> Artificial visual systems that have, at best, only a rudimentary understanding of what they are looking at, are in no position to act this way, and can assign high confidence values to what we would regard as ludicrous interpretations of the scene.
It's not a good idea to generalize like that. There are many AI systems that use neural nets for perception and then use different models, such as conditional random fields (CRF) to see if entities make sense in context.
Not to mention that these adversarial examples don't work well from all distances and angles. As a car changes position slightly, the adversarial example disappears. It's very hard to create an adversarial example that works from all angles on all networks. There is also a special training regimen that fortifies neural nets against adversarial examples.
The problem of adversarial images is closely related to GANs and has been brought into attention by the same researcher, Ian Goodfellow. So it's not a neglected problem - some of the top minds are on it.
The adversarial examples don't need to work from all distances and angles.
Many street signs especially in rural areas might have a pretty small visibility window and they will only be visible from a pretty fixed angle until you pass them.
So while you might not be able to develop an adversarial example that works for all signs from all distances and angles it wouldn't be hard to develop one for a specific sign.
The angle for that sign is known, the distance in which most cars could resolve the sign is also known, the average speed on the road can also be taken into account which gives you a rough idea of the time span the autonomous driver would have to identify and resolve the sign.
Heck if you know the algorithm it wouldn't be that hard to build an app that basically allows you to take a picture of the sign with your phone from the road, select a car or subset of cars you want to jam and it would generate a likely pattern to apply to the sign to jam those cars.
I should also add that in this case some of the examples that do seem to work look to be of the shape and size of common bumper stickers, this is pretty worrisome since these are not that uncommon on street signs, especially in rural areas where they are more or less at reachable heights.
I also wonder how well do these systems deal with graffiti.
Wait until AI puts most truckers out of a job and you suddenly have a substantial number of people in rural America feeling resentful towards fully automated freight trucks... adversarial examples are one thing, but I think the US military's experience in Iraq has taught us that a determined, intelligent, and constantly innovating human adversary is a continuing challenge.
Especially given that the cost of crashing an unmanned truck will be 100% capital and 0% human, I expect we'll start seeing some pretty serious criminal penalties passed once the tech gets there.
> So it's not a neglected problem - some of the top minds are on it.
That's why I believe it will be fixed. It is the "it's not a problem" crowd that I take issue with, and I would be surprised if many of the top researchers held that position, unless they are confident that they are close to a solution.
I think you are under-generalizing here. The risk here is not of a sign being mistaken for a gorilla specifically, or even about signs specifically, but the fragility of image recognition in general. Similarly, because the issue is not (just) about adversarial images, the fragility of adversarial images is not much of a mitigating factor.
It's not a good idea to generalize like that. There are many AI systems that use neural nets for perception and then use different models, such as conditional random fields (CRF) to see if entities make sense in context.
Not to mention that these adversarial examples don't work well from all distances and angles. As a car changes position slightly, the adversarial example disappears. It's very hard to create an adversarial example that works from all angles on all networks. There is also a special training regimen that fortifies neural nets against adversarial examples.
The problem of adversarial images is closely related to GANs and has been brought into attention by the same researcher, Ian Goodfellow. So it's not a neglected problem - some of the top minds are on it.