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Comment by snovv_crash | Hacker News Reader

My problem with our brains and NN is exactly this "fuzzy geometry" you speak of. Using precise geometry, if we have a pair of cameras equivalent in layout to eyes we can reconstruct a world with mm-precise accuracy. That is something that cannot be done with neural networks (artificial or biological), instead you get a sort of semantic tagging of "green sofa here, table here, bed there".

But if afterwards you want to use this model to know if the sofa will fit into the alcove (or the car into the garage), the NN systems will be wildly unreliable.

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It seems to me that humans can predict 3D spaces pretty good.

I've done alot of construction work. After some practice you can measure quite precisely what does fit where and even how long something is. Our brains seem to be able to "compute" such things despite all the difficulties of constructing a coherent image of our surroundings in the first place.

Or think of moving your huge couch through the narrow stairway of your house - we can predict how you need to turn it so it fits. Or think of truck drivers that are able to maneuver their large vehicles within cm range. Even when they can't see some of it (dead angles) and need to rely on their mental model.

Did I misunderstand your comment? Could you elaborate what I am missing?

Anecdotal evidence: I used to work at a construction materials depot when I was younger. I remember how lost I was when I initially started, I couldn't recognize any of the material sizes by looking at them, while all the old timers always just knew what size they were just by glancing at them. Then only after a couple of months, suddenly I could too. Years later I'm still pretty good at guessing lengths, while people around me seem to always be way off. I can hold out my hands and make a foot, three feet, 1 cm, 0,5 cm 10 cm between them pretty reliably, while other people seem to be way off.

I'm talking about the optical illusions such as seeing a small person next to a car makes the car look bigger. Anything based on ML instead of geometry, or even using ML as a strong prior, will be susceptible to this kind of problem.

You can learn to estimate various geometrical quantities with surprising accuracy, but it always depends on context (scale of the geometry, distance to it and angle from which you view it).

One interesting thing that I recently noticed is that essentially everybody is going to grossly overestimate the angle of slope of road and at least while driving many people perceive curves of less than ~30° as essentially straight road.

You would probably hate David Hockney's famous photo collages then.

https://photomuserh.wordpress.com/2012/03/04/david-hockney-p...

I don't see what this has to do with being able to make real-world accurate measurements and predictions based on CV algorithms.

And from first glance, I actually find his stuff pretty cool.

Ah, I misinterpreted this as you saying that you dislike the fuzzy geometry of the human mind in general - not specifically for measurement purposes:

> My problem with our brains (...) is exactly this "fuzzy geometry"

Wow, this is really fascinating. It's remarkable that the human brain (well, at least my brain) has almost no issue understanding the "scene". I wonder how a NN would do...

The composition of the scenes seems to neatly match the particular way how human vision works, namely, that we look for/at details with only a narrow center part of our vision, and gather the total detailed picture by scanning over it with our eyes.

So, for this type of artwork, whenever we're looking for details, we rest our eyes at a single separate photograph that covers those details fully just as if we'd be looking at a normal real image; but this contrasts with the wider view captured by our peripheral vision, which is obviously artificial.

Hockney was fully aware of this mechanism when he made these works, which motivated him to make them in the first place:

> For his part, critics often got Hockney all wrong as well, misinterpreting the intensity of the ways he would presently be engaging photography—taking literally hundreds of thousands of photos, coming to feel that the Old Masters themselves had been in thrall to a similar optical aesthetic—as a celebration of the photographic over the painterly, and specifically the post-optical painterly, when in fact all along he’d been engaged in a rigorous critique of photography and the optical as “all right,” in his words, “if you don’t mind looking at the world from the point of view of a paralyzed cyclops, for a split second, but that’s not how the world really is.”

http://www.believermag.com/issues/200811/?read=article_wesch...

Like anything, the more tools in your toolbox, the more problems you can solve!

I love that humans seem to be innately wired to constantly create new and better tools.

But those conventional, mm accuracy detectors tend to work best when they know what to look for. Isn't this, preanalyzing the scene for tailored vision algorithms, where those fuzzy NN classifiers (will) have the biggest utility?

ML vision may get all the fame right now, but useful applications will need both.

I'm not talking about detection, I'm talking about 3D reconstruction and making judgements on whether X will be able to pass by Y without hitting it.

ML has all the same weaknesses that humans do in that they are easily fooled and can't accurately project outside of their trained parameter space. Without an accurate physics model and an integrated comprehension of the geometry involved we are basically planning to put autistic pigeons in charge of driving our cars.

That seems like a bad idea. But don't computers have the upper because they can do both simultaneously and weight the inputs appropriately?

But the nice thing about computers is that you can feed the results of the high level ML classifier into the mm accurate geometry detector. You can only determine whether X will be able to pass by Y if you already know that there is an X and a Y.

Humans and pigeons will identify an X and a Y and will then try to use the same method to classify the scene as either "will pass" or "won't pass". Computers can do the fuzzy thing for identification, but don't have to use the same method for "will pass"/"won't pass". They can switch to geometric measuring and simulation for that.

In order to accurattely ascertain whether thing X will fit into Y hole you have to know accurate geometry of both which is not something that can be reliably determined from results of the fuzzy classification pass. When humans want to know this they either trust their fuzzy judgement and try it or measure the X and Y in question with tape measure.

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