Inspired by Google, GE Is Learning About Jet Engines through AI
“GE already makes hundreds of millions of dollars by crunching the data it collects from deep-sea oil wells or jet engines to optimize performance, anticipate breakdowns, and streamline maintenance. But Colin Parris, who joined GE Software from IBM late last year as vice president of software research, believes that continued advances in data-processing power, sensors, and predictive algorithms will soon give his company the same sharpness of insight into the individual vagaries of a jet engine that Google has into the online behavior of a 24-year-old netizen from West Hollywood.”
That was written by McKinsey Quarterly in June of 2015. As of February 2017, the resulting platform, Predix, launched as an AI based B2B platform-as-a-service business. This involves selling access to their cloud-based AI platform for analyzing data from industrial machines to predict when they will need maintenance as well as suggest performance optimizations. More recently, Mark Grabb, a member of GE’s executive leadership team working on digital research, framed their progress this way:
”We are also starting to see more applications where AI is becoming part of a ‘living system,’ where it’s continuously learning. There’s a new analytical structure that’s being used for AI. We are starting to see significant performance increases from the combination of deep learning and reinforcement learning, where you have a human in the loop correcting the system. Once you build a smooth user experience and get the system going, people don’t even know they are correcting the AI along the way.”
In other words, GE is using AI technology they developed for their own use to develop a new business model. This model has GE simultaneously developing an AI platform for itself and other businesses, and in doing so, they’re gaining access to more diverse operational data, which improves their model. This is similar to a network-effect advantage that Amazon Web Services had.
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