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Advances in storage and compute have led to a disturbing fetishization of machine learning. While the modern Machine Learning Movement makes sense in a histori

Advances in storage and compute have led to a disturbing fetishization of machine learning. While the modern Machine Learning Movement makes sense in a historical context and is a reasonable reaction to the disappointing returns from symbolic inference during the early days of AI research, it is terrifying that the research community is satisfied to rely on big data and statistical methods to carry us forward. Few among us recognize the need to prioritize the study of the human brain. Even fewer are placing their bets on intelligent computer systems seeded with neurologically-inspired designs. Vicarious gets it. How long before others see the writing on the wall? Now is the time to stop reacting. Now is the time to consider the field in a broad context and develop a balanced, holistic approach. Consider this a wake-up call. http://blog.perfmode.com/the-noml-movement/ http://blog.perfmode.com/the-noml-movement/

unlikelymordant 13y ago
I'm not sure I understand. "Few among us recognize the need to prioritize the study of the human brain", a great deal of the state of the art machine learning results are based on deep learning, which are algorithms that are "neurologically inspired" as you would put it. You seem to have a problem with big data and statistical methods, but one of the main deep learning algorithms, RBMs, are statistical methods. Also, could you expand on what a "balanced, holistic approach" to machine learning is?
- perfmode 13y ago
  "Even though an amplifier and a computer are both made of transistors, they have almost nothing else in common. In the same way, a real brain and a three-row neural network are built with neurons, but have almost nothing else in common." -- Jeff Hawkins (On Intelligence) One example is that most NNs neglect the time domain. A balanced approach recognizes the importance of learning from data, but does not _rely_ on big data. A holistic approach entails a close examination of biological learning systems.
  - varjag 13y ago
    As a counterpoint, nearly all human technical advances (flight, propulsion, energy, computation) did not entail examination of biological systems. And ANNs in particular share a host of problems with statistical techniques: black-boxish behavior with lack of human-accessible state introspection and poor tractability.
    - perfmode 13y ago
      "On the basis of observation, Wilbur concluded that birds changed the angle of the ends of their wings to make their bodies roll right or left.[30] The brothers decided this would also be a good way for a flying machine to turn—to "bank" or "lean" into the turn just like a bird—and just like a person riding a bicycle, an experience with which they were thoroughly familiar." https://en.wikipedia.org/wiki/Wright_brothers https://en.wikipedia.org/wiki/Wright_brothers
      - varjag 13y ago
        This doesn't however make airplanes aerodynamically anywhere similar to bird flight. Besides, Wright's method of wing warping was soon discarded as structurally unsound and is seldom used now.
    - chongli 13y ago
      black-boxish behavior with lack of human-accessible state introspection and poor tractability. I have a philosophical question in response to this. Is it even possible to have intelligence without it being a black box? Are people willing to call something intelligent if they completely understand how it works?
      - eli_gottlieb 13y ago
        >Is it even possible to have intelligence without it being a black box? That depends. Do you believe in Cartesian dualism, or atomic monism? If you believe in a monist universe where minds don't reside on some other plane of existence, then plainly a mind must be explicable to a sufficiently smart other mind, because after all so is everything else.
eli_gottlieb 13y ago
Oh, you just walked into a bloody minefield, mate. Numenta has pulled crap like this before. We know patents may be pending, but you don't have the epistemological right to go blowing the Great Shofar for the invention of True AI with a link to your company's website and a fancy buzzword about neural or cortical this-and-that on the front page. We need to see some published research, or you need to take over the world. Preferably the former. Until then, stop making claims unless you want the rest of us to consider you a crackpot and a braggart.
- aufreak3 13y ago
  Wasn't Dileep George part of Numenta?
  - eli_gottlieb 13y ago
    I checked and yes. Which bugs me even more. Come on, guys, put up or shut up. If you've made the kind of advance in machine learning that entitles you to talk about human-level cognition, take out a patent and then publish some freaking papers. Or take over the world. There are accepted ways of proving claims like this, and founding company after company without releasing a product or publishing research isn't one of them.
    - aufreak3 13y ago
      While that criticism is valid, it is also possible for them to think that the details of it be better kept a trade secret than be revealed to the public through either a patent document or a detailed enough paper. (Just giving them the benefit of doubt.) If you look at Gary Drescher's work published in "Made up minds" [1], it seems possible that an AI that can influence the world can more efficiently arrive at intelligence than one that simply observes it - i.e. one that can learn by performing experiments rather than only looking at data coming out of everywhere. So there does seem to be scope for approaches to AI that aren't in the "data trumps everything" gang. [1] http://books.google.co.in/books/about/Made_up_Minds.html?id=jYsEzeKHLNUC&redir_esc=y http://books.google.co.in/books/about/Made_up_Minds.html?id=...

