5 ms·

>Human babies don’t get tagged data sets, yet they manage just fine, and it’s important for us to understand how that happens I do not really understand this.

>Human babies don’t get tagged data sets, yet they manage just fine, and it’s important for us to understand how that happens I do not really understand this. Human babies get a constant stream of labeled information from their parents. Contextualized speech is being fed to them for years. Toddlers repeat everything you say. Is this referring to something else that babies can do?

ben_w 7y ago
The things you’re calling labels are themselves learned data, as we don’t start off with an innate knowledge of language. Whether that distinction is important or not, I don’t know enough brain science to guess.
onefuncman 7y ago
I agree with you -- we have little idea how much labeled information is encoded in the genome either. I rather like to think of the genome as the most well trained model of life we have access to.
richardw 7y ago
I show my 19 month old daughter like three cartoon drawings of owls and she recognises a live one at the bird park instantly, unprompted. We have a way to go.
- taneq 7y ago
  I believe cartoons are our equivalent of adversarial images. They typically look nothing like (photos of) their namesake and yet we recognise them usually without prompting.
  - EliRivers 7y ago
    It is my understanding (although I sure don't have any evidence on me) that cartoons and such (at least, the ones where we haven't simply learned that this cartoon means this animal) work by being a picture of what we remember about an animal. Akin to a caricature; the cartoon contains the most salient features. It doesn't work by looking like the actual animal; it works by reacting with how we remember the animal.
    - taneq 7y ago
      Isn't that kind of the same thing? Adversarial examples work by matching what the neural net 'remembers' about the target classification, rather than being a picture of a thing in that class. Neural nets just find different features salient . I've wondered in the past if we could use black box adversarial methods with Mechanical Turk to generate adversarial examples that work on humans. Maybe they'd end up looking like cartoons? (Also agreed, some cartoon animals are just informed likeness - for instance Goofy doesn't look anything like a dog, at least to me.)
    - YeGoblynQueenne 7y ago
      >> Akin to a caricature; the cartoon contains the most salient features The question is - how do we know what are the salient features? How do we figure out that if we make _this_ drawing, it will "remind of" an owl, and if we make _that_ drawing it will "remind of" a dog (or not, as the case may be)? I mean, if we knew that, how humans extract salient or relevant features from their environment, we'd be way ahead on the path to AI.
tsimionescu 7y ago
There may be some kind of labeling encoded in genes. One thing that it is safe to assume is genetically encoded somehow is that sounds made by your parents/humans around you is worth repeating while other sounds are not. However, past that, the actual sounds themselves, and any association to meaning, are pretty far from tagged data sets. Stuff like the specifics of language (e.g. that a dog is called 'dog') are definitely learned, and children learn them with typically only a handful of stimuli, often a single one. For contrast, imagine training a model with raw sound data tagged only with "speech" vs "not speech" (and probably only a few thousand data points at that) and I will be amazed if it can recognize a single word. And babies don't just learn words, they learn their association to things they see and hear, and grammar, and abstract thought. Do note that it is very likely that human brains can learn all that because they have some good heuristics built in. We definitely know some stuff is "hardware" - object recognition, basic mechanics, recognizing human faces and expression, and others. We are pretty sure higher level stuff is also built in - universal grammar, basic logic, some ability to simulate behavior seen/heard in other humans. This specialized hardware was also most likely learned, but over much, much greater periods of time, through evolution over hundreds of millions of years (since even extremely old animals are capable of picking out objects in the environment, approximating their speed etc).
- taneq 7y ago
  > One thing that it is safe to assume is genetically encoded somehow is that sounds made by your parents/humans around you is worth repeating while other sounds are not. Well, these sounds come with a face attached and we know babies are hardwired to pay attention to faces.
  - tsimionescu 7y ago
    That may well be the mechanism behind this. I was talking in very general terms, not a specific 'imitate humans' structure in the brain.
- Quarrelsome 7y ago
  > One thing that it is safe to assume is genetically encoded somehow is that sounds made by your parents/humans around you is worth repeating while other sounds are not. I don't see how that's safe to assume at all. What one could assume is the level of familiarity and comfort (sight, smell, touch) might be somewhat genetic and gives such inputs precedence. OR it might just be that those sources of information are engaging and animated. > Do note that it is very likely that human brains can learn all that because they have some good heuristics built in. Nor do I see this assumption having any weight, many of the heuristics we take for granted were hard fought, its just so long ago that we've forgotten the fight. Lets not forget how "little" our species gets over the first few YEARS of child development. If your child can move their body, just about walk and talk a little at TWO WHOLE YEARS in, they're an achiever.
faceplanted 7y ago
Humans don't _just_ learn to recognise the things they see though, they have complex mental models of the objects and things about them they can access by choice as well as make hypotheses about new things that they can immediately test, humans don't get labelled photos of cats, but they see cats in 3D and can interact with them and use spatial reasoning and walk around them to completely separate that cat from the background behind them.
YeGoblynQueenne 7y ago
I'm curious to know what you mean by "labelled information". I'm guessing that what you are calling "labelled information" is various forms of encouragement or discouragement that could be considered positively and negatively "labelled" examples. If that is the case, linguistics research back in the '70s found that infants get almost no negative examples of, in particular, language. For example, a parent will not correct a child by saying, "no you can't say 'eated' because then you could also say 'sitted'". Instead they will correct by saying "no, you should say 'ate'" etc. That is important because there was a famous proof in inductive inference (the precursor to computational learning theory) that languages higher in the Chomsky hierarchy than regular languages cannot be learned from positive examples alone. And yet, babies eventually learn to speak human languages, which are assumed to be at least context-free. Chomsky used these findings to support his claim of a "universal grammar" or innate language endowment [1]. If you are talking about multi-class labelling, that's even harder to imagine. In machine learning, a multi-class classifier will map inputs to some set of categorical labels (i.e. a set of integers) but the mapping from those labels to concepts that a human would recognise, such as 1:cat, 2:bat, 3:hat, etc, must be perormed manually, because the classifier and humans do not have a shared understanding of what e.g. "cat", "bat" and "hat" mean. The classifier only knows 1,2,3... etc, the human knows that "1 means cat". How would this lack of shared context be resolved between an adult and a baby, so that the adult could provide "multi-class labels"? ___________ [1] Sorry that I don't have any references for all this handy- I can try to dig some up if you're interested, but you could start by reading the wikipedia page on Language Identification in the Limit, which is about the famous result from inductive inference I mention (also known as Gold's result from the man who derived it): https://en.wikipedia.org/wiki/Language_identification_in_the_limit https://en.wikipedia.org/wiki/Language_identification_in_the...
- Tainnor 7y ago
  By now, the Chomskian approach to linguistics is not unchallenged anymore and there is some doubt on whether the "poverty of stimulus" argument holds any water (see e.g. [1]). IMHO, modern cognitive science based approaches (such as by Tomasello and others) have a better chance of explaining how language is acquired than the hypothesising of the 70s. I don't have time now to go into more references, but the question is far from settled. [1] https://scottthornbury.wordpress.com/2015/06/07/p-is-for-poverty-of-the-stimulus/ https://scottthornbury.wordpress.com/2015/06/07/p-is-for-pov...

