I don't know of any optical illusions where a bus turned upside down is perceived as a plowshare, or an image of an elephant superimposed on the image of a room causes the other objects in the room to be misidentified.
Human optical illusions reveal the structural bias in the way we see, but that has nothing to do with adversarial examples and overfitting to a few pixels.
I wouldn't be so sure about that actually. Observing my daughters, I'm sometimes extremely surprised about how they identify objects. As you grow, your concious mind probably simply does not notice the continuous cross-validation happening.
Also, at the severe augmentation levels of years of ~50 fps "live video" training data, I can imagine there's another compensation algorithm at work in humans that tries to logically orient any picture into a most common, "upright" fashion before even trying to identify anything.
Humans give oddities "more CPU" (or run like heck to avoid being trampled and worry later about being right). We apply life experience and logic to explore it deeper. In the elephant case, the shadows look wrong also, which should trigger the "Maybe it's a cheap Photoshop job" module in our brain. But NN's don't understand the concept of cheap Photoshop jobs because they don't browse the web for cute kitten videos and fun but fake news like humans do. Maybe NN's can be trained for fake photo detection, but solving every identification hurdle of the human world by adding yet another dimension to the training step probably won't scale.
If I'm thinking of the same picture you are, it's a schoolbus on its side on a snowy road that is misidentified as a snowplow.
FWIW, that picture does look a lot like a snowplow to my eye. Big yellow rectangle against road cleared of snow? If I glanced at it, I might have said it's a snowplow.
I wonder what the mental model for that is - I've always assumed the brain iterates and refines but short-circuits when we're tired since processing is slower/limited.
The human mind has a lot more potential inputs AND it also tends to have years or decades of experience to draw on as well. Give the AI a quadcopter to fly around in and a huge database and it's going to go investigate the mislabeled data and figure out that it was misled. Similarly people walk towards mirages looking for an oasis.
What kind of AI is that? It has the ability to choose its own goals, to figure out when it's made a mistake and explore the world around it to correct its model of it? That's a pretty damn advanced AI.
Edit: Btw, if you think that sort of thing can work and that all you need is an AI, a quadcopter and a huge database- why don't you try it? The rewards would be astronomical, well worth any investment.
1) Database with objects likely to be found together and statistical weights next to them.
2) Image recognition that can link to the database from 1.
3) Lidar that will allow the quadcopter to map environment in 3D regardless of how it labeled objects.
4) Any object that shows up that shows a statistical unlikely placement triggers the quadcopter to fly around it in a circle in an attempt to get a different picture so that a relabel attempt can be made.
I do admit that my total understanding of how good image recognition is isn't state of the art, but I thought there were some success stories.
> Btw, if you think that sort of thing can work and that all you need is an AI, a quadcopter and a huge database- why don't you try it? The rewards would be astronomical, well worth any investment.
This does sound a bit like, "If you're so smart why aren't you rich." I suppose I won't be able to implement the plan because A) I don't have access to massive databases that contain objects and the other objects they are likely to be found next to and B) I don't think my "fly around weird stuff and try to relabel" algorithm is likely to have a very good business model.
What do you mean "link"? Link- how? Why does the image recognition model need a separate database? From your description, that sounds like a database with proximity relations between different objects. "Statistical weights" sounds vague- what statistics? Weights appying to what? In any case, what objects would go to the database? Where would they come from? Are you imagining a database of pairs of images of all possibles objects the AI might encounter and in every possible combination? That is unrealistic.
You are describing a setup that is not at all like the image recognition systems in existence today, that take in a large number of labelled examples of images of various types of objects (the labels determine the type of object) and output the parameters of a classification function.
4) Any object that shows up that shows a statistical unlikely placement triggers the quadcopter to fly around it in a circle in an attempt to get a different picture so that a relabel attempt can be made.
Yes, this is too ambitious. You seem to base this ability on the proximity database you describe earlier, but like I say, such a database is unrealistic. Even given such a database, your proposed system would not automatically have the ability to take correct decisions based on that database- you'd have to develop the ability to "fly around it in a circle" etc separately, and that is a very hard problem. Because it's a hard problem to develop intelligent agents with autonomous behaviour, particularly with intrinsinc motivations, like a motivation to refine their own model of the world. At the moment, this kind of thing is science fiction.
I didn't mean to offend you. The creation of the database you describe would have a very high monetary cost, but if what you propose could work you'd get your money back in spades, and undying fame through the ages, to boot, because you would have solved strong AI. So, if you really think it can work and you don't see why not- try it; and see why not.
But if you won't try it, at least try to learn a few things about AI and figure out why what you propose makes no sense given current technological capabilities.
No that's not what I had in mind.
> Are you imagining a database of pairs of images of all possibles objects the AI might encounter and in every possible combination?
No, that's not what I had in mind.
> You seem to base this ability on the proximity database you describe earlier,
If you say so, but that wasn't my intent.
> "fly around it in a circle" etc separately, and that is a very hard problem
The first thing that I actually did suggest was possible. Using lidar to make sure you don't bump into anything, fly left of the object and once lidar indicates you've cleared it in the other axis fly "towards it. Continue until you've circled it (although, in this scenario it's a square). Made much easier because quadcopters fly so it's much less likely to bump into something.
Here's a video where someone claims to have an autonomous plane that avoids obstacles: https://www.youtube.com/watch?v=_qah8oIzCwk
That's the only thing my claim needs.
I'm sorry if I don't understand what kind of database you mean, still, but that's because you keep describing it in very vague terms. First it was a "huge database" (of what?), then it was a "Database with objects likely to be found together and statistical weights next to them". What kind of objects, what kind of statistics and what kind of weights?
I mean, if you yourself don't know what you're trying to say, don't blame me for not getting it.
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