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Jeff Hawkins gets shit on a lot in ML because his theories haven’t produced models with results as good as the mainstream approach, but I’m glad they keep worki

by Cacti 9y ago

Jeff Hawkins gets shit on a lot in ML because his theories haven’t produced models with results as good as the mainstream approach, but I’m glad they keep working at it and keep coming up with interesting ideas. Too much of ML these days is about some NN model that does 0.n% better than SOTA on some specific task. Then you change one tiny parameter and he entire thing breaks, and it turns out we didn’t understand why it was working at all.

daveguy 9y ago
I do think his reasoning and focus of what's wrong with ML today is good. Online learning, sequence learning and "motor" feedback are all critical. Unfortunately he came up with this HTM and appears to be trying to cram all advances into the same framework rather than stepping back and thinking about how those critical features could be implemented differently. Also as far as I know hebbian learning (like HTM) has achieved ~90% accuracy on MNIST while SOA is above 99% accuracy. So it's not a small gap between HTM and state of the art. If HTM was near 98% it would be impressive (learning is faster and transfer better). I think alternatives or hybrids are necessary, but he seems stuck on his pet project. That said, the principles and "what's important for intelligence" I think he is right on target and articulates those aspects well.
chplushsieh 9y ago
Jeff Hawkins gets shit on a lot in ML because his theories haven’t produced models with results as good as the mainstream approach, but I’m glad they keep working at it and keep coming up with interesting ideas. I just ran across a paper [1] from Numenta on Time Series Anomaly Detection using HTM last night which provide a benchmark[2] with some existing approachs. (But it seems to me there is no NN based approach in them. ) [1] Unsupervised real-time anomaly detection for streaming data http://www.sciencedirect.com/science/article/pii/S0925231217309864 http://www.sciencedirect.com/science/article/pii/S0925231217... [2] https://github.com/numenta/NAB https://github.com/numenta/NAB
davesque 9y ago
From a mostly outside point of view, I've wondered a lot about this. It seems a lot of ML/DL research that goes on nowadays amounts to educated guessing and tinkering i.e. "Based on our notion of what seemed right, we tried this other configuration of layers and/or activation functions and look; it worked". I imagine at some point that the kind of research discussed in the linked post will begin to pan out more and more and will eventually lead to another period of rapid progress like that which followed Hinton's work on DL.
bluetwo 9y ago
Show me how HTM solves any multi-step process. Tic-tac-toe. Anything.
rhombocombus 9y ago
Cargo cult programming abounds in a lot of the ML stuff I read. It's complex and complicated, and I think it happens because a lot of the research happens in corporate environments with top-down pressure. I have worked in computational modeling of sensory motor systems, and it is really challenging work. This is way more interesting than banal model performance bragging!
- simonster 9y ago
  As a neuroscientist who just started doing ML research, I would call this paper cargo cult programming. If you cobble together a hodgepodge of ideas from neuroscience and build a network to accomplish some trivial task with no baseline to compare it to, I find it really difficult to take anything away from that. Ignoring the cortical column aspect, I'm not particularly convinced that Hawkins's model is a better approximation of biology than a typical deep neural network, just different (and likely far less capable, if you were to apply it to a challenging task). Why not start with a network that we know works and make a biologically-inspired change, and then see if that improves performance on a well-studied problem? If it does, then you have 1) an improvement on the previous network and 2) weak evidence that your idea of how the brain works may be right, if we assume that the brain is a highly optimized information processing device.
eli_gottlieb 9y ago
>Jeff Hawkins gets shit on a lot in ML because his theories haven’t produced models with results as good as the mainstream approach, but I’m glad they keep working at it and keep coming up with interesting ideas. And also because Numenta's work isn't good, empirically-checkable neuroscience either.
- deleted 9y ago
  [deleted]
- TrinaryWorksToo 9y ago
  What part of Numenta's model is wrong? I certainly see some guesses and simplifications in their model, especially in poorly researched areas, but I figure that's the cost of trying to get things to work.
  - amthewiz 9y ago
    Numenta's HTM theory seems functionally similar to variants of N-gram models (https://en.wikipedia.org/wiki/N-gram https://en.wikipedia.org/wiki/N-gram), where the model is able to predict future steps based on learned conditional probabilities. Maybe that's why it hasn't worked exceptionally well?
- ewjordan 9y ago
  Not sure whether that was a jab at Numenta or not, but I do think the combination of these two comments cuts to the heart of the PR problem for Numenta: they're neither trying to do 0.1% better on the classic perception benchmarks than other algorithms do, nor trying to publish accurate, testable descriptions of the true details of meatspace neuroscience. To me, exploring alternative network architectures and algorithms seems an extremely worthwhile goal even if it's only loosely tethered to actual biology, but from a PR perspective they really need to be better about priming the conversation if they want people to care. Bad (neuroscience-focused): "We're doing a lot of research on neuroscience, and finding some really interesting stuff, so we built a model that doesn't exactly match the way the brain works but is still interesting. No, we haven't tried to make it work to classify ImageNet test cases, that's not our goal. But look, it's closer to biology, and we have working code that we're playing with!" Better (ML-focused): "We're developing a novel neural network architecture that performs online unsupervised learning using only local update rules. Though it performs competently at classic benchmarks X, Y, and Z when a small WTA layer is thrown on top, it can also tackle problems A, B, and C that classical deep learning networks can't make any progress on." To be fair, I'm not even sure if Numenta's networks could perform competently at any classic benchmarks (I'm guessing that if they could, it would take some work to get them to do so), and I have no idea what new problems it could work on. But they really do need to reframe the conversation and emphasize that sort of innovation if they want to be taken more seriously - focusing on neuroscience underpinnings is not a great move if they're not engaged in research that can actually win over neuroscientists, and just pointing out that they're focusing on those things is not a way to win over industry ML folks if they don't have any results to point at.
tziki 9y ago
I thought he gets shit because he patented his approach instead of making it available for everyone (please do correct me if I'm wrong).
- TrinaryWorksToo 9y ago
  It's GPLv3: https://github.com/numenta https://github.com/numenta
  - taneq 9y ago
    That's the copyright license, which is orthogonal to patent issues. Edit: Apparently not, GPL v3 has clauses regarding patent grants in it.
    - TrinaryWorksToo 9y ago
      https://www.gnu.org/licenses/rms-why-gplv3.en.html https://www.gnu.org/licenses/rms-why-gplv3.en.html It looks like it includes some patent information.
      - taneq 9y ago
        Huh, I didn't realise that. That 'patents' clause is pretty powerful - if my (very quick) reading is right, any time you contribute to a GPL3 project, any patents that you own which would apply to that project are basically set free for anyone to use. Cool for FOSS advocates but I can see why a lot of businesses are wary of anything using GPL3.
        belorn 9y ago
        GPLv3 initial draft was a copy of the clause from the apache license: "each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work," The final gplv3 text says: "Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version." As can be seen, they are very similar. The key difference between apache, a fairly popular license with businesses, and GPLv3 in the realm of patents, is the Novell-Microsoft pact clause. Its not about patents that you own, but rather patents for which you sub license from others. "If you convey a covered work, knowingly relying on a patent license,... you must ether ... arrange to deprive yourself of the benefit of the patent license for this particular work or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients." This clause is the main crux of the issue. Large companies with large lawyer departments has complex patent deals with competitors, and deprive yourself of the patent would mean to open up yourself to being sued, and the other option would mean renegotiate with the competitor to grant the patent to all and everyone.

