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“The easier it is to communicate, the faster change happens.” – James Burke, Science Historian

During an October 2015 press conference announcing the autopilot feature of the Tesla Model S, which allowed the car to drive semi-autonomously, Tesla CEO Elon Musk said each driver would become an “expert trainer” for every Model S. Each car could improve its own autonomous features by learning from its driver, but more significantly, when one Tesla learned from its own driver—that knowledge could then be shared with every other Tesla vehicle. As Fred Lambert with Electrik reported shortly after, Model S owners noticed how quickly the car’s driverless features were improving. In one example, Teslas were taking incorrect early exits along highways, forcing their owners to manually steer the car along the correct route. After just a few weeks, owners noted the cars were no longer taking premature exits.

“I find it remarkable that it is improving this rapidly,” said one Tesla owner.

Intelligent systems, like those powered by the latest round of machine learning software, aren’t just getting smarter: they’re getting smarter faster. Understanding the rate at which these systems develop can be a particularly challenging part of navigating technological change.

Ray Kurzweil has written extensively on the gaps in human understanding between what he calls the “intuitive linear” view of technological change and the “exponential” rate of change now taking place. Almost two decades after writing the influential essay on what he calls “The Law of Accelerating Returns”—a theory of evolutionary change concerned with the speed at which systems improve over time—connected devices are now sharing knowledge between themselves, escalating the speed at which they improve.

“I think that this is perhaps the biggest exponential trend in AI,” said Hod Lipson, professor of mechanical engineering and data science at Columbia University, in a recent interview.

“All of the exponential technology trends have different ‘exponents,’” Lipson added. “But this one is potentially the biggest.”




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