Sultry, sultry, Austin, Texas. This was the site of the IEEE WIE ILC conference held on May 23-24, and I had the privilege of attending both days, along with 1400 other attendees. From the IEEE WIE ILC conference website, the conference is described as follows:

Launched 5 years ago, the IEEE Women in Engineering International Leadership Conference (IEEE WIE ILC) provides professional women in technology, whether in industry, academia, or government, the opportunity to create communities that fuel innovation, facilitate knowledge sharing and provide support through highly interactive sessions designed to foster discussion and collaboration. IEEE WIE ILC focuses on providing leading-edge professional development for mid-level and senior women.

Overall, I attended a dozen keynotes and sessions, two breakfasts, two lunches, a networking happy hour, and moseyed through the exhibitor hall, looking at the booths and chatting with many people.

One theme that emerged, at least considering the sessions I attended, is the concept of diversity. Obviously, diversity in the workplace is important—and there were sessions about that, too—but that’s not what I’m talking about. This conference had keynotes and sessions on AI, and many of the presenters were concerned with the diversity of training data for AI systems.

The Sessions

Candice Worley, chief technology strategist at McAfee pointed out that measurement bias, algorithmic bias, sample bias, and prejudicial bias exist in training any new AI system. Training itself is inherently biased because training data is created by humans, who are also inherently biased. The problem is how to train AI to have the subtlety that humans use. Common sense doesn’t exist for AI systems—even little kids can tell you that hippos can’t ride bikes, but an AI system needs to be told this. How do you teach a system about things that aren’t written down?

In another session, Conrad Tucker, associate professor at Penn State, presented a new technology that detects a patient’s heartbeat using AI facial recognition. As shown on their website, Video Vitals uses an RPG camera that amplifies the colors and plots relevant points on the vascular areas of the face, specifically the patient’s cheeks and between the eyebrows. The system detects minute changes of color in the user’s face, which indicates their heartbeat. This technology is a great first step in gathering vital information about patients in a remote medical environment.