9 ms·

Stunning success in creating Hal 9000s or image classifiers? Because traditional AI was aiming for generalized intelligence and not just success in narrow domai

Stunning success in creating Hal 9000s or image classifiers? Because traditional AI was aiming for generalized intelligence and not just success in narrow domains. Minsky's critique of modern AI (or rather the entire history of the field so far) is the lack of progress on common sense. So an ML model can learn to recognize cat pictures better than humans. Great, but what does it know about cats beyond being able to correctly pick out pictures with them in it?

dantheman 8y ago
Being able to recognize and learn from the environment can be seen as a the first step to learning common sense. Imagine learning common sense without any way of getting or making sense of sensory input. Also being able to perform actions in the real world and seeing results. To be able to learn on it's own seems to be the only way to learn and develop.
ben_w 8y ago
I strongly dislike the phrase “common sense”. I have yet to encounter even one example of “common sense” which accurately represents the world. “Things fall when you let go of them” «Except flying things, things already on the ground, and probably other examples too.» “But that’s not what I meant, it’s common sense that’s not what I meant.” «Then it’s a tautology, things that fall when you let go of them, fall when you let go of them. The real rule (or rather, good enough at our scale) is Newtonian mechanics.» Or similar conversations about being in two places at once. When it comes to cats, what does common sense tell you — What cats do? How cats respond to things? How they interact physically with their environment? That’s all statistically learnable.
- YeGoblynQueenne 8y ago
  >> What cats do? How cats respond to things? How they interact physically with their environment? That’s all statistically learnable. Not at all. At least not in practice and certainly not for any significant subset of the things that "cats do" or the ways they "respond to things". You can certainly collect some examples of all the behaviours you describe above, but there are so many of them you are never going to have enough to model a cat's behaviour with anything like significant accuracy.
  - red75prime 8y ago
    DGANs learn to generate realistic faces. Do you think it's impossible, because there are so many different faces?
    - YeGoblynQueenne 8y ago
      The generation is limited to features very close to the ones seen by the network in examples - i.e., to a limited domain. Here is an explanation of the limited ability of deep networks to generalise beyond their dataset, by Francois Cholet (maintainer of Keras): https://blog.keras.io/the-limitations-of-deep-learning.html https://blog.keras.io/the-limitations-of-deep-learning.html
      - red75prime 8y ago
        (Shrug) NNs are universal function approximators. A critique of existing approaches is important, but it will not last.
  - ben_w 8y ago
    > never going to have enough “Hey Siri show me cat videos” “Here are some videos I found of ‘cat’ on the web:” «Link to YouTube page saying “About 79,900,000 results”»
    - YeGoblynQueenne 8y ago
      The parent discusses: >> What cats do? How cats respond to things? How they interact physically with their environment? Not "how does a cat look on youtube".
      - ben_w 8y ago
        You’re the second person who’s tried to correct my interpretation of my own posts this week. What’s that quote about losing grandparents? :) Anyhow, my point was that there is plenty of data about the sort of cat behaviour which humans collectively find notable.
evrydayhustling 8y ago
One person's unmet dream isn't someone else's (or a field's) failure. Anyone who thinks their definition of AGI won't be met by current techniques is free to develop an alternative approach. The second it shows signs of being able to do something useful, it will attract funding and attention. (Usually, the folks doing the OPs kind of heckling are posturing to get attention and funding before demonstrating something useful.)

dantheman 8y ago

Being able to recognize and learn from the environment can be seen as a the first step to learning common sense. Imagine learning common sense without any way of getting or making sense of sensory input. Also being able to perform actions in the real world and seeing results. To be able to learn on it's own seems to be the only way to learn and develop.

ben_w 8y ago

I strongly dislike the phrase “common sense”. I have yet to encounter even one example of “common sense” which accurately represents the world. “Things fall when you let go of them” «Except flying things, things already on the ground, and probably other examples too.» “But that’s not what I meant, it’s common sense that’s not what I meant.” «Then it’s a tautology, things that fall when you let go of them, fall when you let go of them. The real rule (or rather, good enough at our scale) is Newtonian mechanics.» Or similar conversations about being in two places at once. When it comes to cats, what does common sense tell you — What cats do? How cats respond to things? How they interact physically with their environment? That’s all statistically learnable.

YeGoblynQueenne 8y ago
>> What cats do? How cats respond to things? How they interact physically with their environment? That’s all statistically learnable. Not at all. At least not in practice and certainly not for any significant subset of the things that "cats do" or the ways they "respond to things". You can certainly collect some examples of all the behaviours you describe above, but there are so many of them you are never going to have enough to model a cat's behaviour with anything like significant accuracy.
- red75prime 8y ago
  DGANs learn to generate realistic faces. Do you think it's impossible, because there are so many different faces?
  - YeGoblynQueenne 8y ago
    The generation is limited to features very close to the ones seen by the network in examples - i.e., to a limited domain. Here is an explanation of the limited ability of deep networks to generalise beyond their dataset, by Francois Cholet (maintainer of Keras): https://blog.keras.io/the-limitations-of-deep-learning.html https://blog.keras.io/the-limitations-of-deep-learning.html
    - red75prime 8y ago
      (Shrug) NNs are universal function approximators. A critique of existing approaches is important, but it will not last.
- ben_w 8y ago
  > never going to have enough “Hey Siri show me cat videos” “Here are some videos I found of ‘cat’ on the web:” «Link to YouTube page saying “About 79,900,000 results”»
  - YeGoblynQueenne 8y ago
    The parent discusses: >> What cats do? How cats respond to things? How they interact physically with their environment? Not "how does a cat look on youtube".
    - ben_w 8y ago
      You’re the second person who’s tried to correct my interpretation of my own posts this week. What’s that quote about losing grandparents? :) Anyhow, my point was that there is plenty of data about the sort of cat behaviour which humans collectively find notable.

evrydayhustling 8y ago

One person's unmet dream isn't someone else's (or a field's) failure. Anyone who thinks their definition of AGI won't be met by current techniques is free to develop an alternative approach. The second it shows signs of being able to do something useful, it will attract funding and attention. (Usually, the folks doing the OPs kind of heckling are posturing to get attention and funding before demonstrating something useful.)