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In this episode of the podcast (#180), Gary McGraw of the Berryville Institute of Machine Learning joins us to talk about the top security threats facing machine learning systems.
As long as humans have contemplated the idea of computers they have contemplated the idea of computers that are capable of thinking – reasoning. And as long as they’ve contemplated the notion of a thinking machine, they’ve wondered about how to contend with the consequences of computers’ faulty reasoning?
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Stories about machines acting logically – but based on faulty or incorrect assumptions – are the fuel for science fiction tales ranging from 2001: A Space Odyssey (Arthur C. Clark) to Minority Report by Philip Dick, to the 1980s cult classics like the movies War Games and The Terminator.
So far, these warnings have been the stuff of fiction. But advances in computing power and accessibility in recent years has put rocket boosters on the applications and abilities of machine learning technology, which now influences everything from multi-billion dollar trades on Wall Street, to medical diagnosis to what movie Netflix recommends you watch next.
As machine learning and automation fuel business disruption, however, what about the security of machine learning systems? Might decisions be manipulated and corrupted by malicious actors intent on sowing disruption or lining their own pocket? And when machine decisions go awry, how will the humans impacted by those decisions know?