These things will be fixed in time. There is nothing to be gained by pretending that they are not problems.
These things will be fixed in time. There is nothing to be gained by pretending that they are not problems.
If they were trying to directly use this info, this would be the least of the serious issues. For example, there are plenty of stop signs well hidden by trees. Such a car would probably last about a mile in a non freeway setting.
A trivial model of "hey, does this make any sense appearing at this place on a map" defeats this, etc.
Of course this stuff is it is infancy and can do dumb things. But this is just isn't that big a deal.
Humans also act much worse than you imply. Look at the number of people who accelerate into crashes instead of braking, etc. Again, doesn't mean AI should get a pass, and humans are infinitely more complex, but we shouldn't be held up as amazing at this either.
(That's actually the part that worries me. That we aren't good at it)
This is not unlike the human body's deeply-evolved reflexes - before the neocortex can even process that a pan is boiling hot, the nervous system bypasses the brain and and tells the hands to drop it.
In the case of driverless cars, DannyBee is absolutely correct: truly driverless cars will not make decisions from a single data point. I have some past experience with unmanned aerial vehicles and those things had insane amounts of redundancy in almost every signal and decision making process; driverless cars are more complex and will come decades later and will probably take this even further.
In the case of driving, however, the problem of deciding what behavior to veto is itself a hard problem - perhaps the hardest of all. When the system monitoring the AI is itself AI, the argument becomes circular - which is not to say it cannot be solved, but I think it makes it harder to dismiss the concerns.
Oh look:
http://www.rtl-sdr.com/spoofing-gps-locations-with-low-cost-...
Seriously. Y'all realize how much in your daily life would pretty much explode and kill people if that wasn't true, right?
What are the guarantees that those higher-level "complex systems" aren't going to have some weird behaviors as well?
The problem isn't just that ANNs misclassify adversarial examples. The problem is that it's counter-intuitive to an average observer and that no one clearly knows why those examples generalize so well. It's a remarkable property that a lot of AI "enthusiasts" try to downplay.
It's one thing to wire up unreliable but simple and fully understood components into a more reliable system. It's an entirely different level of challenge if the "unreliable components" are complex and poorly understood.
> Humans also act much worse than you imply. Look at the number of people who accelerate into crashes instead of braking, etc.
Except we know very well how and how often people make mistakes. For most of those mistakes we have a pretty good idea why they happen (at the high level, I'm not talking about neuroscience). Our roads, our cars and our laws are designed to handle these failures. Also, we have a pretty good model of how other people behave on the road, so we cal react accordingly.
All of this goes out of the window with self-driving cars.
Hell, most people naively assume that if a single self-driving gets into 50% less accidents than an average person than replacing all drivers with self-driving cars will reduce the global accident rate by at least 50%. This assumption doesn't take into account the fact that many accidents are caused by complex interactions between several vehicles and the environment. So introducing many self-driving cars can lead to some nasty emergent behaviors and some mass accidents that just aren't possible with human drivers.
I'd say that's a pretty big deal.
I think the problem with the driver-less car is that it doesn't have goal-oriented general perception like human beings do. Humans filter the world through goals. The world is interpreted as a set of affordances and threats to your goals, your brain is predisposed with distinguishing the two.
As it stands, this is not how driver-less cars are being designed, they are being designed to specifically recognize the presence and content of street features like signs and other moving bodies. A human driver might see a tall parked car with a big gap in front, and recognize a pedestrian affordance which he should steer clear of in case somebody pops out. The way I see people talk about driver-less cars, this "common sense" type of judgement would have to be deliberately integrated.
... and the solutions will be written in blood.
But I suspect that driverless cars will start out pretty safe and improve from there.
I think there is an interesting distinction that is worth discussing ...
At the advent of train/plane travel, you could opt out of those novel risks by eschewing those modes of travel.
However, you could be driving along in your driver-piloted car, minding your own business and find yourself involved in an accident caused by the software in a driverless car you are sharing the road with.
Before cars, people routinely walked in the middle of streets. "Jaywalking" wasn't a concept. It was entirely up to early cars to stop in time, and they didn't have good brakes. Fortunately they weren't very fast.
Also, early pilots, besides the obvious risks, were sometimes daredevils who would do things like buzz houses.
I think the main point here is that society's attitude towards risk has changed; we take small risks much more seriously than they used to when earlier forms of transportation were introduced. Accidents involving driverless cars make the national news and manufacturers are being very cautious. With earlier attitudes towards risk, I expect they'd be in widespread use by now.
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.
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.
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.
I think that scenario isn't as likely as unintentional screwups at the moment.
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.
No. It's just a fact of parsimonious signal representation that sometimes you get null spaces with which you can manipulate to maximally separate the performance of two very different receivers.