Here’s a nightmare story for you: Machines, endowed with artificial intelligence, get smarter than their creators, take charge and attempt to save humans from themselves. Oops. Smarts, it turns out, are different from wisdom. Pooh-poohing a saying variously attributed to Socrates, Aristotle as well Albert Einstein — the more I know, the more I realize I know nothing — the machines push ahead and bring civilization crashing down on all of us.
This sci-fi trope could have a real-world parallel. When engineers give AI more leeway in controlling complex systems, how do they keep the machines from overstepping their bounds and putting property, and perhaps lives, in danger? For instance, what’s to keep an AI in charge of optimizing the efficiency of a generator from pushing it past the breaking point for the sake of an extra kilowatt-hour of performance?
This lack-of-wisdom problem weighs heavily on Colin Parris, head of software at GE’s Research Center. There are clear benefits in an AI algorithm that, say, continually adjusts the tilt of the blades and the speed of a wind turbine’s rotation to reach an optimal level of performance based on a simulation of what happened in the past. The turbine generates more power with less wear and tear on the equipment, which translates to lower costs.
But there’s also some risk involved. The ability of the algorithm to achieve these results is closely linked with the accuracy of the machine learning algorithm it relies on. “When you build a model, you make an implicit assumption that the data you use when you execute the model will be the same as the data when you built the model,” Parris says. “For example, if I build a model in the summer, it may not be accurate in the winter.”
The real world can also stir up a “black swan,” an event of low probability with possibly catastrophic consequences. What happens when, say, a weird weather pattern produces a once-in-a-hundred-year gust of wind that was not captured in the data originally fed into the learning algorithm? The uncertainty over what a computer running a wind farm is going to do in those rare situations poses a business risk.
The solution, says Parris, may be to program humility into AI. That doesn’t mean endowing the algorithm with feelings — though some researchers are looking for ways to code morals. It means giving the AI an awareness of the limitations of the simulation of the real world it relies on, and an alternative way of proceeding that removes any uncertainty from its behavior. That plan B is typically an old “deterministic” algorithm that sacrifices peak performance for extra caution and predictability. “[Humble] AI knows its region of competency,” Parris says. “It can be defined as a region of temperatures and pressures. When it goes outside that region, right away it says, I’m going to revert back to an old algorithm, which has been robust for many years.”
Parris and his colleagues have undertaken two pilot programs to test this concept. One is a wind farm with two turbines. An AI there is forecasting the wind speed, adjusting the pitch of the blades and other factors to catch as much wind as possible. The other pilot program involves electricity-generating gas turbines, where the algorithm controls the nozzles that mix air and gas and regulate the pressures in the combustion chamber. In both situations, GE engineers have seen improvements in energy output of 1 or 2 percent.
Parris expects this new “humble AI” to be incorporated into commercial products by the end of 2019 or early 2020. Going forward, whenever the algorithms encounter conditions beyond their realm of competency, they will ask for new data so that they can expand the situations they’re able to handle.
The humble-AI concept is important, Parris says, to encourage acceptance of this powerful new tool. The best AI in the world is not going to work if it makes people nervous. Says Parris: “Humble AI allows people to embrace AI according to rules they feel comfortable with.”
How long is the world’s largest wind turbine blade? Stretching 107 meters, the blade is longer than a football field and equal to 1.4 times the length of a Boeing 747. Using a different measure, it would take Usain Bolt, the fastest human and a world record holder in the 100-meter dash, close to 10 seconds to race from its root to its tip. It might also represent one of the largest single machine components ever built. Workers just popped the first one from its mold at an LM Wind Power factory in Cherbourg, France.
Three of these blades will form the rotor of GE’s Haliade-X 12 MW, the world’s largest and most powerful offshore wind turbine, which is capable of powering 16,000 European homes. GE acquired LM Wind Power, the world’s largest designer and manufacturer of wind turbine blades, in 2017.
GE also recently laid the foundation for the first Haliade-X in Rotterdam, Holland. When complete, the prototype, which will stand on land, will be 260 meters tall from base to blade tips, and the rotor will sweep a circle with a diameter of 220 meters. The machine is expected to start producing electricity later this year.
The brand-new factory where LM Wind Power makes the massive blades for the Haliade-X 12 MW is located on the banks of the English Channel in Normandy, just a short drive from the wide, sandy beaches where Allied troops landed on D-Day. LM Wind Power built the plant near Cherbourg’s industrial port to allow workers to load the blades onto ships and send them to their destination.
Operating in three shifts, workers build the blades from a high-tech sandwich made from thin layers of glass and carbon fibers, and wood. They fuse everything together with a special resin.
GE Renewable Energy will put two blades through rigorous testing to demonstrate their ability to withstand more than 20 years of spinning offshore. Lukasz Cejrowski has been building wind turbine blades at LM Wind Power for more than a decade and oversees the company’s effort to build the 107-meter blade. Originally from Poland, he’s been living in Cherbourg since 2017, when LM Wind Power broke ground on the new factory. To him, the size of the blade is a matter of perspective. “When you spend some time with the blade, it doesn’t seem so big anymore. You get used to it,” he laughs. “Then again, after a hard day, I remind myself: If breaking a world record was easy, then everybody would do it, right?”
Bolt would agree.