5 ms·

When you say you're not sure, are you saying you're not sure children could be confused like that? I've witnessed it before. Especially for very young children,

When you say you're not sure, are you saying you're not sure children could be confused like that? I've witnessed it before. Especially for very young children, it can take several repetitions of mapping a word to different depictions of an object before they realize what you're talking about.

pron 8y ago
They can be confused, but not quite in the same manner as a statistical clustering algorithm. Children (or adults) don't seem to learn by statistical clustering. It's often said that NNs require many samples while humans normally just need one, but a more precise description is that humans need zero. I can teach you to recognize a Chinese character without showing you even a single picture. It is true that this requires some background knowledge that small children lack, but there is little reason to believe that their learning is in any way similar to statistical clustering.
- throwawaymath 8y ago
  How would you teach me to recognise a Chinese character without showing me a picture?
  - pron 8y ago
    A square, divided into four equal squares, floating symmetrically inside a larger rectangular U whose two vertical sides slightly protrude lower than the horizontal bar, and above the square, a horizontal line, slightly longer than the side of the square and equal in length and alignment to the horizontal line below the square, the base of the U.
    - throwawaymath 8y ago
      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.
      - yorwba 8y ago
        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.
        pron 8y ago
        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).
        yorwba 8y ago
        > 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 https://arxiv.org/abs/1704.05539), but it doesn't magically enable learning from zero samples (just more kinds of samples).
        pron 8y ago
        > 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.

pron 8y ago

They can be confused, but not quite in the same manner as a statistical clustering algorithm. Children (or adults) don't seem to learn by statistical clustering. It's often said that NNs require many samples while humans normally just need one, but a more precise description is that humans need zero. I can teach you to recognize a Chinese character without showing you even a single picture. It is true that this requires some background knowledge that small children lack, but there is little reason to believe that their learning is in any way similar to statistical clustering.

throwawaymath 8y ago
How would you teach me to recognise a Chinese character without showing me a picture?
- pron 8y ago
  A square, divided into four equal squares, floating symmetrically inside a larger rectangular U whose two vertical sides slightly protrude lower than the horizontal bar, and above the square, a horizontal line, slightly longer than the side of the square and equal in length and alignment to the horizontal line below the square, the base of the U.
  - throwawaymath 8y ago
    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.
    - yorwba 8y ago
      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.
      - pron 8y ago
        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).
        yorwba 8y ago
        > 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 https://arxiv.org/abs/1704.05539), but it doesn't magically enable learning from zero samples (just more kinds of samples).
        pron 8y ago
        > 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.