
MIT researchers use Bayesian inference, a form of AI, to make computer programs - laurex
https://www.zdnet.com/article/mit-aims-to-help-data-scientists-by-having-ai-do-the-heavy-lifting/
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dragontamer
I've always found Bayesian Inference to be a more "solid" theory than neural
nets. I like the idea, but its just a mish-mash of words to me right now...

EDIT: The link in the article doesn't work for me. Here's the link I used
instead:
[https://dl.acm.org/citation.cfm?id=3290350](https://dl.acm.org/citation.cfm?id=3290350)

Looking it over, the words seem to be... in the correct order. :-) "Proper use
of Prior" and Baye's theorem. Heh, I'm going to have to pull out some
textbooks to remind myself what these words mean.

EDIT2: Are they... generating a Bayesian model automatically? That's some
meta-level theory going on here. A Bayesian Model is a graph of priors (If A
then B 50% of the time, if B then C 25% of the time, etc. etc.). You observe
"C", and then the program seems to be trying to find the "If A->B" probability
and "If B->C" probability.

Unlike neural nets which are "unobservable", the human can theoretically look
at the list of priors and understand the model that the computer has
generated. As I stated earlier: I've always found this branch of AI to be more
interesting than neural nets.

~~~
jamessb
Working link to a copy of the paper on one of the author's websites:
[https://people.csail.mit.edu/rinard/paper/popl19.pdf](https://people.csail.mit.edu/rinard/paper/popl19.pdf)

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htfy96
The title pretty much reflects today people's focus on such a progress. Maybe
we can train attention networks to simulate focus of different groups of
people, and I'm sure that "MIT" & "AI" will end up with high weights for
average people while "Bayesian inference" will be on the top for researchers'
model.

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eli_gottlieb
Bayesian program synthesis is an approach to ML that's been slowly brewing
since the early parts of this decade, and as a member of a "sister lab" to
Vikash's, I'm excited to see their POPL paper getting this kind of popular
attention.

I'll be even more excited if their techniques from this paper can actually be
widely reapplied! BPS as a technique hasn't had its "ImageNet" moment in which
a strong, nontrivial, real-world benchmark is identified and then solved.

