Like I said, I'm essentially continually prompting to refine the material. LLMs certainly continue to append, and never cut back. It just keeps spitting out additional content at me. So that's a bit annoying too. But I can basically get figure out what's going on with a few extra promps.

If youre curious what i've got so far... just be warned it is quite literally AI slop plus me continually prompting for clarification/cleanup etc. : https://github.com/cmoscardi/ai-for-ai

I pretty regularly work with social science researchers who have a need for something like this... will keep it in mind. For a bit we thought of setting something like this up within the Census Bureau, in fact. I have some stories about routing engines from my time there...

People from the federal civic tech nexus started them up over the past decade as they termed out of 18F, USDS, and PIF

The approach I share here (fine-tuning recent deep learning models) is the first one that's gotten me anything resembling high-quality OCR on these particular noisy historical documents. OCRing these has been something of a white whale for me for several years (except, a white whale that I have spent comparatively little time on).

At this point I think I am reasonably competent in OCR, but no expert... Curious for your thoughts.

Comments URL: https://news.ycombinator.com/item?id=41732252

Points: 2

# Comments: 1

[1] https://erikbern.com/2016/04/04/nyc-subway-math

[2] https://erikbern.com/2016/07/09/waiting-time-math.html

1. I believe the IR jargon for getting a JSON of this form is Key Information Extraction (KIE). MS has an out-of-the-box model for this. I just tried the screenshot and it did a pretty good (but not perfect) job. It didn't get every form field, but most. MS sort-of has a flow for fine-tuning, but it really leaves a lot to be desired IMO. Curious if this would be "good enough" to satisfy the use case.

2. In terms of just OCR (i.e. getting the text/numeric strings correct), MS is known to be the best on typed text at the moment [1]. Handwriting is a different beast... but it looks like MS is doing a very good job there (and SOTA on handwriting is very good). In particular, it got all the numbers in that screenshot correct.

If you want to see the results from MS on the screenshot in this blog post, here's the entire JSON blob. A bit of a behemoth but the key/value stuff is in there: https://gist.github.com/cmoscardi/8c376094181451a49f0c62406e...

[1] https://mindee.github.io/doctr/latest/using_doctr/using_mode...

Using the models underlying a library like this, there's maybe room for fine-tuning as well if you have a set of documents with specific semantic boundaries that current approaches don't capture. (And you spend an hour drawing bounding boxes to make that happen).

5 minute deck: https://github.com/codingitforward/cdfdemoday2018/blob/maste...

Feel free to shoot me a message.

This confused me as well, since I've always used the normal central-limit-based confidence interval for binary outcomes.

[1] https://en.wikipedia.org/wiki/Binomial_proportion_confidence...

(I am no expert in the analytic underpinnings of the beta distribution or precisely how it is the conjugate prior to the binomial -- or, rigorously speaking, what conjugate prior means -- but the formula here lines up with his formula :P )

Comments URL: https://news.ycombinator.com/item?id=16178220

Points: 2

# Comments: 2

https://www.lilite.co/

I'd love for it to be made obsolete though :)

This site gives me an interface to say, "oh yeah, I /do/ need VLC on this laptop, come to think of it", rather than having to lazy-evaluate that when I have a video downloaded and realize I don't currently have a means of playing it.

I spend a fair bit of time doing DevOps work, and this isn't meant to be some sort of configuration management or provisioning tool. This is for desktop / dev users, which is, I think, quite a different use case (but an increasingly relevant one). I have a dotfiles repo, which is to say I'm familiar with the "document things in scripts and repos" game - but it's not clear to me that something like a script-as-documentation is even a useful process to go through when any given computer I set up is going to be used differently. That laptop playing VLC is very different than the web dev box I spin up to mess around with a new project.

The motivation was basically a version of ninite[1] for Linux. So very desktop-focused. I actually got in touch with the ninite folks, who used to run a Linux installer [2]. They said there wasn't enough interest and package managers are "good enough." I respectfully disagree on that point - I think that having the ability to point-and-click for the core, most important packages you want to install is really useful when you're spinning up a new desktop from scratch.

I was running into this problem after basically doing ad-hoc setup for every new machine I turned on. Sure - I could write a shell script / list of packages and save it to my dotfiles repo, but where would the fun be in that? :)

(Plus, package lists on every machine are different)

[1] https://ninite.com/ [2] https://ninite.com/linux