evanthebouncy OP t1_isfytt9 wrote on October 15, 2022 at 6:08 PM

Reply to comment by JNmbrs in [P] a minimalist guide to program synthesis by evanthebouncy

Yaya hit me with the question and I'll see what i can do!

JNmbrs t1_isgqdyr wrote on October 15, 2022 at 9:24 PM

Thanks! A few questions below:

What do you see as the bottleneck to be overcome to make library learning program synthesis systems (e.g., Dreamcoder) scalable? Where I've seen recent work on these systems, the work seems to focus on improvements in (a) search algorithms (e.g., https://arxiv.org/pdf/2110.12485.pdf); (b) program abstraction/library compression (e.g., https://mlb2251.github.io/stitch_jul11.pdf and http://andrewcropper.com/pubs/aaai20-forgetgol.pdf); (c) optimizing neural guidance (e.g., https://openreview.net/pdf?id=rCzfIruU5x5 and https://arxiv.org/pdf/2206.05922.pdf); and (d) specification (e.g., https://arxiv.org/pdf/2007.05060.pdf and https://arxiv.org/pdf/2204.02495.pdf). While obviously work proceeds in these (and other related) domains, I'd love to hear your thoughts on which one(s) are the bottlenecks where breakthroughs are most needed.
In the immediate term (3-5 years), in what fields (e.g., theory generators to aide scientists or as modules in robotics) do you think library learning program synthesis programs will have the greatest impact?
(Sorry if this is especially stupid, but) Do you think humans have explicit representations of rules (e.g., programs) in our brain "hardware" that we could in theory point to?
I was intrigued but also left a little confused by the LARC paper. In the conclusion you advocate for that we need advances to help map from natural programs to machine programs or, instead, that machine programs should have the properties of natural language (like being ambiguous)? Or did I miss the point entirely lol?

Huge thanks again for your time.

evanthebouncy OP t1_isgwjhn wrote on October 15, 2022 at 10:09 PM

Q: What do you see as the bottleneck to be overcome to make library learning program synthesis systems (e.g., Dreamcoder) scalable?

A: one can take a simulation based approach to understand/view dreamcoder -- an agent starts with only knowing primitives, and is asked to solve a set of tasks, from easy to hard. the agent solves a few, compress the library, then try to solve the ones slightly harder than those, and repeat. the input to the simulation is the set of tasks, and the learning algorithm, and you just hit "run" and off it goes in a self-contained way, and we observe what it comes up with in a few days -- kind of like opening a jar of a closed evolutionary system and see if dinosaurs are in there or something like that lol.

so obviously we can improve the simulation by picking up different components of dreamcoder and make them run faster or more efficient. my idea (kind of silly tbh) is to allow additional input as the simulation is run. what if you let users tweak the simulation as it is running? what if you let the user guide some of the search, or pick different curriculum of tasks? etc? how do we make it so it is easy for end-users to inject knowledge into the system as it is running?

ultimately we're designers as much as simulation creators. we can half let the system run on its own through self-play, half with some hand-picked intervention because humans are good at solving problems.

Q: In the immediate term (3-5 years), in what fields (e.g., theory generators to aide scientists or as modules in robotics) do you think library learning program synthesis programs will have the greatest impact?

A: well the sell is library right? so I'd say it'll do well in a field where, there _should_ be some library, yet they're somewhat unintuitive for the humans to design. I'm unsure haha maybe robotics is a good domain, or planning problems if we can view library learning as a kind of hierarchical planning set up, being able to come up with its own abstractions.

Q: (Sorry if this is especially stupid, but) Do you think humans have explicit representations of rules (e.g., programs) in our brain "hardware" that we could in theory point to?

A: I... don't know and I don't think about these problems too much tbh, I'm more practical, I want to build systems that end-users use. so by profession I don't ponder those questions. philosophically I'm more into music and reading old chinese stories hhaha so I don't ponder those questions philosophically either. I will tell you a funny story though, hopefully it makes up for my lack of answer. There was this lex friedman lecture at MIT at one point, and he invited a rly awesome neuro biologist. a student asked her a question "how do we know worms have no consciousness, what if they do?" and she simply said "it's unlikely because the sensory neuron (eye) of the worm directly wires into the motor (feet) of the worm, no in between, it sees bright light, it retracts backwards reflexively. so what hardware is there for the worm to even process and make decision?" and I thought that was brutally hilarious answer.

Although, irrespective of what our brain program is like, we _did_ invent rules and logic right? we _did_ invent tools that are highly programmatic, and reliable in their executions. So maybe the question should be "can we make AI systems that can _invent_ logic itself" because clearly humans have done it.

Q: I was intrigued but also left a little confused by the LARC paper. In the conclusion you advocate for that we need advances to help map from natural programs to machine programs or, instead, that machine programs should have the properties of natural language (like being ambiguous)? Or did I miss the point entirely lol?

A: the latter. machine programs need to have the properties of language, namely, being expressive and universal (i.e. can express a lot of ideas, and be understood by a range of interpreters), yet still being precise (i.e. can be used to carry out specific tasks). How to do it? honestly iono but I'm working on it so subscribe for the next episode (that's a sn00pdawg quote isn't it ahahaha)