unlikelymordant 13y ago

I'm not sure I understand. "Few among us recognize the need to prioritize the study of the human brain", a great deal of the state of the art machine learning results are based on deep learning, which are algorithms that are "neurologically inspired" as you would put it. You seem to have a problem with big data and statistical methods, but one of the main deep learning algorithms, RBMs, are statistical methods. Also, could you expand on what a "balanced, holistic approach" to machine learning is?

perfmode 13y ago
"Even though an amplifier and a computer are both made of transistors, they have almost nothing else in common. In the same way, a real brain and a three-row neural network are built with neurons, but have almost nothing else in common." -- Jeff Hawkins (On Intelligence) One example is that most NNs neglect the time domain. A balanced approach recognizes the importance of learning from data, but does not _rely_ on big data. A holistic approach entails a close examination of biological learning systems.
- varjag 13y ago
  As a counterpoint, nearly all human technical advances (flight, propulsion, energy, computation) did not entail examination of biological systems. And ANNs in particular share a host of problems with statistical techniques: black-boxish behavior with lack of human-accessible state introspection and poor tractability.
  - perfmode 13y ago
    "On the basis of observation, Wilbur concluded that birds changed the angle of the ends of their wings to make their bodies roll right or left.[30] The brothers decided this would also be a good way for a flying machine to turn—to "bank" or "lean" into the turn just like a bird—and just like a person riding a bicycle, an experience with which they were thoroughly familiar." https://en.wikipedia.org/wiki/Wright_brothers https://en.wikipedia.org/wiki/Wright_brothers
    - varjag 13y ago
      This doesn't however make airplanes aerodynamically anywhere similar to bird flight. Besides, Wright's method of wing warping was soon discarded as structurally unsound and is seldom used now.
  - chongli 13y ago
    black-boxish behavior with lack of human-accessible state introspection and poor tractability. I have a philosophical question in response to this. Is it even possible to have intelligence without it being a black box? Are people willing to call something intelligent if they completely understand how it works?
    - eli_gottlieb 13y ago
      >Is it even possible to have intelligence without it being a black box? That depends. Do you believe in Cartesian dualism, or atomic monism? If you believe in a monist universe where minds don't reside on some other plane of existence, then plainly a mind must be explicable to a sufficiently smart other mind, because after all so is everything else.

eli_gottlieb 13y ago

Oh, you just walked into a bloody minefield, mate. Numenta has pulled crap like this before. We know patents may be pending, but you don't have the epistemological right to go blowing the Great Shofar for the invention of True AI with a link to your company's website and a fancy buzzword about neural or cortical this-and-that on the front page. We need to see some published research, or you need to take over the world. Preferably the former. Until then, stop making claims unless you want the rest of us to consider you a crackpot and a braggart.

aufreak3 13y ago
Wasn't Dileep George part of Numenta?
- eli_gottlieb 13y ago
  I checked and yes. Which bugs me even more. Come on, guys, put up or shut up. If you've made the kind of advance in machine learning that entitles you to talk about human-level cognition, take out a patent and then publish some freaking papers. Or take over the world. There are accepted ways of proving claims like this, and founding company after company without releasing a product or publishing research isn't one of them.
  - aufreak3 13y ago
    While that criticism is valid, it is also possible for them to think that the details of it be better kept a trade secret than be revealed to the public through either a patent document or a detailed enough paper. (Just giving them the benefit of doubt.) If you look at Gary Drescher's work published in "Made up minds" [1], it seems possible that an AI that can influence the world can more efficiently arrive at intelligence than one that simply observes it - i.e. one that can learn by performing experiments rather than only looking at data coming out of everywhere. So there does seem to be scope for approaches to AI that aren't in the "data trumps everything" gang. [1] http://books.google.co.in/books/about/Made_up_Minds.html?id=jYsEzeKHLNUC&redir_esc=y http://books.google.co.in/books/about/Made_up_Minds.html?id=...