AI for Data Journalism: demonstrating what we can do with this stuff

https://simonwillison.net/2024/Apr/17/ai-for-data-journalism/

by duck • 2 years ago

167 32 2 years ago

jeffbee• 2 years ago

Regarding the campaign finance documents, it's not just a vision problem with handwritten filings. Even when I feed Gemini Pro a typed PDF with normal text (the kind you can search, select, and copy in any PDF reader) it just invents things to fill in the result. I asked it to summarize the largest donors of a local campaign given a 70-page PDF and it gave me a top-10 table that seemed plausible but all the names were pulled from somewhere else in Gemini's tiny brain. None of them appeared anywhere in the filing.

simonw9• 2 years ago

Yeah, I'm beginning to think detailed OCR-style extraction just isn't a good fit for these models.

It's frustrating, because getting that junk out of horribly scanned PDFs is 90% of the need of data journalism!

The best OCR API I've used is still AWS Textract - have you seen anything better?

I ended up building a tiny CLI tool for Textract because I found the thing so hard to use: https://github.com/simonw/textract-cli

hm-nah• 2 years ago

Azure Doc Intel is good. It creates a semantic representation (is json) of the doc. The schema is useful with page, paragraph, table, etc objects. The bounding-box for each element is also useful. It flails with images, only giving you the bounding-box of where the image exists. It’s up to you to extract any images separately then figure out how to correlate. Overall I think it’s useful rather than rolling your own (at least for small scale).

mistrial9• 2 years ago

OCR software is thirty years in the making! there must be dozens of alternatives .. interested to hear from people close to this topic

is_true• 2 years ago

I had a good experience using easyocr to get data out of lottery draws videos

wcedmisten• 2 years ago

This is why I can't really trust LLMs for data tasks like this. I can't really be certain that it won't be hallucinating results

devmor• 2 years ago

Generative AI is just not the Leatherman Tool everyone wants it to be.

There are absolutely ways to create solutions in this problem space using ML as a tool, but they are more specialized and probably not economical until the cost of training bespoke models goes down.

anamax• 2 years ago

"Leatherman Tool" is a good comparison, as pretty much every "blade" in a Leathermann is a crappy tool, better only than the Swiss Army knife equivalent.

The Leatherman's reason for existence is that you have it with you for unexpected problems.

ddp26• 2 years ago

Looks great Simon! Do you have any anecdotes of journalists using the techniques in this demo in news pieces we can read?

simonw9• 2 years ago

Most of the stuff I presented in this talk (the structured data extraction things, AI query assistance etc) is so new that it's not had a chance to be used for a published story yet - I'm working with a few newsrooms right now getting them setup with it though.

Datasette itself has been used for all kinds of things within newsrooms. Two recent examples I heard about: the WSJ use it internally for tools around topics like CEO compensation tracking, and Bellingcat have used it for some of their work that involves leaked data relating to Russia.

The problem with open source tools is that people can use them without telling you about it! I'm trying to get better at encouraging people to share details like this with me, I'd really like to get some good use-cases written up.

simonw9• 2 years ago

This post is about a talk I gave at a data journalism conference, but it's also a demo of a whole bunch of projects I've been working on over the past couple of months/years:

- Claude 3 Haiku generation via phone or laptop camera: https://tools.simonwillison.net/haiku

- A new Datasette plugin for creating tables by pasting in CSV/TSV/JSON data: https://github.com/datasette/datasette-import

- A plugin that lets you ask a question in English and have that converted into a SQL query (also using Claude 3 Haiku): https://github.com/datasette/datasette-query-assistant

- shot-scraper for scraping web pages from the command-line: https://shot-scraper.datasette.io/en/stable/javascript.html

- Datasette Enrichments for applying bulk operations to data in a SQLite database table: https://enrichments.datasette.io/en/stable/ - demonstrating both a geocoder enrichment https://github.com/datasette/datasette-enrichments-opencage and a GPT-powered one: https://github.com/datasette/datasette-enrichments-gpt

- The LLM command-line tool: https://llm.datasette.io/ - including a preview of the image support, currently in a branch

- The datasette-extract plugin for extracting structured data into tables from unstructured text and images: https://www.datasette.cloud/blog/2024/datasette-extract/

- The new datasette-embeddings plugin for calculating and storing embeddings for table content and executing semantic search queries against them: https://github.com/datasette/datasette-embeddings

- Datasette Scribe by Alex Garcia: a tool for transcribing YouTube video audio into a database, diarizing speakers and making the whole thing searchable: https://simonwillison.net/2024/Apr/17/ai-for-data-journalism...

doctorpangloss• 2 years ago

But Simon, you are a strongly opinionated guy whose opinions are interesting. Why don't you make a data journalism piece? Why don't you submit an article to the NYTimes, WSJ, etc.? They will publish it! What are you waiting for?

simonw9• 2 years ago

Sounds like a lot of work!

