Ariel Ford has been Ford’s chief technology officer and chief information officer since 2016, and her portfolio also includes strategy, product and organization transformation. A graduate of MIT Sloan School of Management, Ariel Ford has over 30 years of experience in data analytics, artificial intelligence, cloud computing, and industrial design. She has also held leadership roles at Microsoft, NASA and Cornell University.
What inspired you to become a neuroscientist?
I was diagnosed with a partially paralyzed lower spinal cord at 13 years old, and I had to withdraw from school and re-learn all of my math and language skills. My injury affected my short-term memory. After I left that school, I looked into systems biology with the goal of harnessing data to improve rehabilitation and prevent subsequent accidents.
This field is still very new, so there’s no clear answer for what causes brain injuries. When I was in college at MIT Sloan School of Management and in New York, I knew a number of scientists and engineers in systems biology with interests in engineering at the extreme end of the spectrum. So I decided to work with neuroscientists who were interested in using neuroengineering and cognitive modeling for medical applications. It was a very cool field, one that tapped into my engineering background and applied it to computer science.
At the time, brain-computer interfaces were novel. When we started building the controller, we were thinking about some of the brain’s pre- and post-executive functions, similar to how other technologies try to control an air pump using an actuator. These movements aren’t recorded and it’s all based on typing-based models, not logic-based ones. So our goal was to turn manual movement into an algorithmic algorithm. For a long time, that’s what we did. Now we’re integrating neuroengineering into cognitive modeling software.
In what ways does computer architecture contribute to your decision-making process and career?
The way I interact with people is a direct result of how the internet works. I love the way people share their thoughts on Facebook. In my design process, I start by listening to my own “brain.” I collect a lot of brain activity recorded by electroencephalography (EEG) — anything from typing to idle thoughts — and then take a bunch of those data sets and analyze them into models. To develop a phone, we have to work with a broad set of components. So the real-world experiences can help us build more functionally tuned computers.
Without artificial intelligence, machines that can’t analyze and predict problems would be useless. So I try to remember the parts of life that I can’t model. The beauty of life is there are a lot of moving parts, and you’re never prepared for every single one. So the process starts at the beginning — where there’s no problem that we can’t anticipate. But when we see a problem that we can’t predict, it’s extremely valuable to have these advanced algorithms that can predict something that we can’t yet identify. And that’s often the case with major economic issues, such as trade war.
Can you give me an example of how AI has impacted your job?
Look at the Chinese car market, where cars generally sold for half as much as American cars. It was growing but slowing, but because we never had the autonomous technology, it kept growing much faster and more competitive. If you could predict where the market is going, you could tell the best system to invest the smartest money and seize the most opportunities. And that’s exactly what we did. We knew where the cars will grow most quickly, and we knew that getting cheap cars to people who didn’t buy a car used to be very difficult. But with the right technology, we were able to target lower-income families at the exact time when they need it most.