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The author proposes a lot of vague ideas in this article (for example "I believe one of the biggest problems is the use of Error Propagation and Gradient Descent") without references or any solid proofs why they are necessary to solve the proposed program (Automate programming using ML?).

In fact there is already a lot of solid work just on this subject:

* Learning algorithms from examples http://arxiv.org/abs/1511.07275 https://arxiv.org/abs/1410.5401

* Generating source code from natural language description http://arxiv.org/abs/1510.07211

* And, the most closest work to what author probably wants, a way to write a program in forth while leaving some functions as neural blackboxes to be learned from examples: http://arxiv.org/abs/1605.06640

* Also there is a whole research program by nothing less than Facebook AI Research that explicitly aims at creating a conversational AI agent that is able to translate user's natural language orders into programs (asking to the user additional questions if necessary): http://arxiv.org/abs/1511.08130 (there is also a summary here http://colinraffel.com/wiki/a_roadmap_towards_machine_intell... )

And deepmind is also working on conversational agents: https://youtu.be/vQXAsdMa_8A?t=1265

Given current success of such models, automating simple programming tasks maybe not as much research as engineering and scaling up problem.

There is a lot of exciting machine learning research out there nowadays. Almost all of this research is available for free from papers posted on arxiv. It is a really good idea to read more about state of the art before coming with new ideas.

⬐ iammyIP

The first response of an actual AI confronted with the task of creating an Uber-for-cats would be: "What do you mean with that? Please be more precise."

⬐ yazr

Great mini survey!

Can you mention any key papers about NNs that manipulate existing code? e.g. parse it, find design pattern, optimize, etc I think this is just as interesting and already feasible.

⬐ sapphireblue

Thank you! I found a great list of deep learning paper summaries that is mostly about learning algorithms from data and other cutting edge models: http://colinraffel.com/wiki/tag:neural_networks?do=showtag&t... all kudos to the owner/author of this list !

>NNs that manipulate existing code? e.g. parse it, find design pattern, optimize

Also: you probably shouldn't need special architectures for such problems, these can be solved by seq2seq, NTM, RL-NTM and such, given a good dataset. Such tasks are still beyond current state of art though, it seems.

⬐ bjenik

The forth black box paper seems to be quite close actually. The big difference is that I would like to go for higher level tasks. The low level variant - having code missing a few lines/functions and filling those is indeed quite well researched. More interesting would be the opposite: Having blocks of code (called components in my text) and letting the net find a good way to use them. Normally the way a programmer would build something like let's say the amazon website is sketching up a few ideas how it could look - cutting it into small parts and then writing (or reusing) the code for the small parts.

A lot of the "using AI for programming" papers (AI in this case being anything from nets to logic stuff) focus on building these small parts - I'm more interested in the levels above that. The "generating code from natural language/dialogs" also kind of misses my idea as it still assumes a "programmer" to tell it what to do (in the amazon example "display the products on a grid" or "make the buy button bigger"). The programmer would in this case either guess the right thing ("everybody has their products in a grid") or run something like A/B testing against some metric ("items sold" or "$ revenue") and then go back to the conversational programming tool and tell it "change the button". Why not skip the programmer and give the metric directly to the net? The thing I would like to get working is telling the net "here is a ton of different buttons, grids, lists and a lot of other UI stuff, and there are all our products - I want to maximize revenue - do whatever you like to get there".

Regarding the vague ideas: I believe (also totally unsubstantiated - though research in this area would be quite interesting) that writing down ideas first and then looking into them (either by reading other papers or doing own research) is far better than the other way round, because it has a higher probability of leading to something new, because you don't follow assumptions and errors others potentially made which could lead to a dead end. Of course there is a certain trade off, because if you try to reinvent everything you will not get to the point of something new.

⬐ teddyknox

> The thing I would like to get working is telling the net "here is a ton of different buttons, grids, lists and a lot of other UI stuff, and there are all our products - I want to maximize revenue - do whatever you like to get there".

As you referenced in your footnote, this goal is a little like the goal of putting a man on the moon, if the year is 1900. One could the make the case that something resembling an AGI agent would be needed to design/manage a merchant website with any degree of introspection and customizability. Your machine would need to understand business objectives, UI patterns, aesthetics, etc before taking the helm, and these things take years to learn even for even humans. This is not to say that AGI isn't an admirable goal, but that it's an obvious one, just as I imagine reaching the moon was in the centuries leading up to 1969.

Scientific research is certainly a massively parallel effort, pushing the boundary of our knowledge in many different direction at once, but rest assured that there are many good men and women working in this specific direction.

(also know that I have a lot of respect for this kind of vision-oriented thinking, especially because it generally leaves the writer vulnerable to criticism like this)

⬐ sapphireblue

IMHO talking about AGI here sounds like "all or nothing" type of thinking. Almost all problems can be formulated as [perhaps very hard] machine learning problems of 3 basic types https://en.wikipedia.org/wiki/Machine_learning#Types_of_prob... .

Every ML problem has a lot of algorithms that can be applied to it to solve it. Previously unsolvable problem (e.g. learning playing atari game from pixels) can become solvable if a better ML algorithm is devised, more compute power is applied, or the problem is simplified (maybe with data augmentation or feature engineering, or constraining the domain).

I think the problem you are stating - finding the best configuration of website to extract more ad revenue - can be simplified until it is solvable by currently available algorithms and hardware. Big companies are already doing similar things - Google uses reinforcement learning to recommend new items on its services, Facebook is known to use machine learning to somehow make more profit from users.

Given expert knowledge of the problem and good algorithms, it can be solved in some form, that's my point here.

⬐ teddyknox

Sure. I think what the OP was playing around with was whether we could generalize from, as you said, "expert knowledge of the problem[s] and good algorithms" so that AI could replace components in the incumbent programming paradigms. Agreed, when the end problem is well-defined and you know how "do" machine learning, the world is your oyster.