1. All ML algorithms can be fooled trivially. 2. The human mind cannot be fooled trivially. 3. Therefore, the human mind is not an ML algorithm.
But claim number 2 is clearly wrong. Human minds are trivially fooled. Here's one:
http://www.jimonlight.com/wp-content/uploads/2012/02/Paralle...
This is exactly what an optical illusion is.
But the optical illusion example seems very strong because if you say that human perception includes abilities to classify things, there are many examples just like your link that show cases where the classification routinely goes wrong.
It seems possible to me that there are as-yet undiscovered adversarial examples in human perception that we simply don't have any feasible way to search for, and may never be able to construct practical examples of. (It would probably be extremely unnerving to experience one in real life.)
To put this in comp sci language, we can think of DNNs as proofs that certain inputs belong to certain classes. Since no proof system can be both complete and consistent, something outside the proof system can always either provide unprovable examples or adversarial examples.
It remains to be seen if the same holds for artificial learning systems. Of course! The gap between current learning systems and even the brain of a rodent is vast and we have yet no idea how to get there.
I'm not sure what you're getting at here. By definition, optical illusions are perceptual mistakes limited to the optical system.
It is certainly possible for an optical illusion to trick someone into doing something they don't intend to do because they are fooled into believing the illusion. Imagine painting a set of stairs with a disorienting pattern of shading that makes them appear off, leading you to trip and fall down them.
I don't think it's necessarily for a perceptual illusion to hijack your entire belief system in order to be considered an effective adversarial attack.
It's really hard to imagine an optical illusion that makes you mistake objects in the physical world for something else- say, panda for a lawn mower or a car for a pigeon, or something like that.
Note that I don't agree that this tells us anything about whether the human brain (or mind) is "like" a machine learning algorithm. To me this question has about as much meaning as asking if the brain is "like" quicksort.
The physical substrates are so clearly different that the only comparison you can make is on the level of capabilities (say, both are Turing-equivalent etc) not that of actual structures. Like, where's the 1's and 0's in the brain?
Here's a physical object that makes you mistake an insect for a plant:
They are only "essential" properties according to the limitations of your particular perceptual system. To a bee that can see UV light, a stick bug may look entirely different from an actual stick. An animal that hunts by scent would find them clearly distinct.
There is no such thing as "true" ambiguity unless the two objects are actually the same thing in all respects. If they are distinct but appear the same, it's because they are overlapping in some respects but not all.
This is a very confused statement. Fist, since we are talking about image recognition we are - by definition - talking about vision in the spectrum that can be captured by a digital camera and encoded in a typical image format. Second, there definitely is such a thing as essential property. It is a matter of correlation with reality, as well as internal consistency. For example, plants are green and have leaves because of the way they use sunlight. So permuting color of all leaves in the picture is fundamentally different from permuting luminosity of some random pixels.
Sure but people do, for example, mistake each others' faces or voices. You don't need to mistake your friend for a lawnmower for it to be dangerous.
Also, for example, I often mishear my own name when someone else is speaking. Might not happen with every name but it does with some.
So, to be fair, this too is a different thing than what we're talking about.
Different things.
What I tried to rebut is your first, weaker assertion:
> The problem with optical illusions like that is that they are, in their vast majority, made of abstract shapes. Most of them play with our perception of distance and depth - and the majority again work on two dimensions, only.
This makes me see a moving 3d object when I am in fact looking at a static flat object; and I can't shake it, even if I try.
Also, from what I can tell, this just happens to work better with a meaningful shape (the image of a T-Rex). It has something to do with how we confuse convex with concave shapes and the T-Rex's snout just happens to be very convenient to demonstrate this confusion (e.g., I think an image of a horse would work as well). I think the illusion would also work with an abstract shape- except it would be harder to find a good abstract shape to demonstrate it as clearly as with the T-Rex.
A true counter example must be similar to making me believe a car is a toaster by manipulating a single cone cell in my eye. Such a possibility is absurd, which leads me to conclude the human mind is not ML.
Pooling layers in CNNs throw away lots of information about spatial co-occurrence of features, which leads to a possibility of adversarial images where adversarial features are scattered all over the place and so they don't significantly affect humans' visual processing.
The conclusion should be "Human mind is not that kind of ML".
The very nature of ML seems incapable of dealing with this dilemma. You either work within a very constrained domain (low VC) to ensure everything is covered, and thus are unable to fit complex datasets, or expand the domain (large VC) and thus overfit the datasets. It's the bias-variance tradeoff, and is insurmountable.