I was sitting on a tarmac recently— plane delayed due to last minute maintenance— when an article in the Washington Post caught my eye: a new partnership between Boeing and Carnegie Mellon University might keep me away from such irritating delays in the future.
As part of a three-year, $7.5 million deal that will establish a new Aerospace Data Analytics Lab, Boeing and the Carnegie Mellon School of Computer Science will work on a range of new projects that will apply the principles of AI and big data to improving the quality of Boeing’s aerospace activities.
While the principal goal of the venture is to make sense of the burgeoning amount of data in the aerospace industry, one of its outcomes may be what the Post terms the self-healing airplane.
“The mass of data generated daily by the aerospace industry overwhelms human understanding, but recent advances in language technologies and machine learning give us every reason to expect that we can gain useful insights from that data,” says Jaime Carbonell, Carnegie Mellon professor and Director of the Language Technologies Institute, who will head up the new Aerospace Data Analytics Lab.
One example of how machine learning can be used to gain useful insights is the whole issue of airline maintenance. Think of airline maintenance the same way you think of maintenance for our car – you can follow the generally suggested guidelines for your vehicle (e.g. an oil change every 3,000 miles) – or you can use real-time data to see which planes needs fixing, when. By fixing planes before – not after – they need maintenance, an airline flying Boeing planes could gain a real competitive advantage over its peers.
This means determining when planes actually need maintenance instead of following historical maintenance schedules.
And while Boeing’s CIO speaks of using this initiative to “push the technology envelope,” I am thinking it would be great just to push these planes out of the airport on schedule— a victory for intelligence artificial or otherwise.