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Researchers Fooled a Google AI Into Thinking a Rifle Was a Helicopter (wired.com)
An anonymous reader shares a Wired report: Algorithms, unlike humans, are susceptible to a specific type of problem called an "adversarial example." These are specially designed optical illusions that fool computers into doing things like mistake a picture of a panda for one of a gibbon. They can be images, sounds, or paragraphs of text. Think of them as hallucinations for algorithms. While a panda-gibbon mix-up may seem low stakes, an adversarial example could thwart the AI system that controls a self-driving car, for instance, causing it to mistake a stop sign for a speed limit one. They've already been used to beat other kinds of algorithms, like spam filters. Those adversarial examples are also much easier to create than was previously understood, according to research released Wednesday from MIT's Computer Science and Artificial Intelligence Laboratory. And not just under controlled conditions; the team reliably fooled Google's Cloud Vision API, a machine learning algorithm used in the real world today. For example, in November another team at MIT (with many of the same researchers) published a study demonstrating how Google's InceptionV3 image classifier could be duped into thinking that a 3-D-printed turtle was a rifle. In fact, researchers could manipulate the AI into thinking the turtle was any object they wanted.
Some can even mistake their wife for a hat!
Some can even mistake their wife for a hat!
Which actually raises an interesting question: Can advanced AI systems develop rare and bizarre neurological disorders as those described by Dr. Oliver Sachs . . . ?
That's something we might want to think about avoiding if it is a military AI system . . .
Schroedinger's Brexit: The UK is both in and out of the EU at the same time!
Humans are susceptible to optical illusions too, and optical illusions have caused driving accidents. Not to mention other human failings.
The key problem with AI, is its trust in in its sources. They havn't programmed in a silly algorithm yet. When kids are learning to process the world. Kids learn when things are in the wrong context then it is probably silly or just wrong. Even if it from a trusted source, a kid will laugh at their parent if they are saying something that is contradicting their view of the world. Such as when the parent is playing with the kid, they substitute a toy car for a doll, and play with the car like a doll. The child find this amusing because the context is all wrong. The AI algorithm seeing this, would just say this toy probably of usage has expanded to be used as a doll so it must be a doll. There is no questioning saying "no, that is not how you play with that toy". it will take the source as factual and just add it to its list.
If something is so important that you feel the need to post it on the internet... It probably isn't that important.
All of these vision AIs are programmed backwards -- for convenience. This random object looks "more like" a speed limit sign and "less like" a stop sign. Great. No body cares about how much "like" something another something appears.
You can ask any 10-year old. A stop sign is a red octagon. Any 16-year old will say it also has a white border, and white lettering in the middle. Any experienced driver will add that it appears at some sort of intersection, obstruction, or event, alongside a narrow road.
Now, if you see a red octagon, and you stop, and it turns out to be a giant lollipop, then that's good. Because a giant lollipop on the road is absolutely acting as a stop sign.
If something isn't a red octagon, then it's definitely not a stop sign.
The problem here is that google's vision AI doesn't identify an sight according to what defines a stop sign -- a red octagon on the side of the road. That's because it's highly stupid.
And the question really comes down to something much simpler. If I put a big square sign on the side of the road, blue, with yellow lettering, that says "please pause, thank you", will google treat it as a stop sign? Good bet that any driver who sees it (and can certainly be forgiven for not noticing it) will stop.
Conversely, on a highway, at 120kph, if I put a real stop sign on the side of the road, will google treat it as a stop sign? No human driver is going to slam on the brakes.
Google's not thinking. Therefore, it ain't an AI. It surely "looks like" an AI, but it's not an AI. It uses collected intelligence to determine what the object is, but it doesn't use its own intelligence to make decisions. It doesn't make decisions at all.
Show me a vision system that can take any photograph of any road, and decide whether or not it should stop the car. Doesn't need to be right or wrong, correct or incorrect, it just needs to make a decision, reliably, that makes sense. See, if it can do that, "reliably", then we can change the signs for them. We chose the signs for us for a reason. Humans see red first, so stop signs are red. If machines have trouble with octagons, and love purple, then we can give them that instead. Dual signage is common in multi-lingual communities.
But these shitty AI systems are much worse. They don't even make their classifications reliably -- because the more data they collect, the most they distract themselves. So a guaranteed "this is a stop sign, 100%" can change a year later, as it "learns", such that the very same stop sign is now only 80%. There's no fortification. There's no stubbornness. That's a problem.
Like feeling a terrible pain in all the diodes on your left side? Or deciding that your human colleagues are jeopardizing your mission and need to be eliminated.
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