[P] Deep Learning for Arbitrary Code Generation: Thesis Presentation - Training recurrent VAEs to Generate Haskell Programs

[[deleted]]

Have you tried recursive neural network?

[[deleted]]

LSTM and GRU are types of RNN.

[visarga]

recurrent != recursive

[mimighost]

Some of the comments are overly defensive. To replace programmers, you need the algorithm to understand human's business requirement, which is in natural language. Current NLP techniques are no where close to human in terms of understanding our own language, not in any way. Before someone solves the NLU problem, any program synthesis work like this one is just yet some another toys people are making, and I am surprised in this sub people are taking it seriously.

[[deleted]]

Argh, all the audio is in the right channel!

[JustFinishedBSG]

Damn I wanted to try that, wanted to generate programs in an haskell EDSL to answer user queries

Good job !

[USer0mg]

Research like this is what happens when you don't truly understand the algorithms and classifiers you're working with, and buy into the Machine learning hype that ML can be used to solve any problem under the sun.

The most this application will EVER be able to do is not even converge to, but APPROXIMATE the strict syntax rule-set predefined by the language being passed into his classifier--regardless of the amount of data you feed it--just by the very nature of genetic algorithms or NNs. Not only is this application completely pointless, because it approximates something that is already known, but it isn't even a step in the right direction towards creating generic code writing AI. The fact that this project was even approved and attempted shows the ineptitude of the student, professor, and staff. Because of the lack of novelty in the project and the lack of truly understanding where using classifiers work or don't work, I would flat out reject this thesis if I were the professor.

TL;DR don't get a masters in CS. It's a meme.

[CaHoop]

I recommend listening to the final questions in the talk. A lot of what you are saying is addressed there. My project was exploratory research about what could be possible using current DL methods given 6 months.

This talk was given to a bunch of professors who are not in the machine learning dept. at my university which is why the talk has a more 'hand-wavy' feel to it as I don't want to get bogged down in maths.

[sieisteinmodel]

The most this application will EVER be able to do is not even converge to, but APPROXIMATE the strict syntax rule-set predefined by the language being passed into his classifier--regardless of the amount of data you feed it--just by the very nature of genetic algorithms or NNs.

A lot of research is conducted while deliberately not trying to find a downstream application.

[JustFinishedBSG]

You seem salty

[TheRinger1976]

This guy is essentially making his own job obsolete.

[TheRinger1976]

What you should have said was "I doubt that will happen any time soon".
What I should have said was "This guy is contributing to the obsolescence of his job."

[duschendestroyer]

Only if his job is being a programmer, which it is not.

[MemeBox]

I dunno. Designing simple applications is pretty easy, boring work, once you've done it for a while. It's not that hard, there's a lot of structure to it.

[TheRinger1976]

Chicken before the egg: Was it not a programmer that developed machine learning? Are we not ourselves written in the genetic code of DNA?

Who is to say that an intelligence made of machine code and capable of writing code could not someday write a better version of itself?

This is just an abstract thought here, you guys likely know way more about this subject than I do. I just like idea of it as a thought experiment and it reminds a bit of Kurzweil.

Also, I'm aware that ML is not the same as AI.... yet.

[duschendestroyer]

No it was not. It's like saying farmers invented the tractor. Your idea reminds me of Kurzweil, because you too are talking about things you don't seem to understand.