I don't mean to be obtuse but I think there are two problems here...
First, I personally found that really difficult to follow - to the point that practically using that method seems ridiculous to me. Why would you ever do this instead of just showing a picture? I can only imagine how laborious it would be to rhetorically explain an entire alphabet through tedious description.
Second, and maybe more importantly, what is a "square" and what is a "line"? Sooner or later you need to anchor this in something visual or you'll have built up a brittle tower of abstraction. This seems like moving the goalposts.
I very strongly disagree that this example is illustrative of how children - or humans in general - could learn something with 0 samples. This strikes me as a contrived and inorganic way for humans to learn things which are fundamentally visual or auditory in nature, not semantic.
That it's hard to follow is beside the point (and it's hard because this particular character is rather complex, but I wanted to pick the character for "picture" as a pun; some characters are much simpler, and could be very easily described, while others are far more complex). People are capable of doing that, and do it all the time (maybe not with characters, but certainly with other things). Sure, it requires prior knowledge, but that we can do it and do it often, suggests that perhaps most forms of high-level learning internally build this "tower of abstraction". One could claim that the internal layers of an NN do something similar, but that's not quite accurate, because the backpropagation process is global, rather than modular.
The way I actually used that description is to mentally construct the example from it, to then recognize "画". Verbal descriptions are just a different way to input examples, they don't help you learn without any examples at all.
But that's not the way NNs work. NNs learn classification on a particular representation only. From the NN perspective, this kind of description is exactly zero samples (of the required representation).
> NNs learn classification on a particular representation only
To perform the task based on the description, you need to have learned to associate descriptions of images with the image itself. To train that ability you'll need lots of paired examples in both representations.
That the description is not in the same representation as the image you want to recognize doesn't change the fact that both need to use a representation that has been trained on.
Cross-representation learning is still useful because it increases the range of usable data (e.g. it can improve game-playing agents by telling them how to play https://arxiv.org/abs/1704.05539), but it doesn't magically enable learning from zero samples (just more kinds of samples).
> To train that ability you'll need lots of paired examples in both representations.
People rarely ever need lots of paired examples, except possibly once, when they learn the concept of "paired representation," and even then probably a few examples suffice to learn an extremely abstract concept that is then used in all learning. People simply don't learn high-level concepts through statistical clustering.
> that both need to use a representation that has been trained on.
But this training is of a very different nature. As far as I know, not a single person claims that NNs learn in a way remotely similar to how people learn (nor are they meant to), certainly not when it comes to high-level concepts.
First, I personally found that really difficult to follow - to the point that practically using that method seems ridiculous to me. Why would you ever do this instead of just showing a picture? I can only imagine how laborious it would be to rhetorically explain an entire alphabet through tedious description.
Second, and maybe more importantly, what is a "square" and what is a "line"? Sooner or later you need to anchor this in something visual or you'll have built up a brittle tower of abstraction. This seems like moving the goalposts.
I very strongly disagree that this example is illustrative of how children - or humans in general - could learn something with 0 samples. This strikes me as a contrived and inorganic way for humans to learn things which are fundamentally visual or auditory in nature, not semantic.