Out-of-work truckers armed with “adversarial machine learning” could dazzle autonomous vehicles into crashing.
Before autonomous trucks and taxis hit the road, manufacturers will need to solve problems far more complex than collision avoidance and navigation (see “10 Breakthrough Technologies 2017: Self-Driving Trucks”).
These vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning (see “AI Fight Club Could Help Save Us from a Future of Super-Smart Cyberattacks”). As consensus grows that autonomous vehicles are just a few years away from being deployed in cities as robotic taxis, and on highways to ease the mind-numbing boredom of long-haul trucking, this risk of attack has been largely missing from the breathless coverage.
It reminds me of numerous articles promoting e-mail in the early 1990s, before the newfound world of electronic communications was awash in unwanted spam. Back then, the promise of machine learning was seen as a solution to the world’s spam problems. And indeed, today the problem of spam is largely solved—but it took decades for us to get here.
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