"Emotion" is the last thing any scientist or design engineer wants to deal with, especially when it comes to developing computing systems. "Oh, dear," Rosalind Picard, professor at the MIT (Massachusetts Institute of Technology) Media Laboratory, remembers muttering to herself, when it first became unavoidably clear to her that "emotion is vital to intelligent functions." Picard was then working on machine learning systems. "Scientists want to be rational. We develop machines that decide right or wrong in terms of 1's and 0's," said Picard.
Growing up in Egypt in the 1980s, Rana el Kaliouby was fascinated by hidden languages--the rapid-fire blinks of 1s and 0s computers use to transform electricity into commands and the infinitely more complicated nonverbal cues that teenagers use to transmit volumes of hormone-laden information to each other. Culture and social stigma discouraged girls like el Kaliouby in the Middle East from hacking either code, but she wasn't deterred. When her father brought home an Atari video game console and challenged the three el Kaliouby sisters to figure out how it worked, Rana gleefully did. When she wasn't allowed to date, el Kaliouby studied her peers the same way that she did the Atari. "I was always the first one to say'Oh, he has a crush on her' because of all of the gestures and the eye contact," she says.
In 1998, while looking for topics for her Master's thesis at the American University in Cairo, [Rana] el Kaliouby stumbled upon a book by MIT researcher Rosalind Picard. It argued that, since emotions play a large role in human decision-making, machines will require emotional intelligence if they are to truly understand human needs. El Kaliouby was captivated by the idea that feelings could be measured, analyzed, and used to design systems that can genuinely connect with people. Today, el Kaliouby is the CEO of Affectiva, a company that's building the type of emotionally intelligent AI systems Picard envisioned two decades ago. Affectiva's software measures a user's emotional response through algorithms that identify key facial landmarks and analyze pixels in those regions to classify facial expressions.
People are good at understanding one another's emotions. We realize quickly that now is not a good time to approach the boss or that a loved one is having a lousy day. These skills are so essential that those without them are considered disabled. Yet until recently, our machines could not identify even seemingly simple emotions, like anger or frustration. The GPS device chirps happily even when the driver is ready to hurl it out the window.