This is one of the most exciting things I've seen come out of AI research for my own interests. The way that they distributed the mathematical equations is called an abstract syntax tree (maybe they mentioned this, but I was excited-skimming), and is also how computer code is represented. It also happens that there is _a lot_ more computer code laying around in computer-readable format than high school and college math problems. With this, you get: metamorphic code, better optimizers, more abstract programming languages (maybe), and probably lots of other steps forward.
If anyone is working on AI+programming languages, please send me a message. I'd love to work with others on this problem.
Have you considered attempting to develop a "differentiable programming language" so that it's possible to do gradient descent directly on the space of programs? There was some work on building a "differentiable lambda calculus", https://core.ac.uk/download/pdf/82396223.pdf, but I haven't heard of anyone trying to use something like that in modern program synthesis.
Love to chat. Sent u an email (former HFT and kernel guy).
One topic which is feel is neglected is a good GCN (or any GNN) to operate on existing code trees. Most approaches seem to prefer seq or at most tree inputs.
Is this simply not finding yet a good network architecture, or is it a performance issue ?
I'm planning on applying for PhD programs this fall to work in this area. There are only a few places in the world right now that I know of working on these types of problems. They are:
* Martin Vechev, ETH Zurich
* Dawn Song, University of California Berkeley
* Eran Yahav, Technion
* Miltiadis Allamanis, Microsoft Research Cambridge
If anyone knows other advisors looking for graduate students in this area, please let me know. Due to personal circumstances I can most likely not apply to ETH Zurich or Technion (I don't speak Hebrew anyway), which leaves me with only one potential advisor in a program that I really want.
There is also the Python writing model that Open AI showed recently at the Microsoft Build conference, so maybe there is some interest growing at other places as well.
I was also recently working on a deep learning decompiler but was unable to get my transformer model to learn well enough to actually decompile x64 assembly. I have the source code for the entire Linux kernel as training data, so it's not an issue with quantity. If anyone is interested in helping out with this project, please let me know in a comment.
I have the source code for the entire Linux kernel as training data, so it's not an issue with quantity
Linux kernel is only ~30M LOC. That's a really small dataset. For comparison, the reddit based dataset for GPT-2 is 100 times larger. Try using all C code posted on Github.
decompile x64 assembly
You can't "decompile" assembly. Either you decompile machine code, or you disassemble assembly code. The latter is easier than the former, so if you're trying to decompile executables, then perhaps you should train two models: one to convert machine code to assembly, and the other to convert assembly to C. Assembly code produced by an optimizing compiler might differ significantly from assembly code which closely corresponds to C code.
> perhaps you should train two models: one to convert machine code to assembly, and the other to convert assembly to C.
Is the step of going from machine code to gcc-produced assembly not trivial? Is gcc actually producing assembly code that an assembler needs to do more with than convert to the corresponding opcodes?
There are two kinds of assembly: 1. assembly that corresponds to optimized machine code, and 2. assembly that closely corresponds to the original C code. As I said, these two assembly versions might look very different depending on optimizations performed by the compiler. You can reduce the difficulty of learning the conversion from machine code to assembly at the expense of increasing the difficulty of learning the conversion from assembly to C code (and vice versa).
* Ras Bodik (Berkeley -> UW): esp. w/ Pedro Domingos and all the MSR collaborators (Sumit Gulwani, ...) <- a bit biased b/c I was in the group while at Berkeley; Ras + Dawn are crazy creative
* Percy Liang (Stanford): Coming from the ML side and w/ a long-running interest here
UCL is another good choice for this area. I did some research in this area back in undergrad. There was a workshop called NAMPI (neural abstract machine program intelligence) on this subfield so I'd recommend going through the accepted papers for it and seeing where all the faculty come from.
Also general advice, after finding a few papers you like go through the papers they cite that are relevant to the subfield and also papers that cite them. That's one of the best ways of finding other related research that interests you.
I've listened to some talks by Song, and my impression is that she does not seem to have a strong technical grasp of what's going on, for what it's worth. There is a lack of clarity in her communication style. I'd try to work with Vechev, Liang, UW PLSE group if I were you...
If the language barrier is the only reason keeping you from applying to the Technion, or any other university in Israel, graduate courses in Israel will generally be given in English if a non-Hebrew speaker is present. It's standard practice, just email the instructor in advance.
Besides, Haifa is a very nice city.
You should also check out professors at Ben Gurion University, Tel Aviv University and the Hebrew University who might have similar interests, IIRC.
Feel free to hit me up if there's some page in Hebrew that doesn't translate well.
Another person to talk to would be Brooks Paige, who recently joined UCL: https://tbrx.github.io/ This is totally not my area of research (I'm a PL person), but I know Brooks from the Alan Turing Institute and I think he'd be a great advisor for this kind of work.
Maybe don't discount the Technion so easily. All of the professors are fluent in English as is most of the student body. There are many graduate students who start studying there without knowing Hebrew.
There are a lot of groups working in this area. I'm going to pitch ours first, and then also point you to some others!
