The Infosys Labs research blog tracks trends in technology with a focus on applied research in Information and Communication Technology (ICT)

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Rise of Emotional Intelligence in AI

We typically prefer to be with people who can understand us and are emotionally intelligent. Body language and tone play a significant part in what we think and feel. Emotional intelligence encompasses the ability of people to recognize, understand and control their own emotions as well as recognize, understand and influence others' emotions. EQ has become an important consideration when we talk about AI development. As per Rana el Kaliouby, co founder and CEO of Affectiva, an MIT spinout company that works on emotional recognition technology, "If it's interfacing with a human, it needs social and emotional skills." The addition of EQ to AI will help such systems respond better to more complex human needs leading to creation of better customer experiences and thereby improve customer satisfaction.

Businesses are increasingly benefitting from advances in emotionally intelligent AI as they uncover new opportunities by understanding consumer likes and dislikes along with gauging their affinity towards a brand or product. As per a recent study by Market Research Future (MRFR), the global emotion analytics market is expected to reach USD 25 billion by 2023, growing at a CAGR of 17% between 2017 and 2023. Also, Gartner predicts that by 2022, 10% of our personal devices will include emotional AI capabilities, up from less than 1% in 2018. Using sentiment analysis to understand consumer perception towards a product/brand in the offline world has remained a daunting task. Detecting emotions from facial expressions using AI can be used as a substitute to better understand consumer preferences and how they engage with particular brands.

Traditionally market research companies have relied on using different methods such a surveys, trade interviews to better understand consumer requirements. However, these methods assume a direct correlation between future actions and what the consumers state verbally, which may not always be accurate. In this scenario, behavioral methods are considered more objective and are often deployed to observe a user's reaction while interacting with a product/brand. Manually analyzing video feeds of users interacting with a product/brand can be pretty labor intensive. Facial emotion recognition can be useful in this scenario as they allow market research companies to record facial expressions automatically and derive meaningful insights from them.

Disney has designed an AI-powered algorithm to gain a better understanding of how audiences enjoy its movies, this algorithm can recognize complex facial expressions and also predict how audiences will react for the remaining part of the movie. As per reports, the tests processed a staggering figure of 16 million data points derived from 3,179 viewers.

Earlier this year, Soul Machines partnered with Daimler Financial Services to present "Sarah", a digital human as an interface to Daimler's financial services and mobility ecosystem aiding them to deliver enhanced customer experiences in the areas of car financing, leasing and insurance by utilizing facial gestures and natural voice intonation.

Annette Zimmermann, vice president of research at Gartner claimed in January 2018, "By 2022, your personal device will know more about your emotional state than your own family." Facial analysis, voice pattern analysis and deep learning when used together in conjunction can help decipher human emotions with applications across a broad range of industries such as retail, financial services, medical diagnosis, autonomous cars, fraud detection and recruitment among others.

The shift from data-driven interactions relying heavily on IQ to EQ-guided experiences will also present companies an opportunity to connect with customers on a much more intimate level. However, emotions are immensely personal and companies working in this space should be wary about consumer concerns such as intrusion of personal space and manipulation. Suitable psychological training for people is also required to interpret emotional results from these machines and fix deviations as deemed appropriate.

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