7 ms·

Humans usually can't do your 2. either. In some cases, people may be able to recognize things based on descriptions alone, but those are typically simple combin

Humans usually can't do your 2. either. In some cases, people may be able to recognize things based on descriptions alone, but those are typically simple combinations of known entities. For recognizing relatively simple entities, are there advantages humans still have over neural nets (assuming the same scope of knowledge)?

teekert 10y ago
Definitely my 3 y/o can recognize a cat in an abstract drawing of a cat that is unlike any cat he has seen before.
- nopinsight 10y ago
  Humans are great at learning abstraction from concrete examples. That's also what deep learning does and the big reason for its success as well. I'd guess that some neural nets architecture can do the same with your cat example (perhaps with adaptation). Can any expert weigh in? An idea: We can also run several cat photos through image processing algorithms to filter out details. The output would be outlines similar to the drawings in the Google Quickdraw app. We put those through the app to generalize (perhaps the app needs some training with a few categories of objects, not necessarily animals). Voila! Software can now recognize drawings based on photo examples.
  - gcp 10y ago
    Basically: you are right.
  - vanderZwan 10y ago
    > Humans are great at learning abstraction Of course, there's severe bias here, in the sense that what we consider abstraction is by definition "human shaped" abstraction If multiple humans try to "abstract" a cat, the overlap in underlying processes will be pretty big, making it more likely that we can recognise each other's abstractions.
    - gcp 10y ago
      Of course, there's severe bias here, in the sense that what we consider abstraction is by definition "human shaped" abstraction I can read the words here, but I don't understand the meaning. We abstract to find a common set of features in things that are supposed to be the same but that are not present in things that are not supposed to be the same. Grouping these features then produces higher level abstractions, and so on. Where would the bias be? Even if the features differ, the process is the same. And even the features are often the same. If you reverse a DCNN to see what it uses to classify things as "cats", expect to see whiskers and fur.
      - vanderZwan 10y ago
        You implicitly (and I think without realising) presume objectivity + complete knowledge in the observer. Human perception is heavily biased towards features that had evolutionary advantages, and limited by whatever technical flaws our eyes/brains/etc have. That's a selection bias in our perception of information, in our processing of said information, and therefore in the abstractions that result from it.
        gcp 10y ago
        I agree with what you say, but it doesn't support your earlier statements. I presume it's possible that the limitations of our visual system means we may miss powerful features and hence the ability to build some more powerful abstractions. (I didn't even argue this, just pointed out the process is the same even if features differ) But I don't see how this supports your original claim of bias, which was: "If multiple humans try to "abstract" a cat, the overlap in underlying processes will be pretty big, making it more likely that we can recognize each other's abstractions." If humans are good at recognizing each others' abstraction, that's a validation that low-pass (for lack of a better term) filtering the features due to human's physical design still creates very good abstractions and classifiers. That is to say, if anything you're confirming that humans are designed in a way that makes the abstractions they can make maximally useful.
        10y ago
  - TheGrumpyBrit 10y ago
    Humans have a bit of an advantage on two levels here. First, we know what a cat looks like. Not a video or a picture or a drawing, but an actual cat. That gives us a solid frame of reference. "That is definitely a cat. That drawing looks kind of like what I know a cat to look like, so it's a drawing of a cat." The closest a computer can get is "This drawing has quite a bit in common with these other drawings, and apparently these other drawings are cats. So this is probably a cat too." Second, when we look at a picture of a cat, we're looking at a human's interpretation of what a cat looks like. If we asked a computer to draw a cat, it might look nothing like a cat to us, but another computer could look at it and go "Oh sure, that's a cat." I seem to recall Google did a thing with this a while ago, where they effectively created a feedback loop in a neural net - feeding its own drawing back into itself. As I recall, the result looked like the computer had done way too much LSD.
