Emotion AI (also known as an effective computer) is a broad range of technologies that use artificial intelligence to sense and learn human emotions. Emotion AI uses text, audio, and video data to analyze signals from humans. You can, for example:
- Textual data is processed using sentimental analysis and natural language processing.
- Voice AI is used For processing audio
- Facial motion analysis and gait analysis of videos.
Emotion AI has been in high demand recently due to the numerous practical applications it can provide that reduce the gap between humans & machines. According to a MarketsandMarkets Research report, the size of the emotion detection market is expected to exceed $42 billion in 2027, up from $23.5 billion in 2020.
Explore how this fascinating sub-category of AI works.
How Does Emotion AI Work?
As with any AI technique. Data is needed to improve the performance of Emotion AI and to understand users’ emotions. Data varies depending on the use case. To understand emotions, we use, for example, social media activity, video recordings of speech and actions, physiological sensors on devices, etc.
Then, the feature engineering process is carried out whereby relevant features that impact emotions are identified. When determining facial emotions, eye movement, mouth shape, and eye gaze are used to determine whether a person feels happy, sad, or angry. In speech-based emotion recognition, pitch, volume, and tempo can be used to determine if someone is happy, sad, or angry.
Then, the features are preprocessed to create a machine-learning algorithm that accurately predicts emotional states. The model is then deployed to real-world applications in order to enhance user experience, boost sales, and recommend appropriate content.
Four Important Applications of Emotional AI
Emotion AI models are used by companies to identify user emotions. They then use the knowledge gained to improve customer service and marketing campaigns. This AI technology is used by a variety of industries. As an example:
Emotion AI solutions for the advertising industry to provide richer and more personalized experiences for the customer. The emotional cues from customers are often used to develop targeted ads, increase engagement and boost sales.
Affectiva is a Boston-based Emotion AI firm that captures user data, such as their reactions to an advertisement. AI models are then used to determine which emotional responses viewers had. These insights are then incorporated into the ads in order to optimize campaigns and boost sales.
2. Call Centers
Inbound and outbound call centers are constantly dealing with different campaigns and services. Call centers to measure the performance of agents and customer satisfaction by analyzing emotions during calls. Agents use Emotion AI in order to communicate with customers effectively and understand their moods.
A leading health insurance provider, Humana has been using Emotion AI for some time in its call center to efficiently deal with customers. Agents in the call centers are guided by an Emotion AI digital coach to adapt their pitch and conversations in real-time.
3. Mental Health
A report from the National Institute of Mental Health states that more than one-fifth of U.S. adult citizens suffer from a mental disorder. Millions of people either aren’t aware of their own emotions or can’t handle them. Emotion AI helps people become more self-aware and learn stress reduction strategies.
This space is, The Cogito CompanionMx platform helps people detect mood changes. This application analyzes the voice of a user’s phone to detect anxiety and mood fluctuations. There are also wearable devices that can detect stress, pain, or frustration in users by monitoring their blood pressure and heartbeat.
Around 1.446 billion cars are registered worldwide. In 2021, the automotive industry alone in the United States generated $1.53 trillion. The automotive industry is one of the biggest industries in the globe, but it strives to improve road safety and reduce accidents. A survey found that motor vehicle accidents in the United States result in 11.7 deaths for every 100,000 people. Emotion AI is a tool that can reduce accidents and ensure the sustainable growth of the industry.
Sensors can be used to monitor the state of the driver. They can detect stress, frustration, or fatigue. Harman Automotive, in particular, has developed a facial recognition-powered Emotion AI adaptive vehicle control system that analyzes a driver’s emotional state. The system can adjust the settings of the car to provide comfort to the driver, such as calming music and ambient lighting.
Why Does Emotion AI Matter?
In his book, “Emotional Intelligence: Why it can matter more than IQ”, psychologist Daniel Goleman explains that Emotional Quotient (IQ) is less important than Emotional Quotient. He said that EQ has a greater impact on a person’s life success than IQ. It is clear that the ability to control emotions is essential for making informed and sound decisions. Emotion AI is able to assist in daily life tasks by making the right decisions. As humans can be prone to emotional bias, which can influence their rational thinking.
Globally, people are increasingly using technology, especially in the current technological age. The use of technology for all kinds of issues increases as people become increasingly interconnected and technology advances. Artificial empathy is therefore essential to make interactions with people more personal and empathetic.
Emotion AI integrates artificial empathy in machines to create smart products that understand and respond effectively to human emotions. A research team from RMIT University developed an application that uses artificial empathy to improve healthcare. This application analyzes the voice of an individual to detect Parkinson’s. Developers in the gaming industry are using artificial empathy for creating lifelike characters who respond to player emotions and enhance their overall gaming experience.
Even though Emotion AI’s benefits cannot be matched by other technologies, there are challenges in implementing and scaling emotion-based applications.
Ethical Considerations & Challenges of Emotion AI
At the moment, Emotion AI is still in its infancy. Many AI labs have begun developing software that recognizes human speech and emotions to reap practical benefits. As the technology’s development and growth increase, several risks are being discovered. Accenture states that the data used to train AI models are more sensitive than any other information. These are the primary risks associated with data:
For training, an Emotion AI model needs highly detailed data about private feelings and behaviors. The model knows the intimate state of the person. A model that predicts emotions based solely on microexpressions could do so several seconds before the person can. This poses a serious concern for privacy.
Emotion AI requires a lot more data than other AI applications. The data that represents the state of mind differs and is complex. The emergence of Emotion AI-powered applications is therefore more challenging. They require high investment in resources and research to yield fruitful results.
Emotion AI requires complex data, which can lead to misinterpretations or classifications that are inaccurate. It is difficult for humans to interpret emotions, so delegating it to AI could be risky. Model results may be far from reality.
Modern data engineering pipelines, decentralized architectures, and modern data engineering tools have made the process of model training remarkably faster. In the case of EmotionAI, errors can quickly proliferate and are difficult to fix. These potential pitfalls could spread quickly throughout the system and cause inaccuracies. This would negatively impact people.