Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

The science and applications of the emerging field of artificial emotional intelligence

Rana el Kaliouby (Affectiva)
2:35pm3:15pm Wednesday, June 28, 2017
Verticals and applications
Location: Grand Ballroom West Level: Non-technical
Secondary topics:  Machine Learning, User interface and experience, Vision
Average rating: *****
(5.00, 2 ratings)

What you'll learn

  • Understand emotion AI and why this emerging space matters
  • Learn the main machine learning and deep learning approaches being used to build emotion technology (from face and speech)
  • Explore killer use cases, where the market traction is at the moment, and the future of emotion AI


Human emotional intelligence (EQ) is the ability to recognize emotions (both your own and those of other people) and use the emotion information to guide your behavior and achieve your goals. It is widely accepted that people with higher EQs lead more successful professional and personal lives—they are more likable and more persuasive, tend to be more effective leaders, and generally lead healthier, happier, and even longer lives.

In the new digital era, where hyperconnected smart devices and advanced AI systems dominate our communication and daily lives, infusing artificial emotional intelligence becomes critical. Emotion AI is a branch of artificial intelligence that aims to bring emotional intelligence to AI systems. Our interactions with technology are becoming more conversational and more relational, and the devices we use are more perceptual and are increasingly expected to interact with their users the way people interact with one another. Advanced AI systems are expected to work hand in hand with humans: improving productivity in a work environment, assisting doctors and nurses in delivering care to chronically ill patients or the elderly, or engaging students and personalizing their learning experience. These systems need to have emotional intelligence; they must be able to read the emotions of their users and adapt their operations accordingly.

Emotion AI is projected to grow to a multibillion dollar industry in the next five years, transforming industries such as market research, automotive, and healthcare. Rana el Kaliouby reviews the state of the art in emotion AI, leading a deep dive into some of the challenges of building truly emotionally and socially intelligent machines. Rana discusses the commercial and practical applications of emotion AI, where it’s being applied today, and how businesses use emotion analytics to make decisions. She also explores the future roadmap, which includes multimodal emotion recognition and the idea of an emotion chip.

Photo of Rana el Kaliouby

Rana el Kaliouby


Rana el Kaliouby is cofounder and CEO of Affectiva—a pioneer in emotion AI, the next frontier of artificial intelligence—where she leads the company’s award-winning emotion recognition technology, built on a science platform that uses deep learning and the world’s largest emotion data repository of nearly 4.9 million faces analyzed from 75 countries, amounting to more than 50 billion emotion data points. Previously, Rana was a research scientist at MIT Media Lab, where she spearheaded the applications of emotion technology in a variety of fields, including mental health and autism research. Her work has appeared in numerous publications including the New Yorker, Wired, Forbes, Fast Company, the Wall Street Journal, the New York Times, CNN, CBS, Time magazine, Fortune, and Reddit. A TED speaker, she was recognized by TechCrunch as a women founder who crushed it in 2016, by Entrepreneur magazine as one of the seven most powerful women to watch in 2014, and on Ad Age’s 40 under 40 list. Rana has also been inducted into the Women in Engineering Hall of Fame and is a recipient of Technology Review’s 2012 Top 35 Innovators Under 35 award and Smithsonian magazine’s 2015 American Ingenuity Award for Technology. Rana holds a BSc and MSc in computer science from the American University in Cairo and a PhD from the Computer Laboratory at the University of Cambridge.