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Comment by pgodzin | Hacker News Reader

> To teach a computer to recognize a cat from many angles, for example, could require thousands of photos covering a variety of perspectives. Human children don’t need such explicit and extensive training to learn to recognize a household pet.

Human children see their pet from a million different viewpoints every day

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Sure, but they're not being explicitly trained to do anything. It just happens because that's what children do. Also, you don't have to label their cat experiences to identify them from the other millions of experiences they have in a day. You don't need to, they just figure it out without even realizing it.

That's pretty great.

Yes, and toddlers (my 14 month old...) can see a cat in a picture, and recognize a cat in real life, even if it's different color.

Sure, but at some point you do label "This is our cat Tabby. He lives here." Now every time they see a cat in the house, it is implicitly labelled. I'm definitely disagreeing with "extensive" more than "explicit" in the original quote, but I think it's silly to differentiate explicit vs implicit when talking about a human vs computer

How do you know it's the same cat? A neural network needs to be explicitly told, "these are all the same cat".

Not necessarily: https://www.theverge.com/2017/10/16/16483542/google-photos-r...

This article says that google photos’ model can still have trouble telling your pets apart if you have the same breed - it kind of disproves your point. The model needs to be explicitly told which cat is which. That said, I’m pretty sure a toddler would probably need to be told which is which too.

Don't forget humans don't just see pictures - they see a video upon which they are learning.

Micro-movements of cat body parts and their more general character traits could be the only hint that separates two supposedly same cats.

I wonder where this fits in: https://twitter.com/poorlycatdraw/status/924696744713228288

Or one photo rotated 36 times during training? Instead of having CNN with just translation invariance, you can have one with rotational invariance as well.

Humans see video; it's easier to derive stuff from videos than from static photos. It's awesome we can teach a car to drive from single photos, but those models that incorporate time factor, 3D convolutions + RNNs, perform better...

If you could give an adversarial network control over a 3D camera around an object, you could train that way.

A final exam task for my high school informatics was to find coordinates of a cat in a picture within bounding box of that cat.

We weren't given anything to refer to except that the picture is 1000x1000 rgb array, and that the cat had stripes on the body.

I wonder, if the brain has no specific function to recognize geometry first, and then deduce the object further by secondary characteristics as in today's computer vision, but it synthesizes a resulting decision in reverse from many nearly independent "circuits" (people can guess that a cat is a cat even if they look at its partial image, or even if the cat is painted purple and the person has never seen a purple cat in real life)

Children don't see random pictures, they see videos (pictures in a sequence).

It amazes me how often this fact is ignored in any discussion of AI.

The real world is a pretty damn good "training environment".

I like your sentiment but then I realized of course it is. There is nothing more real than what is real. If suddenly we were dropped in a more random universe with less probabilistic rules, then what we have now wouldn't be good. Short of another reality, that's almost tautological.

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