8 ms·

Neural Programmer: Inducing Latent Programs with Gradient Descent [pdf]

59 points by dave_sullivan 11y ago

nl 11y ago
I for one welcome our new programming overlords. Fuck that is impressive. I've seen some talks from Google Brain people referencing bits of this but never understood the full picture. Damn. We should all go home now.
- deleted 11y ago
  [deleted]
aardvark179 11y ago
ArXiv link http://arxiv.org/abs/1511.04834 http://arxiv.org/abs/1511.04834
obvio171 11y ago
The paper says it's a general program induction solution. Does that mean one can already feed Neural Programmer into itself (ie. make it learn from its own previous executions' inputs—training sets—and outputs—learned programs) and try to see if it comes up with a more efficient version?
norswap 11y ago
This is going to be a very meta comment. You have been warned. The whole field of machine learning makes me kind of nervous. The problem is that the created systems seem kinda magical (quote Clarke, I double dare you). Even their creators don't seem to really understand what goes on in them. It's build it and see what comes out. It's heuristics all the way down. My feeling: the lack of predictability that results makes these technologies good, but not great. It's why Google's search results still suck. Why suggestion engines are not always super smart or painfully transparent. If one system in isolation is already hard to predict, what about interacting systems? Increasingly, our experience is shaped by these systems (search result customization etc). Isn't there a vicious feedback loop in there somewhere that pushes us somewhere at the whim of the un-understandable interactions of un-understandable machines?
- mrdrozdov 11y ago
  SVMs, Kernels, Online Learning are pretty well understood. Neural networks are an abuse of the chain rule and makes your model dependent on a bunch of partial derivatives that you don't necessarily understand why are important, but happen to be extremely effective when given lots of data. I think neural nets are probably what you're referring to. If you understand the intuition about your problem, then you can reason about what those partial derivatives might represent. Except a lot of the time we don't really have that level of understanding. What is your level of experience with machine learning? I'm a little surprised with your perception.
  - te_chris 11y ago
    I'm not OP, but I think you're misreading their comment. Their point, if I read it correctly, is not about the technical correctness of the ML in place, but the sociological effects. You can see this happening already: I'm sure FB's machine learning algos that they use to filter the stream are 'correct' in some way, but their effect is insidious and polarising on the way we consume information.
    - mrdrozdov 11y ago
      I think that's more a reflection of how Facebook chooses to use the results of an algorithm rather than the algorithms themselves.
    - norswap 11y ago
      Both, actually! Point is they are related.
  - norswap 11y ago
    Extreme beginner (I saw a couple presentations). Yes probably neural networks more specifically. I understand that's where most of the excitement is nowadays and what seems to hold promises for the future. Simpler classifiers are easier to grok, but people don't seem to project the same hopes on them that they do on neural networks. > Except a lot of the time we don't really have that level of understanding. Precisely my point. My work is in computer languages and I do a lot of parsing. People have a hard time conceptualizing it (truthfully, it is hard) despite the fact that it's fully deterministic. Visualizing a huge decision tree and its ramifications, and distant consequences within it seems something humans are bad at. Neural networks have this, except they feed back into themselves, are not exactly non-deterministic, but statistical, and use a bunch of encoded intuition.
taliesinb 11y ago
The rate of new ideas in deep-learning-type subfields is starting to remind me of the Cambrian explosion, which generated in only a few million years a vast range of genetic novelty to be whittled down into the familiar stock of body plans we see today. Indeed many of the interesting new papers are elaborations of old ideas along new themes, or useful and elegant new combinations of old ideas, with the occasional attention-grabbing paper when someone tries something that seems like it can't work and it does, and we now have an entirely new thing in our toolbox. Here is a highly unusual paper that showed up on HN recently, to little discussion. It tackles a related problem to the OP paper but in a way that could not be more different from the current default of gradients, linear algebra, large training data volumes, hands free training. It may end up being less impressive than it seems, but it is still a bit mind bending. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140866 http://journals.plos.org/plosone/article?id=10.1371/journal....
abeppu 11y ago
When I was at school one of my professors joked that the halting problem would ensure that, whatever else we were able to automate, there would always be jobs for programmers. I wonder. A while ago there was the automatic statistician [1,2] which can do various statistical analyses and reporting automatically. This year there was a paper out of MIT on Deep Feature Synthesis, in which a largely automated system did quite well on Kaggle problems [3]. Now this, which seems like it could produce solutions to some problems I've used in technical phone screens. At some point, someone will write a framework which automates the process of finding human cognitive tasks to automate, and someone else will give write a cost function of automated-task-to-business-need-mismatch which is amenable to optimization, and then we can all go home. [1] http://www.automaticstatistician.com/index/ http://www.automaticstatistician.com/index/ [2] http://mlg.eng.cam.ac.uk/lloyd/talks/jrl-auto-stat-msr-2014.pdf http://mlg.eng.cam.ac.uk/lloyd/talks/jrl-auto-stat-msr-2014.... [3] https://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uploads/Site/DSAA_DSM_2015.pdf https://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uplo...
- lern_too_spel 11y ago
  The halting problem applies equally to human intelligence.
- RobertoG 11y ago
  ", and then we can all go home." Going home works for me (if there is not a Terminator waiting there).
primaryobjects 11y ago
I feel compelled to also mention "Using Artificial Intelligence to Write Self-Modifying/Improving Programs" http://www.primaryobjects.com/CMS/Article149 http://www.primaryobjects.com/CMS/Article149 The link above describes a project where an AI wrote programs for Hello World, addition, subtraction, multiplication, Fibonacci, bottles of beer on the wall, and a bunch more.

