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This is not true at all, and no offense, is a misunderstanding of how NNs can be used in other applications. For one, what you are modeling itself does not nee

This is not true at all, and no offense, is a misunderstanding of how NNs can be used in other applications. For one, what you are modeling itself does not need to be differentiable, only the network itself needs to be. Second, using neural networks in combination with other techniques for program synthesis is an active area of research currently, and although it is currently at around 50 lines or so, your fundamental assertion here is wrong. Third, there are a number of ways that deep learning could be leveraged in app development. The easiest way would be to make heuristic-type decisions around UI/UX or to build parts of said GUIs using existing code blocks. This has already been used to some extent in website design (e.g. https://arxiv.org/abs/1705.07962 https://arxiv.org/abs/1705.07962 and https://blog.floydhub.com/turning-design-mockups-into-code-with-deep-learning/ https://blog.floydhub.com/turning-design-mockups-into-code-w...). So it's certainly possible that it could be used in conjunction with templates to build common app types. Now, this startup is clearly not doing that, but, that doesn't mean that it's a) impossible to leverage AI for app development and b) all of deep learning is "just curve fitting"

YeGoblynQueenne 7y ago
To be fair, neural program synthesis has been an active area of research for a while and it doesn't seem that it's going to ever take off for good, like machine vision or NLP. I keep linking to this page: https://blog.keras.io/the-limitations-of-deep-learning.html https://blog.keras.io/the-limitations-of-deep-learning.html But what Chollet says is still the case. Machine-learning a mapping from arbitrary specifications to programs is many, many times more difficult than classification. Unless someone comes up with a completely new architecture that is for neural program synthesis what CNNs are for vision and LSTMs for sequence learning, and then some, then there's not going to be any big advances in the field. Source: I study algorithms that learn programs from examples from my PhD and you need three things for it that neural nets lack: a) generalisation, b) the ability to learn recursive functions and c) higher-order representations (i.e. quantified variables). Personally, I was very excited with DeepMind's differentiable neural computers, but it seems very hard to train on anything but toy problems.
- marmaduke 7y ago
  > I study algorithms that learn programs from examples What does work?
  - nickpsecurity 7y ago
    Simple, narrow stuff that requires expensive experts in AI to keep updating along with good hardware. Alternatively, you can pay a cheaper human to more jobs even better with modern tools that make most of them easy. It's what almost all successful and almost-successful companies do.
    - marmaduke 7y ago
      > Simple, narrow stuff that requires expensive experts in AI to keep updating along with good hardware This does seem to describe most machine learning.
  - YeGoblynQueenne 7y ago
    Inductive Logic Programming works: https://en.wikipedia.org/wiki/Inductive_logic_programming https://en.wikipedia.org/wiki/Inductive_logic_programming (But that wikipedia article is a bit behind the times).
    - YeGoblynQueenne 7y ago
      Late edit: Also, Inductive Functional Programming works. Check out Magic Haskeller: http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.html http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.ht...
codesushi42 7y ago
For one, what you are modeling itself does not need to be differentiable, only the network itself needs to be. No. In some simple cases you can model a non differentiable function with a differentiable one accurately. But your model is likely not to perform well if the underlying relation between your parameters and observations is non differentiable, because your model will likely be unstable. Second, using neural networks in combination with other techniques for program synthesis is an active area of research currently, and although it is currently at around 50 lines or so, your fundamental assertion here is wrong. Sorry, but NNs haven't made any significant progress in the realm of program synthesis. I can set up a model with Transformers and BERT that spews out thousands of lines of code that stylistically look correct, but does not compile. NNs aren't the right tool for the job here. Third, there are a number of ways that deep learning could be leveraged in app development That has nothing to do with the original post. There's no AI that's going to write an app for you.
- deleted 7y ago
  [deleted]
- deepnotderp 7y ago
  >Sorry, but NNs haven't made any significant progress in the realm of program synthesis. I can set up a model with Transformers and BERT that spews out thousands of lines of code that stylistically look correct, but does not compile. NNs aren't the right tool for the job here. Di.. did you even bother reading what I wrote? You wouldn't use NNs to directly output code, you'd use it to guide a search process.

YeGoblynQueenne 7y ago

To be fair, neural program synthesis has been an active area of research for a while and it doesn't seem that it's going to ever take off for good, like machine vision or NLP. I keep linking to this page: https://blog.keras.io/the-limitations-of-deep-learning.html https://blog.keras.io/the-limitations-of-deep-learning.html But what Chollet says is still the case. Machine-learning a mapping from arbitrary specifications to programs is many, many times more difficult than classification. Unless someone comes up with a completely new architecture that is for neural program synthesis what CNNs are for vision and LSTMs for sequence learning, and then some, then there's not going to be any big advances in the field. Source: I study algorithms that learn programs from examples from my PhD and you need three things for it that neural nets lack: a) generalisation, b) the ability to learn recursive functions and c) higher-order representations (i.e. quantified variables). Personally, I was very excited with DeepMind's differentiable neural computers, but it seems very hard to train on anything but toy problems.

marmaduke 7y ago
> I study algorithms that learn programs from examples What does work?
- nickpsecurity 7y ago
  Simple, narrow stuff that requires expensive experts in AI to keep updating along with good hardware. Alternatively, you can pay a cheaper human to more jobs even better with modern tools that make most of them easy. It's what almost all successful and almost-successful companies do.
  - marmaduke 7y ago
    > Simple, narrow stuff that requires expensive experts in AI to keep updating along with good hardware This does seem to describe most machine learning.
- YeGoblynQueenne 7y ago
  Inductive Logic Programming works: https://en.wikipedia.org/wiki/Inductive_logic_programming https://en.wikipedia.org/wiki/Inductive_logic_programming (But that wikipedia article is a bit behind the times).
  - YeGoblynQueenne 7y ago
    Late edit: Also, Inductive Functional Programming works. Check out Magic Haskeller: http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.html http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.ht...

codesushi42 7y ago

For one, what you are modeling itself does not need to be differentiable, only the network itself needs to be. No. In some simple cases you can model a non differentiable function with a differentiable one accurately. But your model is likely not to perform well if the underlying relation between your parameters and observations is non differentiable, because your model will likely be unstable. Second, using neural networks in combination with other techniques for program synthesis is an active area of research currently, and although it is currently at around 50 lines or so, your fundamental assertion here is wrong. Sorry, but NNs haven't made any significant progress in the realm of program synthesis. I can set up a model with Transformers and BERT that spews out thousands of lines of code that stylistically look correct, but does not compile. NNs aren't the right tool for the job here. Third, there are a number of ways that deep learning could be leveraged in app development That has nothing to do with the original post. There's no AI that's going to write an app for you.

deleted 7y ago
[deleted]
deepnotderp 7y ago
>Sorry, but NNs haven't made any significant progress in the realm of program synthesis. I can set up a model with Transformers and BERT that spews out thousands of lines of code that stylistically look correct, but does not compile. NNs aren't the right tool for the job here. Di.. did you even bother reading what I wrote? You wouldn't use NNs to directly output code, you'd use it to guide a search process.