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Emotions In AI

DataQuest

|

July 2019

AI is learning and recognising human emotions and using that knowledge to improve everything — from marketing campaigns to healthcare. Here, Ranjan Kumar, founder and CEO, Entropik Tech, explains how machines are replicating the way humans think.

- Aanchal Ghatak

Emotions In AI

Elaborate on Emotion AI. for what purpose can this be used?

AI is a complex web for logical decision making, an intelligence based on ‘if-else’ rules crafted to decipher an outcome based on vast possibilities of logical permutations of choices. However, Emotion AI also understands the emotional context of logic. It’s an AI system, which is not just artificially intelligent, but also emotionally perceptive.

Avenues for applications of Emotion AI are plentiful, including recruitment, medical diagnosis and assistance, loan evaluation, customer service, automobile passenger safety, optimization of ROI for marketers and many more. Entropik Tech is dedicated to building Emotion AI.

What technologies are you using to illustrate the emotion and expressiveness? Do you also map facial expressions?

For the Emotion AI platform, Affect Lab uses proprietary technologies like, brainwave mapping, facial coding and eye tracking to decipher cognitive and emotional response of consumers as they watch an ad, experience a product or undergo a purchase in a retail store.

For brain wave mapping, we use a special hardware which works like wearing a headphone. The hardware monitors electrical activity of the brain, while the user is experiencing a product/watching content. The raw data collected by the EEG (electroencephalogram) headset is then interpreted by algorithms to calculate behavioral and cognitive metrics like attention level of the user, mental effort applied towards completing a task etc.

The facial coding system developed by the team at Entropik can track facial expressions of users by identifying facial landmarks. This data is generated from the way you grin; the way you roll your eyes to way you smirk is fed into our deep learning algorithm and to get emotion metrics.

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