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yldedly t1_isaznkg wrote

Have you tried synthesizing probabilistic programs and inference programs? Any general thoughts on the topic?

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evanthebouncy OP t1_isb2n4f wrote

Not much personally no.

But it's widely applicable, because in many instances, you'll have a stoicastic system that generates data. You see the data, you want to infer the system.

Example 1 modeling behavior. You could have a game, and the way a person plays the game is random, doing something different at times. By observing a person playing the game, you have collected some observation data, that's generated from a random behavior. To model the strategy the person is using, you'd have to use a probablistic program. It'll have some logical components, and some random components.

Example 2 modeling natural phenomenon. You have a toilet (I'm sitting on one now lmaoo) that you're building and you want to know, given the weight and consistency of the poo inside (X), how much water does it need (Y) to flush cleanly. The relationship between X and Y can be described by an equation, plus some noise, making it really intuitive to model as a probablistic program.

I'd learn about it here

https://probmods.org/

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yldedly t1_isb5nsi wrote

What evocative examples :P
I know probmods.org well, it's excellent. I wrote a blogpost about program synthesis. I stumbled on the area during my phd where I did structure learning for probabilistic programs, and realized (a bit late) that I was actually trying to do program synthesis. So I'm very interested in it, wish I had the chance to work with it more professionally. Looking forward to reading your blog!

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evanthebouncy OP t1_isbui1j wrote

I read all of your blog.

I loved this reference

"""The physicist David Deutsch proposes a single criterion to judge the quality of explanations. He says good explanations are those that are hard to vary, while still accounting for observations. """

You write really well! I followed you on twitter. I Think you have thought about the relationship between explaining data and probablistic programming deeper and longer than I have so i cant say much of surprising cool things to you.

I think my work "communicating natural programs to humans and machines" will entertain you for hours. Give it a go.

It's my belief that we should program computers using natural utterances such as language, demonstration, doodles, ect. These "programs" are fundamentally probablistic and admits multiple interpretations/executions.

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yldedly t1_isecsiy wrote

>I think my work "communicating natural programs to humans and machines" will entertain you for hours. Give it a go.

I will, looks super interesting. I'm so jealous of you guys at MIT working on all this fascinating stuff :D

>It's my belief that we should program computers using natural utterances such as language, demonstration, doodles, ect. These "programs" are fundamentally probablistic and admits multiple interpretations/executions.

That's an ambitious vision. I can totally see how that's the way to go if we want "human compatible" AI, in Stuart Russell's sense where AI is learning what the human wants to achieve, by observing their behavior (including language, demonstrations, etc).

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evanthebouncy OP t1_iseg25n wrote

Yaya thanks! My belief is that for most part, people know exactly what they want from computers, and can articulate it well enough so that a developer (with knowledge of computers) can implement it successfully. In this process the first person need not code at all, in the traditional sense.

All we need is the technology to replace the dev with AI haha

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evanthebouncy OP t1_isq1rru wrote

Yo.

Foundation Posteriors for Approximate Probabilistic Inference

Read this on arxiv

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