Are you sure about your examples of things neural nets can't do? I think GANs might be able to "imagine" striped cats, provided they have been trained on enough images to capture the space of black/white/striped objects. And a lynx being classified as a cat doesn't seem so outlandish. It has to be classified as something and cats are likely the closest in appearance.

Of course these are just based on my intuition of what neural nets are capable of, so if you have examples of cases where these specific tasks were attempted unsuccessfully, I'm interested.

Let me remind you of sofas being classified as cats[0] and people being classified as gorillas[1]. You're overestimating the guesswork convnets are able to do, based on fragile training (which is still better guesswork than what previous models did).

[0] http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.ht...

[1] https://www.theverge.com/2015/7/1/8880363/google-apologizes-...

People being classified as gorillas was actually what I was thinking about regarding the lynx/cat example. The model might have been unsure about the kind of ape it was looking at, but clustering them together is its own kind of achievement.

The thing is that the convnets are unable to learn about "macro structures" (or structures in general). A cat has ~4 legs, a tail and pointy ears. Gorillas are black, have a primate-y face and fur. The sofa is lacking the tail, head and pointy ears. People were missing the fur. Yet those things did not prevent the net from missclassffication (because those features weren't detected in the learning phase).

Once again, children are able to see a cat and extract all that relevant information: four legs, head, tail, eyes & nose & ears with a particular shape, different than dogs, most cats fur (except for those alien-looking furless cats, of course).

If you ask a child to draw a hand, they will almost always draw it with five fingers stuck straight out, widely separated. This is a view of a hand that one almost never actually sees; generally you'll have fingers clustered together, occluding each other, foreshortened, etc. So why do they draw it like that?

It's because they're drawing a conceptual, represntational model of a hand, not a distilation of visual "hand" characteristics. That's the difference with human learning: it's based on representational model-making, which is not at all the same thing as pattern matching.

The representation is the distillation of pattern matching. They are isomorphic.

Is that a belief or a fact? I believe, but cannot prove, that symbolic representation is not isomorphic to pattern matching.

If you reverse the output of a CNN "hand" classification, it'll give you images that resemble the geometry and shading of fingers, palms, nails, knuckles, etc. -- these, I submit, are the distillation pattern matching for the actuality of "hands". Under no circumstances will it give you the five widely-separated fingers which a child draws. That's because the child-drawn hand is based on literal visual stimuli, but rather on an abstract logical model of a hand. That logical model is fully integrated with a similarly abstract model of the world, and includes functional relationships between abstractions, like the knowledge that "hands" can open "jars". The value of these being logical models rather than matched patterns is that they can then be extended to include never-before-seen objects. Confronted with a strange but roughly jar-sized object, a child can surmise that maybe it, too, can be opened with hands. That isn't pattern-matching: it's algebra.

If you look at network activations in higher levels of a convolutional neural network, you will find some units that are activated by the individual parts of the hand. (There is a video out there where they show that for parts of the face, I'll be looking for a link.)

Those activations are a representation of the matched patterns at a similar level of abstraction as the compositional model humans might come up with. They are not exactly the same as what a child would draw, but mostly because the neural network isn't trained to draw with hands. With a bit of work, I'm sure a bunch of PhDs could make a neural network model generate child-like drawings from realistic images.

Algebra is pattern matching of a set of operation rules with regard to a space. Your jar example just extends the domain to physicality. And I agree - until these sorts of learning mechanisms have a wide range of quality realisms to pattern match from - they will not be able to form the type of cross visual/physical knowledge that is a much deeper and abstract undestanding of reality. But don't fool yourself. Humans and well..life..are just input output machines with incredible pattern matching capabilities. Algebraic representations are the structural result of that pattern matching.

> But don't fool yourself. Humans and well..life..are just input output machines with incredible pattern matching capabilities.

See, that seems to me like a statement of faith which I just don't share. I think that building relational models of the world via abstract inductive reasoning is qualitatively different than pattern matching. I don't think there's some magic tonnage of pattern matching at which abstract inductive reasoning will suddenly emerge. I don't think that they're isomorphic. I think the AI toolkit still has a few missing pieces.

The only way to induce a consequence in a scenario is to have pattern matched the scenario. Pattern matching can be very abstract. It can use programs that may not halt. You are conflating patterns with exact details. A pattern can be as general as "[wildcard]." The human psyche promotes survival over *, every scenario.

You talk about representations and reasoning but are not assessing the fact that the human brain is literally a decision maker, acting on stored procedures and memory. Any representations and any reasoning will only apply to a select scenario or select objects, regardless to how you wish to define the pattern, the fact that a subset of abstractness/generality out of the whole of existence is specified, implies a pattern that is coded for implicitly or explicitly.

You claim to have the facts on the human brain?

My God, the level of hubris expressed by members of the cult of AI has reached a fever-pitch.

Stored procedures and memory?

Newton, in the age of clocks, managed to present the universe in the image of a clock. Is it any wonder that computer programmers present the universe in the image of the computer?

I would be interested regardless of the outcome.

GANs and style transfer are not the same as being able to recognize and imagine changes on the representation you just learned. Also, look at the GAN examples: even the water in the background is being affected by the "horse to zebra" transfer. You can perfectly imagine a brown horse, standing in the beach, and then being told "now iamgine the horse is white" without than "instruction" affecting how you imagine the beach.

Perhaps explain what your preferred dataset is.

+ Brown horses on beaches

+ Some way to indicate "white" "horse".

If you have a good idea about how we can train for your problem, I'm not so convinced that it cannot be solved.

No, the problem I was pointing at is that you want to change a part of the image (horse into zebra), but the style transfer GAN learned to map pixels from one space into another. So it knows it has to change the colour of some things, and add stripes here and there, and also probably there too. But it isn't consistent with the stripes (you can see in some gifs how the pattern suddenly changes and adjusts), and it doesn't recognize (segment) the horse as the only relevant thing that has to be changed.

But that's not bad per se about style transfer. It's an interesting technique, but if you want to convert all horses to zebras in an image, that seems to be a bit too general for current-generation GAN architectures. Maybe it can be improved upon, or a different, novel architecture is required, and not just something you can solve by throwing more data at it.

Yes, the segmentation could be better. I still think you're not pointing out fundamental issues though. :-)