What is Emotion AI and why is it important?

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Emotion AI, also known as affective computing, is a broad range of technologies used to learn and sense human emotions with the help of artificial intelligence (AI). Emotion AI leverages text, video and audio data to analyze multiple sources and interpret human signals. for example:

Recently, the demand for Emotion AI has increased due to many practical applications that can reduce the gap between humans and machines. In fact, according to a report by MarketsandMarkets Research, the market for emotion detection is expected to grow from $23.5 billion in 2022 to over $42 billion in 2027.

Let’s find out how this amazing subcategory of AI works.

How does Emotion AI work?

Like other AI techniques, Emotion AI needs data to improve its performance and understand users’ emotions. The data will be different for each use case. For example, your social media activity, your speech and actions in video recordings, and your device’s physiological sensors are used to understand your audience’s emotions.

A process of feature engineering is then performed to identify relevant features that influence emotions. Facial emotion recognition can use eyebrow movements, mouth shape, and gaze to determine if a person is happy, sad, or angry. Similarly, the pitch, volume, and tempo of voice-based emotion detection can infer whether a person is excited, frustrated, or bored.

These features are then preprocessed and used to train machine learning algorithms that can accurately predict a user’s emotional state. Finally, deploy the models in real applications to improve user experience, increase sales, and recommend relevant content.

Four Important Applications of Emotional AI

Businesses can leverage Emotion AI models to determine user emotions and use knowledgeable insights to improve everything from customer experiences to marketing campaigns. Various industries are making use of this AI technology. like that:

1. Advertising

The purpose of devising Emotion AI-powered solutions in the advertising industry is to create a richer, more personalized experience for customers. In many cases, customer emotional cues can help you create targeted ads to increase engagement and sales.

For example, Boston-based Emotion AI company Affectiva collects user data, such as reactions to certain ads. We then use AI models to identify what caused the strongest emotional response from your audience. Finally, incorporate these insights into your ads to optimize your campaigns and increase sales.

2. Call center

Inbound and outbound call centers are constantly serving customers with calls for a variety of services and campaigns. Call centers evaluate agent performance and customer satisfaction by analyzing agent and customer sentiment during calls. Additionally, agents utilize her Emotion AI to understand customer moods and communicate effectively.

Humana, a leading health insurance provider, has been using Emotion AI in its call centers to efficiently serve customers for some time now. With the help of Emotion AI-powered digital her coaches, call center agents are prompted in real time to tailor their pitches and conversations to customers.

3. Mental health

The National Institute of Mental Health reports that more than one in five adults in the United States has a mental illness. This means that millions of people are either unaware of their emotions or lack the ability to process them. You can help people by enabling them to learn

In this area, Cogito’s platform CompanionMx helps detect changes in people’s moods. The application tracks users’ voices through their phones and performs analytics to detect signs of anxiety and mood changes. Similarly, dedicated wearable devices are available that recognize a user’s stress, pain, or frustration through things like heart rate and blood pressure.

4. Automobiles

There are approximately 1,446 million vehicles registered worldwide. The U.S. auto industry alone will generate $1.53 trillion in revenue in 2021. Despite being one of the world’s largest industries, the automotive industry is committed to improving road safety and reducing accidents. According to one study, car accidents in the United States kill him at 11.7 per 100,000 people. Therefore, Emotion AI can be used to reduce preventable accidents for the sustainable growth of the industry.

Several applications are available for monitoring the state of drivers using sensors. It can detect signs of stress, frustration, or fatigue. In particular, Harman Automotive developed an adaptive vehicle control system powered by Emotion AI to analyze the driver’s emotional state through facial recognition technology. Under certain circumstances, the system adjusts the car’s settings to comfort the driver, providing calming music and ambient lighting to prevent distraction and accidents.

Why Emotion AI Matters

In his book Emotional Intelligence: Why It Can Matter More Than IQ, psychologist Daniel Goleman explains that Emotional Intelligence (EQ) is more important than Intelligence Quotient (IQ). According to him, EQ can have a greater impact on a person’s success in life than IQ. This shows that controlling emotions is necessary to make healthy, well-informed decisions. Since humans are prone to emotional biases that can affect rational thinking, Emotion AI can help with the chores of everyday life by making mindful decisions and making the right decisions.

Moreover, given the current realm of the technology world, people’s use of technology is increasing globally. As people become more interconnected and technology continues to advance, our reliance on technology to address all sorts of problems grows. Therefore, artificial empathy is essential to make our interactions with people more personalized and empathetic.

Emotion AI builds artificial empathy into machines to build smart products that can effectively understand and respond to human emotions. For example, in healthcare, an application is being developed by a research team at RMIT University using artificial empathy. This application is programmed to analyze a person’s voice and detect if they have Parkinson’s disease. In the gaming industry, developers are using artificial empathy to create realistic characters that respond to player emotions and enhance the overall gaming experience.

Although the benefits of Emotion AI are unmatched, there are some challenges in implementing and scaling emotion-based applications.

Emotion AI Ethical Considerations and Challenges

An illustration of a human Sim pushing a large block in front of them to reach the goal.

Emotion AI is currently in its early stages. Many AI labs have started developing software that can recognize human speech and emotions for practical benefits. As its development and growth progressed, some risks were discovered. According to Accenture, the data needed to train such AI models is more sensitive than other information. The main risks regarding data are:

  • intimacy

    Training an Emotion AI model requires very deep data about an individual’s emotions and private behavior. This means that the intimate state of the person is well known to the model. Based on microexpressions alone, the Emotion AI model can predict emotions seconds before a person detects them themselves. As such, this presents a serious privacy concern.

  • intangible

    The data required for Emotion AI is not simple compared to other AI applications. The data representing states of mind are varied and complex. Therefore, the emergence of applications powered by Emotion AI will become more difficult. As a result, they require significant investment in research and resources to yield fruitful results.

  • Ambiguity

    Emotion AI requires complex data, which can lead to misinterpretations and error-prone classifications by the model. Humans themselves struggle with interpreting emotions, so delegating this to AI can be dangerous. Therefore, the model results may deviate from the actual reality.

  • escalation

    Today, modern data engineering pipelines and distributed architectures greatly streamline the model training process. However, in the case of Emotion AI, errors can grow rapidly and become difficult to fix. These potential pitfalls can quickly spread through the system and force people into inaccuracies, which can have a negative impact on people.

To learn more about exciting advancements in technology and how they are transforming industries, check out Unite.ai.

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