59 points by dave_sullivan 11y ago

nl 11y ago

I for one welcome our new programming overlords. Fuck that is impressive. I've seen some talks from Google Brain people referencing bits of this but never understood the full picture. Damn. We should all go home now.

deleted 11y ago
[deleted]

aardvark179 11y ago

ArXiv link http://arxiv.org/abs/1511.04834 http://arxiv.org/abs/1511.04834

obvio171 11y ago

The paper says it's a general program induction solution. Does that mean one can already feed Neural Programmer into itself (ie. make it learn from its own previous executions' inputs—training sets—and outputs—learned programs) and try to see if it comes up with a more efficient version?

norswap 11y ago

This is going to be a very meta comment. You have been warned. The whole field of machine learning makes me kind of nervous. The problem is that the created systems seem kinda magical (quote Clarke, I double dare you). Even their creators don't seem to really understand what goes on in them. It's build it and see what comes out. It's heuristics all the way down. My feeling: the lack of predictability that results makes these technologies good, but not great. It's why Google's search results still suck. Why suggestion engines are not always super smart or painfully transparent. If one system in isolation is already hard to predict, what about interacting systems? Increasingly, our experience is shaped by these systems (search result customization etc). Isn't there a vicious feedback loop in there somewhere that pushes us somewhere at the whim of the un-understandable interactions of un-understandable machines?

mrdrozdov 11y ago
SVMs, Kernels, Online Learning are pretty well understood. Neural networks are an abuse of the chain rule and makes your model dependent on a bunch of partial derivatives that you don't necessarily understand why are important, but happen to be extremely effective when given lots of data. I think neural nets are probably what you're referring to. If you understand the intuition about your problem, then you can reason about what those partial derivatives might represent. Except a lot of the time we don't really have that level of understanding. What is your level of experience with machine learning? I'm a little surprised with your perception.
- te_chris 11y ago
  I'm not OP, but I think you're misreading their comment. Their point, if I read it correctly, is not about the technical correctness of the ML in place, but the sociological effects. You can see this happening already: I'm sure FB's machine learning algos that they use to filter the stream are 'correct' in some way, but their effect is insidious and polarising on the way we consume information.
  - mrdrozdov 11y ago
    I think that's more a reflection of how Facebook chooses to use the results of an algorithm rather than the algorithms themselves.
  - norswap 11y ago
    Both, actually! Point is they are related.
- norswap 11y ago
  Extreme beginner (I saw a couple presentations). Yes probably neural networks more specifically. I understand that's where most of the excitement is nowadays and what seems to hold promises for the future. Simpler classifiers are easier to grok, but people don't seem to project the same hopes on them that they do on neural networks. > Except a lot of the time we don't really have that level of understanding. Precisely my point. My work is in computer languages and I do a lot of parsing. People have a hard time conceptualizing it (truthfully, it is hard) despite the fact that it's fully deterministic. Visualizing a huge decision tree and its ramifications, and distant consequences within it seems something humans are bad at. Neural networks have this, except they feed back into themselves, are not exactly non-deterministic, but statistical, and use a bunch of encoded intuition.

taliesinb 11y ago

The rate of new ideas in deep-learning-type subfields is starting to remind me of the Cambrian explosion, which generated in only a few million years a vast range of genetic novelty to be whittled down into the familiar stock of body plans we see today. Indeed many of the interesting new papers are elaborations of old ideas along new themes, or useful and elegant new combinations of old ideas, with the occasional attention-grabbing paper when someone tries something that seems like it can't work and it does, and we now have an entirely new thing in our toolbox. Here is a highly unusual paper that showed up on HN recently, to little discussion. It tackles a related problem to the OP paper but in a way that could not be more different from the current default of gradients, linear algebra, large training data volumes, hands free training. It may end up being less impressive than it seems, but it is still a bit mind bending. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140866 http://journals.plos.org/plosone/article?id=10.1371/journal....

abeppu 11y ago

When I was at school one of my professors joked that the halting problem would ensure that, whatever else we were able to automate, there would always be jobs for programmers. I wonder. A while ago there was the automatic statistician [1,2] which can do various statistical analyses and reporting automatically. This year there was a paper out of MIT on Deep Feature Synthesis, in which a largely automated system did quite well on Kaggle problems [3]. Now this, which seems like it could produce solutions to some problems I've used in technical phone screens. At some point, someone will write a framework which automates the process of finding human cognitive tasks to automate, and someone else will give write a cost function of automated-task-to-business-need-mismatch which is amenable to optimization, and then we can all go home. [1] http://www.automaticstatistician.com/index/ http://www.automaticstatistician.com/index/ [2] http://mlg.eng.cam.ac.uk/lloyd/talks/jrl-auto-stat-msr-2014.pdf http://mlg.eng.cam.ac.uk/lloyd/talks/jrl-auto-stat-msr-2014.... [3] https://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uploads/Site/DSAA_DSM_2015.pdf https://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uplo...

lern_too_spel 11y ago
The halting problem applies equally to human intelligence.
RobertoG 11y ago
", and then we can all go home." Going home works for me (if there is not a Terminator waiting there).

primaryobjects 11y ago

I feel compelled to also mention "Using Artificial Intelligence to Write Self-Modifying/Improving Programs" http://www.primaryobjects.com/CMS/Article149 http://www.primaryobjects.com/CMS/Article149 The link above describes a project where an AI wrote programs for Hello World, addition, subtraction, multiplication, Fibonacci, bottles of beer on the wall, and a bunch more.