ben_w 7y ago

The things you’re calling labels are themselves learned data, as we don’t start off with an innate knowledge of language. Whether that distinction is important or not, I don’t know enough brain science to guess.

onefuncman 7y ago

I agree with you -- we have little idea how much labeled information is encoded in the genome either. I rather like to think of the genome as the most well trained model of life we have access to.

richardw 7y ago

I show my 19 month old daughter like three cartoon drawings of owls and she recognises a live one at the bird park instantly, unprompted. We have a way to go.

taneq 7y ago
I believe cartoons are our equivalent of adversarial images. They typically look nothing like (photos of) their namesake and yet we recognise them usually without prompting.
- EliRivers 7y ago
  It is my understanding (although I sure don't have any evidence on me) that cartoons and such (at least, the ones where we haven't simply learned that this cartoon means this animal) work by being a picture of what we remember about an animal. Akin to a caricature; the cartoon contains the most salient features. It doesn't work by looking like the actual animal; it works by reacting with how we remember the animal.
  - taneq 7y ago
    Isn't that kind of the same thing? Adversarial examples work by matching what the neural net 'remembers' about the target classification, rather than being a picture of a thing in that class. Neural nets just find different features salient . I've wondered in the past if we could use black box adversarial methods with Mechanical Turk to generate adversarial examples that work on humans. Maybe they'd end up looking like cartoons? (Also agreed, some cartoon animals are just informed likeness - for instance Goofy doesn't look anything like a dog, at least to me.)
  - YeGoblynQueenne 7y ago
    >> Akin to a caricature; the cartoon contains the most salient features The question is - how do we know what are the salient features? How do we figure out that if we make _this_ drawing, it will "remind of" an owl, and if we make _that_ drawing it will "remind of" a dog (or not, as the case may be)? I mean, if we knew that, how humans extract salient or relevant features from their environment, we'd be way ahead on the path to AI.

