AI adversarial attacks are usually employed by evildoers, but we might find useful the use of such attacks against evil AI, raising thorny AI Ethics issues (including for self-driving cars).
Understanding The Levels Of Self-Driving Cars
Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se . With that clarification, you can envision that the AI driving system won’t natively somehow “know” about the facets of driving. Driving and all that it entails will need to be programmed as part of the hardware and software of the self-driving car.First, it is important to realize that not all AI self-driving cars are the same. Each automaker and self-driving tech firm is taking its approach to devising self-driving cars.
You might be tempted to think that this is not much of an adversarial attack and that it seems rather innocuous. Well, suppose that you are driving in a car and meanwhile a self-driving car that is ahead of you suddenly and seemingly without any basis for doing so comes to an abrupt stop . You might ram into that self-driving car. It could be that the AI was fooled into computationally calculating that a non-stop sign was a Stop sign, thus committing a false-positive error.
That briefly explains the nature of false-positive and false-negative attacks. There can be a lot more trickery involved. I mentioned the simplest case as a means of showcasing what those types of adversarial attacks consist of. For the moment, we will continue to explore how adversarial attacks work.
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