My colleagues and I run the SEFCOM lab at Arizona State University (https://sefcom.asu.edu/). Most relevant to your interests, Fish Wang (fellow SEFCOM professor) and I (Yan Shoshitaishvili) founded the angr program analysis framework (https://angr.io/) back in our gradschool days and have continued to evolve it together with our students in the course of our research at ASU. We're actually currently undertaking a concerted push into decompilation research, using both PL and ML techniques. This research direction is a passion of ours, and there's plenty of room for you here if you're interested!
Of course, we also do quite a bit of work in other areas of program analysis (including less overtly "mathy" techniques, like fuzzing) as well as other areas of cybersecurity. We are also quite active in the Capture the Flag community, if that is something that interests you!
Other places that do research in program analysis off the top of my head:
Forgive me for being entirely ignorant about the field, but I've thought about this frequently and would be grateful for a decent answer:
How theoretically feasible would it be to create Meta-AI.
My naive (incredibly over-simplified) notion goes something like this:
- You convert open-source code into a generic/universal AST/IR format, and figure out a means to represent function inputs and outputs (behavior)
- You use all of the OSS code in the world as data for this, to train a neural network to start making predictions about what kind of AST inputs in a program lead to what kinds of outputs (function synthesis)
- You tell the program to look at other deep learning/ML code, and to attempt to optimize it's own code, while running, inside of isolated processes/VM's.
Something like Erlang/Elixir or Lisp should be capable of this meta-programming and self-code modification while still running. And the BEAM would theoretically be able to isolate actors in the event that the epoch doesn't pan out very well and just kill that branch/lineage off.
Is this insane? I've wondered about this for years.
Although it uses genetic programming instead of neural networks. Those approaches are equivalent in power, the difference is only that neural networks are more amenable to parallelization and we know a very good baseline optimization algorithm (gradient descent and friends).
The problem is that coming up with an optimizer/architecturer is hard. So the benefit of running a meta-optimizer to solve a problem is difficult to realize versus a handmade neural network. The problem needs to be so large the network would need to re-engineer itself to solve it -- but think how many resources have already been spent in engineering neural networks (by humans no less, which are quite powerful optimizers!). Unless the problem is truly titanic (w.r.t. other current problems) the yields might be small.
What you can do in a similar vein is gather a large set of problems and train a 'Neural architect' or something like that that then can be applied many times to new problems. This allows sharing this cost over many new networks. I think it could make sense for governments and large organizations to get together and create this sort of thing. If you know the costs of training a large neural network, imagine the cost of training hundreds of millions to train some kind of neural architect (NA).
There are milder versions of this, where the architecture search itself isn't trained:
Of course even this approach has limitations (even if you theoretically allow it to create essentially arbitrary architectures), because it will still have difficulty "thinking outside the box" like we do using huge amounts of mathematical expertise and intuition (see EfficientNet -- the insight in that paper would be difficult to arise from a NA network) -- it's not the thing that will solve all of our problem forever; but it would be pretty significant (perhaps towards making large AI-based systems with multiple components, say self-driving cars and robots of the future).
Wow, thank you for the answer and the links. I had tried to search for something like this but I didn't have the right terminology.
I really hope to see something happen in this area before I die, just for the sake of seeing it happen.
I often wonder about whether neural networks might need to meet at a crossroads with other techniques.
Inductive Logic/Answer Set Programming or Constraints Programming seems like it could be a good match for this field. Because from my ignorant understanding, you have a more "concrete" representation of a model/problem in the form of symbolic logic or constraints and an entirely abstract "black box" solver with neural networks. I have no real clue, but it seems like they could be synergistic?
There's a really oddball repo I found that took this approach:
No, in fact I belive some variant of this approach is what will eventually lead to AGI. (you can probably do a similar type of solution but approaching from the direction of neural networks where you can use backprop)
If it could be used to greatly accelerate SMT solving/constraint solving, perhaps it would be possible to run a formal model as if it were code, i.e. fast and scalable constraint-based programming. That's about as high-level as you can get, short of a requirements document. I'm not sure if that's the kind of symbolic mathematics that's being studied here, as they seem to be looking at mathematically interesting expressions, rather than boring-but-enormous constraints.
Or, somewhat related, perhaps it could be used to help scale formal verification with languages like SPARK and ZZ.
In general, I think neural networks really struggle with np hard problems. Current state of the art is bottlenecked wrt to even subtle generalization. The strong baselines have been variations on the pointer-network architecture for some time now. Even though they can do reasonably well on restricted problems (e.g. instances of sat/traveling salesman with fixed # of nodes), they struggle to generalize to instances of the problem with variable # of nodes - not to mention varying the problem itself. Some popular approaches also incl. applications of Graph CNNs & reinforcement learning. Don't really have much more than a cursory idea of the current directions though.
To me, more exciting is some kind of joint collaboration between modern ml systems & formal solvers.
You might be interested in the line of work around differentiable relaxations of formal solvers that was kicked off by Zico Kolter et al. They add a Max=SAT solver as a differentiable layer. This allows the neat trick of solving handwritten Sudoku puzzles by composing a ConvNet with the differentiable Max-SAT layer.
If anyone is working on AI+programming languages, please send me a message. I'd love to work with others on this problem.