Affectiva Leadership Rana 790x832

Courtesy of Affectiva

You may not know it, but a woman is leading the way to emotionally intelligent AI. Rana el Kaliouby, a graduate of MIT and the University of Cambridge, is the CEO and Co-Founder of a company called Affectiva. With a keen eye for business, Kaliouby has been working on an emotionally intelligent AI, the value which on the worldwide market is expected to l reach $24.74 billion by 2020. The technology that Affectiva uses involves speech recognition, computer vision, machine learning, and deep learning. The software uses information collected on facial expressions to make judgments. 



Here are a few other female leaders changing the AI industry: 

Fei Fei Li

Courtesy of Standford


Fei-Fei Li is an AI expert known for making her field more inclusive for women and minorities. She currently works as a professor in the Computer Science Department of Stanford University. She also happens to be the co-head of the college’s Human-Centered AI Institute, which focuses on using AI to advance the good of all humankind.




Cynthia Breazeal

Courtesy of MIT


Cynthia Breazeal worked on Jibo, a robot that can socialize with humans and recognize facial movements. The robot is capable of recognizing people’s voices, as well as parsing language. Cynthia is the founder of the MIT Media Lab’s Personal Robots group. As a professor, she teaches media arts and sciences at the Massachusetts Institute of Technology.



Tlau22 Techonomy

Courtesy of QCon San Francisco


Tessa Lau is known for creating robots that make construction safer and more efficient. She is the founder and CEO of a company called Dusty Robotics. She is a brilliant woman ahead of her time, as construction-focused AI industry is expected to be worth $2 billion worldwide by 2023.






Courtesy of Zimbio


Timnit Gebru believes that social justice is actively intertwined with AI, despite appearances to the contrary. After all, there are many human elements that surround the manufacture and usage of AI, and this influences how AIs behave. For example, she works to solve algorithm bias, which is when a computer system repeatedly creates errors ensuring an arbitrary group of users is unfairly privileged over another. Currently, she is also pursuing how to increase the diversity of people working with AI.