by Cacti 9y ago

Jeff Hawkins gets shit on a lot in ML because his theories haven’t produced models with results as good as the mainstream approach, but I’m glad they keep working at it and keep coming up with interesting ideas. Too much of ML these days is about some NN model that does 0.n% better than SOTA on some specific task. Then you change one tiny parameter and he entire thing breaks, and it turns out we didn’t understand why it was working at all.

daveguy 9y ago

I do think his reasoning and focus of what's wrong with ML today is good. Online learning, sequence learning and "motor" feedback are all critical. Unfortunately he came up with this HTM and appears to be trying to cram all advances into the same framework rather than stepping back and thinking about how those critical features could be implemented differently. Also as far as I know hebbian learning (like HTM) has achieved ~90% accuracy on MNIST while SOA is above 99% accuracy. So it's not a small gap between HTM and state of the art. If HTM was near 98% it would be impressive (learning is faster and transfer better). I think alternatives or hybrids are necessary, but he seems stuck on his pet project. That said, the principles and "what's important for intelligence" I think he is right on target and articulates those aspects well.

chplushsieh 9y ago

Jeff Hawkins gets shit on a lot in ML because his theories haven’t produced models with results as good as the mainstream approach, but I’m glad they keep working at it and keep coming up with interesting ideas. I just ran across a paper [1] from Numenta on Time Series Anomaly Detection using HTM last night which provide a benchmark[2] with some existing approachs. (But it seems to me there is no NN based approach in them. ) [1] Unsupervised real-time anomaly detection for streaming data http://www.sciencedirect.com/science/article/pii/S0925231217309864 http://www.sciencedirect.com/science/article/pii/S0925231217... [2] https://github.com/numenta/NAB https://github.com/numenta/NAB

davesque 9y ago

From a mostly outside point of view, I've wondered a lot about this. It seems a lot of ML/DL research that goes on nowadays amounts to educated guessing and tinkering i.e. "Based on our notion of what seemed right, we tried this other configuration of layers and/or activation functions and look; it worked". I imagine at some point that the kind of research discussed in the linked post will begin to pan out more and more and will eventually lead to another period of rapid progress like that which followed Hinton's work on DL.

bluetwo 9y ago

Show me how HTM solves any multi-step process. Tic-tac-toe. Anything.

