Origin of Artificial Intelligence (AI) and Emotional Intelligence (EI)
EI was popularized through the work of Salovey-Mayer and Daniel Goleman, 3 decades ago. AI applications in business gained popularity around the same period when online media platforms like Facebook started using AI for emotion detection. In that sense, one could imagine EI and AI as running on parallel tracks; until they were inter-locked, to design scalable business applications.
‘AI & EI’ or ‘AI vs EI’?
Just as words, “emotional” and “artificial” sound in contrast. How then, can something as uniquely human as Emotion, work with something as manufactured/ doctored as Artificial? The answer lies in the fact that certain attributes are unique to each, EI and AI. AI necessarily involves machines to identify, measure, replicate and repeat data patterns; whereas EI rests on the human ability to identify, understand and manage emotions. AI is guided by human intervention, whereas EI is human-centric.
Artificial Intelligence (AI) uses automation and technology to capture, organize and analyze data. Ultimately, AI aims to utilize the analyzed data to predict/ identify errors in the future. On the other hand, EI has a larger human component, since its origin lies in human behaviour i.e. voice, text and gestures.
These distinctions place AI and EI in a unique space to co-exist. Although these are early days for the AI-EI co-existence space, there are several instances of AI and EI feeding off each other, to provide real, scalable and sustainable business solutions.
Early Days for Commercial Application of the AI-EI Combination
For AI to be applied to EI, the starting point is data capture. In EI, data = emotions. Verbal (text, voice) and non-verbal (facial expressions, body gestures) signals are captured through computer vision, sensors and cameras. This raw data is processed and analyzed, using machine learning algorithms; to determine patterns in emotion. Once identified, the patterns are interpreted, to design appropriate responses. Consider some examples of such applications in various areas of human existence:
Emotion AI technology has been deployed to adjust gamers’ gaming experience basis their feelings while playing the game. For instance, the game atmosphere for a stressed vs an excited gamer can be personalized; for each of them to have a uniquely personal, high-engagement game-playing experience.
This industry has benefitted from various AI-EI applications, from diagnosis to treatment to care. The pandemic has created even more use-cases for tech companies to explore, improvise and validate. For instance, patients’ voice data has been used to improve the diagnosis of Dementia and Depression. Public sentiment about the pandemic has been tracked using social media posts (text data); to devise virus-containment strategies. Nurse bots have been developed to provide dose-time reminders, make supportive conversations and monitor overall well-being.
EduTech industry has seen a range of applications to customize Education to kids’ emotions and moods. Online EduTech platforms are actively using eye-tracking and facial coding data captured from students, during teaching sessions. Such data helps build algorithms to map student journeys and to identify moments when engagement, attention and fatigue set in (during a teaching session). This understanding helps the platform identify how teaching content and methods can be improved to increase students’ attention span.
Among the earliest use-cases, the Automotive industry uses computer vision technology to capture drivers’ emotional states (while on a drive). Basis millions of such data points, extreme emotional states or drowsiness can be identified by the car’s AI system. This understanding has been used to trigger driver alerts to prevent accidents proactively. Other Safety applications include solutions to monitor first-level responders in Disaster Management to ensure their well-being while on-the-job.
Enabling humans to make more emotionally intelligent decisions at work offers vast scope for developing solutions. One such solution is in the area of Fraud Detection, which is used by financial institutions like banks and insurance companies. Customer voice is captured and analysed to identify patterns that indicate how truthful customers are when submitting loan applications/ insurance claims. This helps businesses lower defaulter rates; thus protecting their overall business health. Fraud Detection methods also work well in Recruitment, to ascertain the credibility of candidates during the interview process.
Customer Journeys are another area where AI helps businesses improve their customer-engagement processes. For instance, intelligent call-center routing enables businesses to identify particularly irate customers and route their incoming calls to more experienced agents. Further, agents can monitor real-time, customer moods. This helps them adjust the conversation as it is happening; leading to favourable outcomes for the business.
The AI-EI Future is Indeed Bright
As most technology advancements do, with time, AI will make us progressively more emotionally intelligent. It will eventually make human effort smarter, speedier, more empathetic and efficient. It took years for Data in mobile connections to spread, whereas the shift from feature phones to smartphones was much speedier. Similarly, as newer use-cases for the application of AI to EI data emerge, humans will learn to better leverage emotions in various situations.
Skills like negotiation, socializing and empathy will become critical differentiators when evaluating human contribution to organizations. After all, machines take time to replicate the unique attribute that constitutes humans – the capability to feel emotions, be it fear, love, compassion, empathy, disgust, happiness. The next step is to continuously nurture and invest in these ‘softer’ capabilities, even while we rely on automation and AI to manage the more ‘technical’ parts of our day-to-day work