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I get the sense that many commenters misinterpreted the point of this study. This isn't about show-boating some newfound, great cryptography method, this is abo

I get the sense that many commenters misinterpreted the point of this study. This isn't about show-boating some newfound, great cryptography method, this is about teaching computers to re-write their own code. If you can write software that improved itself better over time, our species as a whole would advance at incredible speeds. Think about all the possibilities that could evolve from self-maintained programs. Obviously the thought is a bit scary, but it's also incredibly exciting!

FT_intern 10y ago
and here I was as a programmer bragging about automating people's jobs...
ape4 10y ago
Its not their own code. Its code they own.
gcr 10y ago
Please don't editorialize. Finding the best parameters that optimize an objective function can hardly be called "re-writing its own code." Granted, the article is just as guilty. (Edit: The article also doesn't mention self-modifying code so I'm not sure where you're getting that idea from.) For the curious, here is the preprint: https://arxiv.org/pdf/1610.06918v1.pdf https://arxiv.org/pdf/1610.06918v1.pdf The technique is an off-the-shelf GAN that attempts to learn a transformation (not arbitrary code!) from input to encrypted output. The learned model is not Turing complete, and most importantly, is _not_ self-modifying. The optimization procedure is what modifies the network, not the network itself. GANs have been used before to create realistic pictures. They're not new here -- the application is. It's a cool application, sure, but doesn't involve self-modifying code.
- Houshalter 10y ago
  I don't think it's entirely inaccurate to call neural networks "rewriting their own code". For a long time people experimented with algorithms that could actually rewrite human programming languages, like genetic programming. And it's mostly a failure, because most mutations to most programs just break them. Neural networks fix this problem by being sort of 'continuous programming language' where any small change to the net results in a small change to the output. And because of that we have algorithms that can find good changes to make much easier than with code. They are theoretically turing complete under some circumstances. But even when they aren't completely recurrent as in this case, they can still learn very sophisticated functions. Theoretically a neural net can learn anything a fixed number of logic gates can learn, which is still pretty powerful. I don't think parent comment meant by computers that can "re-write their own code" was that the programs are self modifying. But the computer itself is self modifying, as one program can change another on that computer, so the computer has modified itself.
  - gcr 10y ago
    In both the examples you cite (genetic algorithms and neural networks), the optimization process is what modifies the parameters. The network doesn't modify its own parameters. It's like calling pencil and paper self-modifying because somebody could use a pencil to write down plans to build a better pencil.
    - Houshalter 10y ago
      As I said, the parent comment didn't say the network was self modifying. The exact quote was "this is about teaching computers to re-write their own code" which is roughly correct. If you see the weights of neural network as a sort of code, then the computer is rewriting it's own code.
      - Radim 10y ago
        And if you see the universe as "a sort of code", the network is rewiring the universe! How's that for a bombastic editorial title? (To be clear, I'm 100% agreeing with gcr here. Parameter optimization is a tremendously powerful technique, and quite possibly the "true currency" of our universe, but there's a few abstraction layers missing between that and a claim of "self-modifying AI")
        Houshalter 10y ago
        The universe isn't code, and only gcr used the term "self modifying". There is absolutely nothing bombastic implied by the statement that a program can rewrite code.
        Dylan16807 10y ago
        But it's fundamentally one module [re]writing a completely different module. "rewrites its own code" is a very misleading way to describe that.
ctrl-j 10y ago
> Think about all the possibilities that could evolve from self-maintained programs. What are some that you are considering? Most maintenance I do is in the form of bug repair and change requests. Most of those wouldn't be fixable (or even identifiable) by machines. It's not errors like "value x should be y" (almost all of those get shaken out in alpha.) It's requests like "When I get to step H in our workflow, I shouldn't have to fill out form 2 to get information about the customer I'm helping." I mean, for technical things like encryption, I could see a lot of growth potential. Other algorithms where there is a good metric for "better" would also be candidates (compression also springs to mind.) But on the whole, most applications have a lot of human interfacing and critical thinking that needs to happen. AI is going to take a long time to get to the point where it can fix those kinds of problems.
jandrewrogers 10y ago
If you read the paper, it is not rewriting its own code. It is optimizing for some objective function, which has been done before. For example, the MetroHash family of hash functions were created using a very similar technique back in 2012; the code didn't just learn to create hash functions, the ones it produced were objectively superior to the state-of-the-art at the time. I've applied the same approach to other computer algorithm optimization problems as well. This technique generalizes poorly, it really only works for algorithms with specific, somewhat narrow characteristics. If you understand the process as a weak form of algorithmic induction then the practical limitations become more apparent. Most code cannot be usefully constructed this way.
deleted 10y ago
[deleted]