tsimionescu 7y ago

There may be some kind of labeling encoded in genes. One thing that it is safe to assume is genetically encoded somehow is that sounds made by your parents/humans around you is worth repeating while other sounds are not. However, past that, the actual sounds themselves, and any association to meaning, are pretty far from tagged data sets. Stuff like the specifics of language (e.g. that a dog is called 'dog') are definitely learned, and children learn them with typically only a handful of stimuli, often a single one. For contrast, imagine training a model with raw sound data tagged only with "speech" vs "not speech" (and probably only a few thousand data points at that) and I will be amazed if it can recognize a single word. And babies don't just learn words, they learn their association to things they see and hear, and grammar, and abstract thought. Do note that it is very likely that human brains can learn all that because they have some good heuristics built in. We definitely know some stuff is "hardware" - object recognition, basic mechanics, recognizing human faces and expression, and others. We are pretty sure higher level stuff is also built in - universal grammar, basic logic, some ability to simulate behavior seen/heard in other humans. This specialized hardware was also most likely learned, but over much, much greater periods of time, through evolution over hundreds of millions of years (since even extremely old animals are capable of picking out objects in the environment, approximating their speed etc).

taneq 7y ago
> One thing that it is safe to assume is genetically encoded somehow is that sounds made by your parents/humans around you is worth repeating while other sounds are not. Well, these sounds come with a face attached and we know babies are hardwired to pay attention to faces.
- tsimionescu 7y ago
  That may well be the mechanism behind this. I was talking in very general terms, not a specific 'imitate humans' structure in the brain.
Quarrelsome 7y ago
> One thing that it is safe to assume is genetically encoded somehow is that sounds made by your parents/humans around you is worth repeating while other sounds are not. I don't see how that's safe to assume at all. What one could assume is the level of familiarity and comfort (sight, smell, touch) might be somewhat genetic and gives such inputs precedence. OR it might just be that those sources of information are engaging and animated. > Do note that it is very likely that human brains can learn all that because they have some good heuristics built in. Nor do I see this assumption having any weight, many of the heuristics we take for granted were hard fought, its just so long ago that we've forgotten the fight. Lets not forget how "little" our species gets over the first few YEARS of child development. If your child can move their body, just about walk and talk a little at TWO WHOLE YEARS in, they're an achiever.

faceplanted 7y ago

Humans don't _just_ learn to recognise the things they see though, they have complex mental models of the objects and things about them they can access by choice as well as make hypotheses about new things that they can immediately test, humans don't get labelled photos of cats, but they see cats in 3D and can interact with them and use spatial reasoning and walk around them to completely separate that cat from the background behind them.

YeGoblynQueenne 7y ago

I'm curious to know what you mean by "labelled information". I'm guessing that what you are calling "labelled information" is various forms of encouragement or discouragement that could be considered positively and negatively "labelled" examples. If that is the case, linguistics research back in the '70s found that infants get almost no negative examples of, in particular, language. For example, a parent will not correct a child by saying, "no you can't say 'eated' because then you could also say 'sitted'". Instead they will correct by saying "no, you should say 'ate'" etc. That is important because there was a famous proof in inductive inference (the precursor to computational learning theory) that languages higher in the Chomsky hierarchy than regular languages cannot be learned from positive examples alone. And yet, babies eventually learn to speak human languages, which are assumed to be at least context-free. Chomsky used these findings to support his claim of a "universal grammar" or innate language endowment [1]. If you are talking about multi-class labelling, that's even harder to imagine. In machine learning, a multi-class classifier will map inputs to some set of categorical labels (i.e. a set of integers) but the mapping from those labels to concepts that a human would recognise, such as 1:cat, 2:bat, 3:hat, etc, must be perormed manually, because the classifier and humans do not have a shared understanding of what e.g. "cat", "bat" and "hat" mean. The classifier only knows 1,2,3... etc, the human knows that "1 means cat". How would this lack of shared context be resolved between an adult and a baby, so that the adult could provide "multi-class labels"? ___________ [1] Sorry that I don't have any references for all this handy- I can try to dig some up if you're interested, but you could start by reading the wikipedia page on Language Identification in the Limit, which is about the famous result from inductive inference I mention (also known as Gold's result from the man who derived it): https://en.wikipedia.org/wiki/Language_identification_in_the_limit https://en.wikipedia.org/wiki/Language_identification_in_the...

Tainnor 7y ago
By now, the Chomskian approach to linguistics is not unchallenged anymore and there is some doubt on whether the "poverty of stimulus" argument holds any water (see e.g. [1]). IMHO, modern cognitive science based approaches (such as by Tomasello and others) have a better chance of explaining how language is acquired than the hypothesising of the 70s. I don't have time now to go into more references, but the question is far from settled. [1] https://scottthornbury.wordpress.com/2015/06/07/p-is-for-poverty-of-the-stimulus/ https://scottthornbury.wordpress.com/2015/06/07/p-is-for-pov...