emotion AI-powered applications becomes more difficult. As a result, they require high investments in research and resources to ripe fruitful outcomes.
As complex data is needed for Emotion AI, there’s a likelihood of misinterpretations and error-prone classifications by models. Interpreting emotions is something humans themselves struggle with so delegating this to AI might be risky. Therefore, model results might be far away from actual reality.
Today, modern data engineering pipelines and decentralized architectures have streamlined the model training process remarkably. However, in the case of Emotion AI, errors can rapidly proliferate and become difficult to correct. These potential pitfalls can spread throughout the system quickly and enforce inaccuracies, thereby impacting people adversely.
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