I have been thinking that I need to do some actual reporting myself at some point though, ultimate version of dogfooding for all of my stuff.

andy99• 2 years ago

Looks interesting - my reaction though is that AI is great at demos and always has been. The devil is in the details and historically that has made it unusable for most applications despite the presence of a cool demo. I'm not criticizing the project, I get that its demos. Just that we judge AI too much by demos and then handwave about how it will actually work in practice.

simonw9• 2 years ago

That was very much the point of my talk - and the reason I had so many live demos (as opposed to pre-recorded demos). I wanted to have at least a few instances of demos going wildly wrong to help emphasize how unreliable this stuff is.

Being unreliable doesn't mean it isn't useful. Journalists handle unreliable sources all the time - fact-checking and comparing multiple sources is built into the profession. As such, I think journalists may be better equipped to make use of LLMs than most other professions!

larodi2• 2 years ago

It is useful and perhaps very useful for journalists and other people who use it onetime. It is very ill suited for massive automation at the moment, and that’s a real problem everyone struggles with.

The application of embedding vectors without the rest of the LLM can presently deliver much sustainable innovation. At least compared to present day SOTA models (imho of course).

fauigerzigerk2• 2 years ago

I found the talk very interesting because it shows both the issues as well as potential solutions.

One of the demos (extracting text from a PDF turned PNG) makes me wonder how you're ever going to fact check whether something in there is a hallucination. Innocent doctors won't always turn out to be Michael Jackson's sister after all :)

But then in one of the last demos you're showing how the fact checking can be "engineered" right into the prompt: "What were the themes of this meeting and for each theme give me an illustrative quote". Now you can search for the quote.

This is kind of eye opening for me, because you could build this sort of deterministic provability into all kinds of prompts. It certainly doesn't work for all applications but where it does work it basically allows you to swap false positives for false negatives, which is extremely valuable in many cases.

Upvoter33• 2 years ago

Great talk and I have been enjoying your work. Keep it up!

ctrw• 2 years ago

Ai in its current iteration is a human enhancement tool, not a human replacing tool. I do not understand how anyone who works for a loving thinks this is a bad thing.

It would be like if the industrial revolution made artisanal weaving a million times more efficient without a giant satanic mill.

photochemsyn• 2 years ago

There was a story about how many legislative bills introduced in the US were written by lobbyists a few years ago that might have benefited from these tools:

https://publicintegrity.org/politics/state-politics/copy-pas...

"Using data provided by LegiScan, which tracks every proposed law introduced in the U.S., we pulled in digital copies of nearly 1 million pieces of legislation introduced between 2010 and Oct. 15, 2018."

The scoring system used seems fairly simple in comparison to what AI can do: > "Our scoring system is based on three factors: the longest string of common text between a model and a bill; the number of common strings of five or more words; and the number of common strings of 10 or more words."

AlbertCory• 2 years ago

I found a couple of bylined articles in the Daily Illini that I wrote as a freshman. Very boring stuff; they didn't give the exciting beats to a newbie.

Then I ran them through ChatGPT to see what it thinks "objective journalism" is.

https://albertcory50.substack.com/p/ai-does-journalism

Conclusion: if I'd done what it suggested, the editor would have red-pencilled it out. It isn't that hard to just write the facts. No one wants to hear "both sides" on a donation of land to the Park District.

simonw9• 2 years ago

One of the themes of my talk was that generating text directly is actually one of the least interesting applications of LLMs to journalism - that's why I focused on things like structured data extraction and code generation (SQL and code interpreter), those are much more useful for data journalists IMO.

Vvector• 2 years ago

"Haikus from images with Claude 3 Haiku"

But think of all the poet jobs you are eliminating...

AI for Data Journalism: demonstrating what we can do with this stuff

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