- raldi 10y ago
  Can you sketch an example of such a drawing? I'm having a hard time imagining something that looks enough like a cat to be recognized as such but unlike any cat a three-year-old has ever seen before.
  - tim333 10y ago
    Not OP but how about https://images.template.net/wp-content/uploads/2016/03/15120347/Smiling-Cat-Drawing-Free-Download-PDF.jpg https://images.template.net/wp-content/uploads/2016/03/15120...
    - raldi 10y ago
      I'd say that misses both my criteria: it looks just like lots of cat drawings any three-year-old has been exposed to, and it also seems like an image Google would have no trouble recognizing as a cat.
  - taneq 10y ago
    http://i.imgur.com/VbRTHRp.webp http://i.imgur.com/VbRTHRp.webp
    - raldi 10y ago
      Again, I think first-world children over the age of 3 have been exposed to plenty of drawings like that, and also, Google can recognize it as a cat anyway -- in fact, it even knows which cat; do an Image Search and you'll see, "Best guess for this image: garfield meme"
      - taneq 10y ago
        Your criteria were "looks enough like a cat to be recognized as one, but unlike any cat that a 3-year-old would have seen before". Google doesn't recognize it as a feline, it recognizes it as Garfield.
- daveheq 10y ago
  I doubt that webpage is as smart as a 3-year-old.
Jugurtha 10y ago
>Humans usually can't do your 2. I think we do. We see a building we've never seen before and we know it's a building because it has certain features that we use to classify it as a building. The examples aren't scarce. I also think a good indicator of us doing it is the use of "y" and "ish" and "sort". As for sthlm's point 2: >2. The software can't recognize a feather if it's never seen a feather like that. It's not a sentient being. This is Asimo in 2009: https://youtu.be/6rqO5eiP7_k?t=5m24s https://youtu.be/6rqO5eiP7_k?t=5m24s
- msinclair 10y ago
  When it comes to abstraction from a simple rendering – no shading, no sense of depth, no discernible dimensions – it's hard to extrapolate features. I feel there is an immense difference between recognizing simple sketches and deriving what an object is based on extended characteristics. The video you linked furthers that by showing that ASIMO was using three-dimensional observation to calculate certain features and ascertain what that object was.
  - gcp 10y ago
    The abstract drawings benefit a lot from the limited selection and the huge implicit context. If you'd give these doodles to people that are not Western males it'll do a lot worse. Someone already pointed out it doesn't recognize woman's shoes.
  - laumars 10y ago
    Humans frequently misrecognise sketches too.
    - pbhjpbhj 10y ago
      If you've ever played pictionary you'll see the level of abstraction we can manage is remarkable too.
      - msinclair 10y ago
vacri 10y ago
You're just wrong on this one. Humans can recognise a lot of things that aren't in the form that they're used to. It's seen a lot of research in psychology. As for advantages over neural nets, one of the primary ones is that humans can recognise things from unusual angles much more easily. When I tried QuickDraw and doodled things from non-stereotyped angles (like a three-quarter view of a car rather than the usual 2D side view), it had no idea. The dalmation optical illusion[1] is another example of human ability to pick out patterns and assign them to belong to certain objects. Neural nets have different abilities, and are sometimes better at picking out different sorts of patterns than humans. [1] http://cdn.theatlantic.com/assets/media/img/posts/2014/05/Perception_in_a_field_of_spots/5c8f91f1a.gif http://cdn.theatlantic.com/assets/media/img/posts/2014/05/Pe...
eslaught 10y ago
Do you really think any reasonable person is going to mistake this couch for a cat? https://rocknrollnerd.github.io/assets/article_images/2015-05-27-leopard-sofa/sofa.jpg https://rocknrollnerd.github.io/assets/article_images/2015-0... The software does: https://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html https://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.h... Sure, you can fool a human. But there are things AI is missing that would be embarrassing if a human made the same mistake. It's hard to say, based on anecdotes like this, how big that gap is, but it's there.