FT_intern 10y ago

and here I was as a programmer bragging about automating people's jobs...

ape4 10y ago

Its not their own code. Its code they own.

gcr 10y ago

Please don't editorialize. Finding the best parameters that optimize an objective function can hardly be called "re-writing its own code." Granted, the article is just as guilty. (Edit: The article also doesn't mention self-modifying code so I'm not sure where you're getting that idea from.) For the curious, here is the preprint: https://arxiv.org/pdf/1610.06918v1.pdf https://arxiv.org/pdf/1610.06918v1.pdf The technique is an off-the-shelf GAN that attempts to learn a transformation (not arbitrary code!) from input to encrypted output. The learned model is not Turing complete, and most importantly, is _not_ self-modifying. The optimization procedure is what modifies the network, not the network itself. GANs have been used before to create realistic pictures. They're not new here -- the application is. It's a cool application, sure, but doesn't involve self-modifying code.

Houshalter 10y ago
I don't think it's entirely inaccurate to call neural networks "rewriting their own code". For a long time people experimented with algorithms that could actually rewrite human programming languages, like genetic programming. And it's mostly a failure, because most mutations to most programs just break them. Neural networks fix this problem by being sort of 'continuous programming language' where any small change to the net results in a small change to the output. And because of that we have algorithms that can find good changes to make much easier than with code. They are theoretically turing complete under some circumstances. But even when they aren't completely recurrent as in this case, they can still learn very sophisticated functions. Theoretically a neural net can learn anything a fixed number of logic gates can learn, which is still pretty powerful. I don't think parent comment meant by computers that can "re-write their own code" was that the programs are self modifying. But the computer itself is self modifying, as one program can change another on that computer, so the computer has modified itself.
- gcr 10y ago
  In both the examples you cite (genetic algorithms and neural networks), the optimization process is what modifies the parameters. The network doesn't modify its own parameters. It's like calling pencil and paper self-modifying because somebody could use a pencil to write down plans to build a better pencil.
  - Houshalter 10y ago
    As I said, the parent comment didn't say the network was self modifying. The exact quote was "this is about teaching computers to re-write their own code" which is roughly correct. If you see the weights of neural network as a sort of code, then the computer is rewriting it's own code.
    - Radim 10y ago
      And if you see the universe as "a sort of code", the network is rewiring the universe! How's that for a bombastic editorial title? (To be clear, I'm 100% agreeing with gcr here. Parameter optimization is a tremendously powerful technique, and quite possibly the "true currency" of our universe, but there's a few abstraction layers missing between that and a claim of "self-modifying AI")
      - Houshalter 10y ago
        The universe isn't code, and only gcr used the term "self modifying". There is absolutely nothing bombastic implied by the statement that a program can rewrite code.
        Dylan16807 10y ago
        But it's fundamentally one module [re]writing a completely different module. "rewrites its own code" is a very misleading way to describe that.

ctrl-j 10y ago

> Think about all the possibilities that could evolve from self-maintained programs. What are some that you are considering? Most maintenance I do is in the form of bug repair and change requests. Most of those wouldn't be fixable (or even identifiable) by machines. It's not errors like "value x should be y" (almost all of those get shaken out in alpha.) It's requests like "When I get to step H in our workflow, I shouldn't have to fill out form 2 to get information about the customer I'm helping." I mean, for technical things like encryption, I could see a lot of growth potential. Other algorithms where there is a good metric for "better" would also be candidates (compression also springs to mind.) But on the whole, most applications have a lot of human interfacing and critical thinking that needs to happen. AI is going to take a long time to get to the point where it can fix those kinds of problems.

jandrewrogers 10y ago

If you read the paper, it is not rewriting its own code. It is optimizing for some objective function, which has been done before. For example, the MetroHash family of hash functions were created using a very similar technique back in 2012; the code didn't just learn to create hash functions, the ones it produced were objectively superior to the state-of-the-art at the time. I've applied the same approach to other computer algorithm optimization problems as well. This technique generalizes poorly, it really only works for algorithms with specific, somewhat narrow characteristics. If you understand the process as a weak form of algorithmic induction then the practical limitations become more apparent. Most code cannot be usefully constructed this way.

deleted 10y ago

[deleted]