Greater adoption of artificial intelligence by business depends on universities doing more fundamental research with their corporate partners.
Take self-driving cars. Advances in neural networks, the software that recognizes and acts on patterns by sifting through huge quantities of data, have let companies like Google's Waymo and General Motors’s Cruise develop autonomous vehicles that are better than a few years ago.
Still, self-driving cars are years, and possibly decades, from widespread use. The best way to accelerate the needed innovation is cooperation between academics and business, explained Martial Hebert, Carnegie Mellon University’s (CMU) dean of computer science.
Researchers have “200 years of engineering science” to draw from when developing complicated machinery like automobiles. This rich history helps researchers certify and explain how their technologies work. This is important so that when people “take an elevator,” it’s not a mystery to engineers, and by extension, the general public, as to why they are moving up or down, Hebert said.
But with machine learning, “we have basically none of that,” said Herbert said, creating a big challenge for companies developing self-driving cars.
“How do you validate the performance of a system whose performance depends not just on the correctness of the code or the hardware, but all of the data it used for training,” Herbert said about proving how A.I. systems work. “How do you do that when the data can evolve over time in ways that you cannot predict ahead of time?”
Autonomous vehicles still have trouble navigating through hailstorms, fog, and snow. And researchers still can't foresee every road condition that can confuse a car's neural network.
Herbert said that self-driving car companies need universities to create the mathematical tools and computing techniques for testing and certifying their A.I. systems. Likewise, universities need companies to provide them with the real-life data that is required to create these technology development standards and methods.
He cited a partnership between CMU and the Ford-backed self-driving company Argo AI as a prime example of industry working with academia. Under the partnership, Argo AI lets CMU researchers access its driving data, which will help the university develop A.I. tools and techniques for explaining how self-driving cars work. This kind of technology, although basic, is critical and could be used to test other kinds of A.I. systems, making it a worthy academic pursuit, Herbert explained.
Other CMU corporate partners include Amazon, Apple, Google, Facebook, and German engineering giant Bosch. These companies also have major A.I. and engineering hubs in CMU’s hometown of Pittsburgh, partly to help with their work on A.I. with local schools.
Herbert said such partnerships are for the long term, just like improving A.I. is a never-ending job. “When is computer vision going to be solved?” said Herbert, referring to the technology used to teach computers to see like humans. “The answer is never.”
Still, not every corporate and academic partnership is as rosy a picture as Herbert paints.
In 2015, Uber poached several professors and researchers from CMU after partnering earlier with it. At the time, Uber was trying to develop self-driving cars, but it longer plans to do so. In December, Uber CEO Dara Khosrowshahi sold the research unit to self-driving car company Aurora; as part of the deal, Uber took a stake in Aurora. Herbert minimized the impact of the raid on his school's staff by saying that Uber only hired a small number of CMU researchers relative to how many the university now employs.
"Word of our demise was largely exaggerated," Herbert said.
Jonathan Vanian
@JonathanVanian
jonathan.vanian@fortune.com
