Emotion AI, also often known as affective computing, is a wide selection of technologies used to learn and sense human emotions with the assistance of artificial intelligence (AI). Capitalizing on text, video, and audio data, Emotion AI analyzes several sources to interpret human signals. For example:

Recently, Emotion AI is experiencing a greater demand attributable to its quite a few practical applications that may reduce the gap between humans and machines. In truth, a report by MarketsandMarkets Research suggests that the emotion detection market size is predicted to surpass $42 billion by 2027, in comparison with $23.5 billion in 2022.

Let’s explore how this amazing sub-category of AI works.

How Does Emotion AI Work?

Like all other AI technique, Emotion AI needs data to enhance performance and understand users’ emotions. The information varies from one use case to a different. For example, activity on social media, speech and actions in video recordings, physiological sensors in devices, etc., are used to grasp the emotions of the audience.

Afterward, the strategy of feature engineering takes place where relevant features impacting emotions are identified. For facial emotion recognition, eyebrow movement, mouth shape, and eye gaze could be used to find out if an individual is glad, sad, or indignant. Similarly, pitch, volume, and tempo in speech-based emotion detection can deduce if an individual is worked up, frustrated, or bored.

Later, these features are pre-processed and used to coach a machine learning algorithm that may accurately predict the emotional states of users. Finally, the model is deployed in real-world applications to enhance user experience, increase sales, and recommend appropriate content.

4 Necessary Applications of Emotion AI

Corporations leverage Emotion AI models to find out user emotions and use knowledgeable insights to enhance every little thing from customer experience to marketing campaigns. Various industries make use of this AI technology. Similar to:

1. Promoting

The aim of devising Emotion AI-driven solutions within the promoting industry is to create more personalized and wealthy experiences for purchasers. Often, the emotional cues of consumers assist in developing targeted ads and increasing engagement and sales.

For example, Affectiva, a Boston-based Emotion AI company, captures users’ data comparable to reactions to a specific commercial. Later, AI models are employed to find out 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 at all times coping with customers over calls for various services and campaigns. By analyzing the emotions of agents and customers during calls, call centers evaluate agents’ performances and customers’ satisfaction. Furthermore, agents make use of Emotion AI to grasp the mood of consumers and communicate effectively.

A number one medical health insurance provider, Humana has been using Emotion AI in its call centers for quite a while now to cope with its customers efficiently. With assistance from an Emotion AI-empowered digital coach, agents in the decision center are prompted in real-time to regulate their pitch and conversation based on the purchasers.

3. Mental Health

In keeping with a report by the National Institute of Mental Health, a couple of in five U.S. adults live with a mental illness. Because of this hundreds of thousands of individuals aren’t either self-aware of their emotions or not able to handling them. Emotion AI may help people by increasing their self-awareness and helping them learn coping strategies to cut back stress.

On this space, Cogito’s platform CompanionMx has been helping people to detect mood changes. The applying tracks the voice of the user via his phone and performs evaluation to detect signs of tension and mood changes. Similarly, there are specialized wearable devices available as well to acknowledge the stress, pain, or frustration of users through their heartbeats, blood pressure, etc.

4. Automotive

There are roughly 1.446 billion vehicles registered on the planet. The automotive industry in the US alone made $1.53 trillion in revenue in 2021. Despite being one in all the biggest industries on the planet, the automotive industry carves for improvement in road safety and reduction in accidents to prevail. In keeping with a survey, there are 11.7 deaths per 100,000 people in motorized vehicle crashes in the US. Subsequently, for the industry’s sustainable growth, Emotion AI could be employed to cut back preventable accidents.

Several applications can be found to observe the driving force’s state using sensors. They will detect signs of stress, frustration, or fatigue. Particularly, Harman Automotive has developed an Emotion AI-powered adaptive vehicle control system to investigate a driver’s emotional state through facial recognition technology. Under certain circumstances, the system adjusts the automobile’s settings to comfort the driving force comparable to providing calming music or ambient lighting to stop 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 greater than Intelligence Quotient (IQ). In keeping with him, EQ can have a greater influence on an individual’s success in life than his IQ. This shows that control over emotions is essential to take sound and informed decisions. As humans are liable to emotional bias that may affect their rational considering, Emotion AI can assist day by day life chores by exercising mindful judgment and making the best call.

Furthermore, given the present realm of the technological world, using technology by people is increasing globally. As people change into more interconnected and technology continues to advance, the reliance on technology to cope with all varieties of matters increases. Subsequently, for making interactions with people more personalized and empathetic, artificial empathy is important.

Emotion AI incorporates artificial empathy into machines to construct smart products that may understand and reply to human emotions effectively. For example, in healthcare, using artificial empathy, an application is developed by a research team at RMIT University. This application is programmed to investigate the voice of an individual and detect if he’s affected by Parkinson’s disease. In gaming industries, developers are using artificial empathy to create lifelike characters that reply to the player’s emotions and enhance the general gaming experience.

Although the benefits 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 in the meanwhile. Quite a few AI labs are beginning to develop software that may recognize human speech and emotion to reap practical advantages. As its development and growth increase, several risks have been discovered. In keeping with Accenture, the information needed for training such AI models is more sensitive than other information. The first risks with the information are as follows:

• Intimacy

  An Emotion AI model requires highly profound data related to non-public feelings and personal behaviors for training. Because of this 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 an individual himself can detect them. Hence, this presents a serious privacy concern.

• Intangibility

  The information needed for Emotion AI just isn’t easy as in comparison with other applications of AI. Data representing the frame of mind is different and complicated. Hence, the emergence of Emotion AI-powered applications becomes harder. Because of this, they require high investments in research and resources to ripe fruitful outcomes.

• Ambiguity

  As complex data is required 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 may be dangerous. Subsequently, model results may be far-off from actual reality.

• Escalation

  Today, modern data engineering pipelines and decentralized architectures have streamlined the model training process remarkably. Nonetheless, within the case of Emotion AI, errors can rapidly proliferate and change into difficult to correct. These potential pitfalls can spread throughout the system quickly and implement inaccuracies, thereby impacting people adversely.

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