Remember the last commercial ad for a product you fancy. What was your reaction? What did you think about the ad? Its presentation? What emotions did the ad spike in you? Excitement? Happiness? Appreciation? Curiosity? Interest? Would you consider buying the product or recommending it to others? Emotions are equally critical to businesses today as they are in humans and animals. Artificial Intelligence has taken a bold new dimension in recent times to recognizing emotions and this has significantly influenced business decisions and operations. Are you considering an AI and ML course? It’s time to take note of the intriguing developments in the AI industry. Technological developments have proved to us that there’s so much that machines can do including training models to recognize human emotions and delivering insights to improve customer experience across industries including advertising, marketing, call centers, automotive, and healthcare. As the world shifts towards adopting artificial intelligence for numerous applications, emotional intelligence is taking shape and becoming increasingly useful in an AI emerging field, emotional AI. (www.caprinow.edu)
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What is emotional AI?
Emotional AI is also known as Artificial emotional intelligence or affective computing. Emotional AI is a branch of AI in which machines are trained to analyze, understand, and react to human emotions based on different sources of data including computer vision, text (natural language processing and sentimental analysis), audio (speech, voice tonality, intonation, and rhythm), video (gestures, gait, facial expressions, physiological signals), biometric sensors, and others. The goal of emotional AI is to determine and react accordingly to someone’s emotional state similar to what happens during human-to-human interactions.
The history of emotional AI dates back to 1995 a publication on affective computing by Rosalind Picard. This was after microphones, physiological sensors, and cameras were used to gather affective responses on human emotions and then machines programmed to respond to those in an MIT lab. Since then, emotional AI has taken a big leap. While drawing much debate about its effectiveness and encroachment into human emotions, emotional AI has also drawn so much attention and is projected that it will be worth at least $174 billion by 2025. The future for emotional AI is promising as businesses rely more on the emotions, motivations, and attitudes data of their clients to stay ahead in the competition.
How does artificial emotional intelligence work?
AI algorithms are designed to explore and learn from input data to discover hidden patterns and insights from datasets. AI algorithms adapt progressively as data is fed into the system to become more accurate with time. In the same way, artificial emotional intelligence works by exploring various datasets to learn and generate insights into human behavior and emotions.
Artificial emotional intelligence makes use of various techniques including computer vision, natural language processing, deep learning, speech science, and others to gather, process, and analyze data to determine and interpret emotions in humans during certain times. With time and data input, emotional AI algorithms become more accurate in identifying and interpreting human emotions and produce valuable insights.
While a debate surrounds the accuracy and reliability of information from emotional AI algorithms, developers today have gone a step ahead to validate this by conducting live tests on humans of their emotions to see how they compare with results delivered by algorithms. In order for algorithms to be accurate, many factors come into play including location, lighting conditions, and culture.
Emotional AI comprises the following techniques:
- NLP and sentiment analysis
- Voice and audio analysis
- Gesture, behavioral, and internal physiology
- Video and multimodal data analysis
- Wearable (IoT) computing
- Facial and expressions coding
- Augmented reality
- Virtual reality
Why it is important in the 21st century
It has grown to become a popular tool employed for numerous applications across such industries as marketing, advertising, review analysis, security, healthcare, finance, automotive, customer service, recruitment, and retail. Emotions reflect human behavior. To begin with, it has the potential to transform the way businesses interact with clients where value is placed on empathy and comfort in meeting customer needs in an effort to achieve a better customer experience.
In addition, it helps businesses to recommend the right products and services to customers based on their specific needs. These systems enable brands to engage meaningfully with their customers ultimately building strong brand loyalty.
Further, businesses are using facial analysis and other techniques to gain insight into their customer behavior through the emotions they elicit for example in shopping stores. Knowing whether the customer is happy, frustrated, disappointed, or frowning helps store owners make more appropriate recommendations in time to address these emotions and eventually retain the customer.
Emotional AI models present many benefits over humans because they operate accurately throughout and so can be depended upon to deliver insights to consistently improve customer experience throughout the customer journey. Customers have come to value emotional over relational engagements and this is what futuristic brands are leveraging through emotional AI systems that:
- Learn their emotions and behavior during their interaction with a brand
- Understand human communication from emotional, relational, and cognitive perspectives
- Tell the difference between literal and non-literal statements
- Identify different types/categories of customer behaviors and motives to strengthen customer relationships through offering personalized recommendations.
The Future of emotional AI
Certainly, there is a human aspect that machines can never replace. Humans are still ahead of machines in understanding and interpreting emotions. However, with the growing interest in emotional AI coupled with the need to optimize and leverage strong customer relationships, there is no doubt that this field is advancing fast.
Emotional AI gives businesses the opportunity to offer their customers personalized products and services to enhance user experience. However, like other technologies, it is now without shortcomings. Being that emotional AI is still in its nascent stage, questions arise about regulation, bias, and its ability to replicate human emotions. For instance, given the difference in race and culture between different individuals, researchers at MIT found out that emotional AI systems had a bit of bias where datasets with nonwhite and nonmale faces skew. As a result, using them to identify emotions in such faces involved a higher level of complexity. Other factors like culture, posed/potential photos, social, race, gender, and more contributed significantly to the ambiguity of emotions.
To increase their accuracy, emotional AI models are currently being applied to simple environments, narrower tasks, or datasets with diverse types of data. The future of emotional AI is in the regulation of data privacy as well as the prevention of unproven and intrusive use of emotional AI applications and devices. This will see to wider adoption and development of domain-specific applications. The future is in emotional AI applications that can both mimic and express emotions.