6 ms·

This is a topsy turvy argument, ignoring the abject failure of the old school AI approaches in the Chomsky and MIT tradition and the stunning success of ML stat

This is a topsy turvy argument, ignoring the abject failure of the old school AI approaches in the Chomsky and MIT tradition and the stunning success of ML statistical approaches. One wonders how little background research the NYT did before they chose these authors.

goatlover 8y ago
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
- 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.)
YeGoblynQueenne 8y ago
What "abject failure" is that? GOFAI fell out of favour because its funding was cut as a result of political decisions by bureaucrats that did not understand the field, not because the field itself decided it had failed in its goals. Here's a good read to give some background: Avoiding another AI winter - James Hendler https://web.archive.org/web/20120212012656/http://csdl2.computer.org/comp/mags/ex/2008/02/mex2008020002.pdf https://web.archive.org/web/20120212012656/http://csdl2.comp... And a little on the Lighthill report from wikipedia: https://en.wikipedia.org/wiki/Lighthill_report https://en.wikipedia.org/wiki/Lighthill_report See the links to youtube videos of the televised debate (feature Jon McCarthy and Donald Michie and, er, another gentleman) in the external links section: https://en.wikipedia.org/wiki/Lighthill_report#1973_BBC_%22Controversy%22_debate https://en.wikipedia.org/wiki/Lighthill_report#1973_BBC_%22C... GOFAI - logic-based AI- worked just fine. Its branch of research was cut short before we could see where it would truly lead. Of course modern reserachers will not tell you that- it's their job to promote their ideas and your job to know the history of AI before 2012.
white-flame 8y ago
Old school AI had stunning successes of offloading and combining human situational knowledge into automated systems (under certain conditions). New school AI has stunning successes of association and prediction on data sets (under certain conditions). Neither seem to point to a grand unified theory of human-level intelligence.
usrusr 8y ago
The symbolic AI tribe failed to reach what that the ML one never attempted. All fundamental differences aside they are hardly competing at all (except for eyeballs and grants). ML has made fantastic progress in recent years, but it could still be argued that the single most important discovery in ML has been the discovery of application domains with extremely little cost of being wrong.
- angry_octet 8y ago
  No so much a discovery, but previous AI researchers defined what was important as what their tools were suited for. But a good counter example is speech. They tried very very hard for many years, with quite limited success. As it turned out, decomposition into semantics via different layers isn't so successful. http://oxygen.csail.mit.edu/Speech.html http://oxygen.csail.mit.edu/Speech.html [Circa 2001] As for AGI, there is no supporting basis for the contention that it can only be achieved using semantic approaches, or that ML isn't a productive path.

goatlover 8y ago

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
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.)

YeGoblynQueenne 8y ago

What "abject failure" is that? GOFAI fell out of favour because its funding was cut as a result of political decisions by bureaucrats that did not understand the field, not because the field itself decided it had failed in its goals. Here's a good read to give some background: Avoiding another AI winter - James Hendler https://web.archive.org/web/20120212012656/http://csdl2.computer.org/comp/mags/ex/2008/02/mex2008020002.pdf https://web.archive.org/web/20120212012656/http://csdl2.comp... And a little on the Lighthill report from wikipedia: https://en.wikipedia.org/wiki/Lighthill_report https://en.wikipedia.org/wiki/Lighthill_report See the links to youtube videos of the televised debate (feature Jon McCarthy and Donald Michie and, er, another gentleman) in the external links section: https://en.wikipedia.org/wiki/Lighthill_report#1973_BBC_%22Controversy%22_debate https://en.wikipedia.org/wiki/Lighthill_report#1973_BBC_%22C... GOFAI - logic-based AI- worked just fine. Its branch of research was cut short before we could see where it would truly lead. Of course modern reserachers will not tell you that- it's their job to promote their ideas and your job to know the history of AI before 2012.

white-flame 8y ago

Old school AI had stunning successes of offloading and combining human situational knowledge into automated systems (under certain conditions). New school AI has stunning successes of association and prediction on data sets (under certain conditions). Neither seem to point to a grand unified theory of human-level intelligence.

usrusr 8y ago

The symbolic AI tribe failed to reach what that the ML one never attempted. All fundamental differences aside they are hardly competing at all (except for eyeballs and grants). ML has made fantastic progress in recent years, but it could still be argued that the single most important discovery in ML has been the discovery of application domains with extremely little cost of being wrong.

angry_octet 8y ago
No so much a discovery, but previous AI researchers defined what was important as what their tools were suited for. But a good counter example is speech. They tried very very hard for many years, with quite limited success. As it turned out, decomposition into semantics via different layers isn't so successful. http://oxygen.csail.mit.edu/Speech.html http://oxygen.csail.mit.edu/Speech.html [Circa 2001] As for AGI, there is no supporting basis for the contention that it can only be achieved using semantic approaches, or that ML isn't a productive path.