rhombocombus 9y ago

Cargo cult programming abounds in a lot of the ML stuff I read. It's complex and complicated, and I think it happens because a lot of the research happens in corporate environments with top-down pressure. I have worked in computational modeling of sensory motor systems, and it is really challenging work. This is way more interesting than banal model performance bragging!

simonster 9y ago
As a neuroscientist who just started doing ML research, I would call this paper cargo cult programming. If you cobble together a hodgepodge of ideas from neuroscience and build a network to accomplish some trivial task with no baseline to compare it to, I find it really difficult to take anything away from that. Ignoring the cortical column aspect, I'm not particularly convinced that Hawkins's model is a better approximation of biology than a typical deep neural network, just different (and likely far less capable, if you were to apply it to a challenging task). Why not start with a network that we know works and make a biologically-inspired change, and then see if that improves performance on a well-studied problem? If it does, then you have 1) an improvement on the previous network and 2) weak evidence that your idea of how the brain works may be right, if we assume that the brain is a highly optimized information processing device.

eli_gottlieb 9y ago

>Jeff Hawkins gets shit on a lot in ML because his theories haven’t produced models with results as good as the mainstream approach, but I’m glad they keep working at it and keep coming up with interesting ideas. And also because Numenta's work isn't good, empirically-checkable neuroscience either.

deleted 9y ago
[deleted]
TrinaryWorksToo 9y ago
What part of Numenta's model is wrong? I certainly see some guesses and simplifications in their model, especially in poorly researched areas, but I figure that's the cost of trying to get things to work.
- amthewiz 9y ago
  Numenta's HTM theory seems functionally similar to variants of N-gram models (https://en.wikipedia.org/wiki/N-gram https://en.wikipedia.org/wiki/N-gram), where the model is able to predict future steps based on learned conditional probabilities. Maybe that's why it hasn't worked exceptionally well?
ewjordan 9y ago
Not sure whether that was a jab at Numenta or not, but I do think the combination of these two comments cuts to the heart of the PR problem for Numenta: they're neither trying to do 0.1% better on the classic perception benchmarks than other algorithms do, nor trying to publish accurate, testable descriptions of the true details of meatspace neuroscience. To me, exploring alternative network architectures and algorithms seems an extremely worthwhile goal even if it's only loosely tethered to actual biology, but from a PR perspective they really need to be better about priming the conversation if they want people to care. Bad (neuroscience-focused): "We're doing a lot of research on neuroscience, and finding some really interesting stuff, so we built a model that doesn't exactly match the way the brain works but is still interesting. No, we haven't tried to make it work to classify ImageNet test cases, that's not our goal. But look, it's closer to biology, and we have working code that we're playing with!" Better (ML-focused): "We're developing a novel neural network architecture that performs online unsupervised learning using only local update rules. Though it performs competently at classic benchmarks X, Y, and Z when a small WTA layer is thrown on top, it can also tackle problems A, B, and C that classical deep learning networks can't make any progress on." To be fair, I'm not even sure if Numenta's networks could perform competently at any classic benchmarks (I'm guessing that if they could, it would take some work to get them to do so), and I have no idea what new problems it could work on. But they really do need to reframe the conversation and emphasize that sort of innovation if they want to be taken more seriously - focusing on neuroscience underpinnings is not a great move if they're not engaged in research that can actually win over neuroscientists, and just pointing out that they're focusing on those things is not a way to win over industry ML folks if they don't have any results to point at.

tziki 9y ago

I thought he gets shit because he patented his approach instead of making it available for everyone (please do correct me if I'm wrong).

TrinaryWorksToo 9y ago
It's GPLv3: https://github.com/numenta https://github.com/numenta
- taneq 9y ago
  That's the copyright license, which is orthogonal to patent issues. Edit: Apparently not, GPL v3 has clauses regarding patent grants in it.
  - TrinaryWorksToo 9y ago
    https://www.gnu.org/licenses/rms-why-gplv3.en.html https://www.gnu.org/licenses/rms-why-gplv3.en.html It looks like it includes some patent information.
    - taneq 9y ago
      Huh, I didn't realise that. That 'patents' clause is pretty powerful - if my (very quick) reading is right, any time you contribute to a GPL3 project, any patents that you own which would apply to that project are basically set free for anyone to use. Cool for FOSS advocates but I can see why a lot of businesses are wary of anything using GPL3.
      - belorn 9y ago
        GPLv3 initial draft was a copy of the clause from the apache license: "each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work," The final gplv3 text says: "Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version." As can be seen, they are very similar. The key difference between apache, a fairly popular license with businesses, and GPLv3 in the realm of patents, is the Novell-Microsoft pact clause. Its not about patents that you own, but rather patents for which you sub license from others. "If you convey a covered work, knowingly relying on a patent license,... you must ether ... arrange to deprive yourself of the benefit of the patent license for this particular work or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients." This clause is the main crux of the issue. Large companies with large lawyer departments has complex patent deals with competitors, and deprive yourself of the patent would mean to open up yourself to being sued, and the other option would mean renegotiate with the competitor to grant the patent to all and everyone.