teekert 10y ago

Definitely my 3 y/o can recognize a cat in an abstract drawing of a cat that is unlike any cat he has seen before.

nopinsight 10y ago
Humans are great at learning abstraction from concrete examples. That's also what deep learning does and the big reason for its success as well. I'd guess that some neural nets architecture can do the same with your cat example (perhaps with adaptation). Can any expert weigh in? An idea: We can also run several cat photos through image processing algorithms to filter out details. The output would be outlines similar to the drawings in the Google Quickdraw app. We put those through the app to generalize (perhaps the app needs some training with a few categories of objects, not necessarily animals). Voila! Software can now recognize drawings based on photo examples.
- gcp 10y ago
  Basically: you are right.
- vanderZwan 10y ago
  > Humans are great at learning abstraction Of course, there's severe bias here, in the sense that what we consider abstraction is by definition "human shaped" abstraction If multiple humans try to "abstract" a cat, the overlap in underlying processes will be pretty big, making it more likely that we can recognise each other's abstractions.
  - gcp 10y ago
    Of course, there's severe bias here, in the sense that what we consider abstraction is by definition "human shaped" abstraction I can read the words here, but I don't understand the meaning. We abstract to find a common set of features in things that are supposed to be the same but that are not present in things that are not supposed to be the same. Grouping these features then produces higher level abstractions, and so on. Where would the bias be? Even if the features differ, the process is the same. And even the features are often the same. If you reverse a DCNN to see what it uses to classify things as "cats", expect to see whiskers and fur.
    - vanderZwan 10y ago
      You implicitly (and I think without realising) presume objectivity + complete knowledge in the observer. Human perception is heavily biased towards features that had evolutionary advantages, and limited by whatever technical flaws our eyes/brains/etc have. That's a selection bias in our perception of information, in our processing of said information, and therefore in the abstractions that result from it.
      - gcp 10y ago
        I agree with what you say, but it doesn't support your earlier statements. I presume it's possible that the limitations of our visual system means we may miss powerful features and hence the ability to build some more powerful abstractions. (I didn't even argue this, just pointed out the process is the same even if features differ) But I don't see how this supports your original claim of bias, which was: "If multiple humans try to "abstract" a cat, the overlap in underlying processes will be pretty big, making it more likely that we can recognize each other's abstractions." If humans are good at recognizing each others' abstraction, that's a validation that low-pass (for lack of a better term) filtering the features due to human's physical design still creates very good abstractions and classifiers. That is to say, if anything you're confirming that humans are designed in a way that makes the abstractions they can make maximally useful.
        10y ago
- TheGrumpyBrit 10y ago
  Humans have a bit of an advantage on two levels here. First, we know what a cat looks like. Not a video or a picture or a drawing, but an actual cat. That gives us a solid frame of reference. "That is definitely a cat. That drawing looks kind of like what I know a cat to look like, so it's a drawing of a cat." The closest a computer can get is "This drawing has quite a bit in common with these other drawings, and apparently these other drawings are cats. So this is probably a cat too." Second, when we look at a picture of a cat, we're looking at a human's interpretation of what a cat looks like. If we asked a computer to draw a cat, it might look nothing like a cat to us, but another computer could look at it and go "Oh sure, that's a cat." I seem to recall Google did a thing with this a while ago, where they effectively created a feedback loop in a neural net - feeding its own drawing back into itself. As I recall, the result looked like the computer had done way too much LSD.
raldi 10y ago
Can you sketch an example of such a drawing? I'm having a hard time imagining something that looks enough like a cat to be recognized as such but unlike any cat a three-year-old has ever seen before.
- tim333 10y ago
  Not OP but how about https://images.template.net/wp-content/uploads/2016/03/15120347/Smiling-Cat-Drawing-Free-Download-PDF.jpg https://images.template.net/wp-content/uploads/2016/03/15120...
  - raldi 10y ago
    I'd say that misses both my criteria: it looks just like lots of cat drawings any three-year-old has been exposed to, and it also seems like an image Google would have no trouble recognizing as a cat.
- taneq 10y ago
  http://i.imgur.com/VbRTHRp.webp http://i.imgur.com/VbRTHRp.webp
  - raldi 10y ago
    Again, I think first-world children over the age of 3 have been exposed to plenty of drawings like that, and also, Google can recognize it as a cat anyway -- in fact, it even knows which cat; do an Image Search and you'll see, "Best guess for this image: garfield meme"
    - taneq 10y ago
      Your criteria were "looks enough like a cat to be recognized as one, but unlike any cat that a 3-year-old would have seen before". Google doesn't recognize it as a feline, it recognizes it as Garfield.
daveheq 10y ago
I doubt that webpage is as smart as a 3-year-old.

Jugurtha 10y ago

>Humans usually can't do your 2. I think we do. We see a building we've never seen before and we know it's a building because it has certain features that we use to classify it as a building. The examples aren't scarce. I also think a good indicator of us doing it is the use of "y" and "ish" and "sort". As for sthlm's point 2: >2. The software can't recognize a feather if it's never seen a feather like that. It's not a sentient being. This is Asimo in 2009: https://youtu.be/6rqO5eiP7_k?t=5m24s https://youtu.be/6rqO5eiP7_k?t=5m24s

msinclair 10y ago
When it comes to abstraction from a simple rendering – no shading, no sense of depth, no discernible dimensions – it's hard to extrapolate features. I feel there is an immense difference between recognizing simple sketches and deriving what an object is based on extended characteristics. The video you linked furthers that by showing that ASIMO was using three-dimensional observation to calculate certain features and ascertain what that object was.
- gcp 10y ago
  The abstract drawings benefit a lot from the limited selection and the huge implicit context. If you'd give these doodles to people that are not Western males it'll do a lot worse. Someone already pointed out it doesn't recognize woman's shoes.
- laumars 10y ago
  Humans frequently misrecognise sketches too.
  - pbhjpbhj 10y ago
    If you've ever played pictionary you'll see the level of abstraction we can manage is remarkable too.
    - msinclair 10y ago

vacri 10y ago

You're just wrong on this one. Humans can recognise a lot of things that aren't in the form that they're used to. It's seen a lot of research in psychology. As for advantages over neural nets, one of the primary ones is that humans can recognise things from unusual angles much more easily. When I tried QuickDraw and doodled things from non-stereotyped angles (like a three-quarter view of a car rather than the usual 2D side view), it had no idea. The dalmation optical illusion[1] is another example of human ability to pick out patterns and assign them to belong to certain objects. Neural nets have different abilities, and are sometimes better at picking out different sorts of patterns than humans. [1] http://cdn.theatlantic.com/assets/media/img/posts/2014/05/Perception_in_a_field_of_spots/5c8f91f1a.gif http://cdn.theatlantic.com/assets/media/img/posts/2014/05/Pe...

eslaught 10y ago

Do you really think any reasonable person is going to mistake this couch for a cat? https://rocknrollnerd.github.io/assets/article_images/2015-05-27-leopard-sofa/sofa.jpg https://rocknrollnerd.github.io/assets/article_images/2015-0... The software does: https://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html https://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.h... Sure, you can fool a human. But there are things AI is missing that would be embarrassing if a human made the same mistake. It's hard to say, based on anecdotes like this, how big that gap is, but it's there.