5,452 karma · joined November 14, 2019
Last time I flew out of Laguardia I opted out and while I was being patted down another TSA agent about twenty feet away kept making kissy faces at me. Very much felt like intimidation.
What a time..
Yes, and many companies have access to both feeds.....
For example, it was very hard for me to identify myself in an anonymized public dataset of vehicle trips, but I did. It was also hard to FOIA for the documents showing them writing SQL to spot my trip.. but I did.
Hard doesn't mean impossible.
Let me share a thing:
Factual, a company that specializes in hyperlocal geofencing, uses geofencing much smaller than the self-regulation that their industry allows in their own rules. I learned this after a coworker quit because our company was allowing ad targeting to people using these smaller geofences. The whole company had an all-hands about it where the CEO of the company told everyone that we were not going to stop using Factual nor the smaller-than-allowed geofences because we, ourselves, were not the ones to produce those geofences. We were just a man in the middle helping to build a system to track people at high resolution.
Please try to reconcile with what your industry has and continues to destroy.
> For now, Everett’s Flock camera network remains offline, as the debate over transparency, privacy and public safety continues in the Legislature. The bill in Olympia that would put guidelines on Flock's data has passed in the Senate.
Edit: no dice!
Feel like that opened something in me..
Love the idea, but strange UX.
Edit: tried it again and I think the problem is that there's no feedback during scrolling during transition screens. When I scroll, I expect it to scroll with me, not go on its own path. So by the time it's possible to scroll again, I already tried everything and gave up. Made more confusing by the instructional text disappearing between transitions. Give a little bit more feedback during those transitions and I think the problem goes away.
This reporting was made possible because it's surprisingly easy to export recording start/stop time, file size, duration, notes, cop badge and model name from the underlying system with a couple clicks (this is true for any agency that uses axon: https://my.axon.com/s/article/Justice-Exporting-search-resul...). I threw that info into postgres, made a materialized view with a column that gets the filesize:duration ratio and filtered for videos with a certain ratio. I never did anything with it besides that article I posted before.
Here's an observable about the BWC analysis that went into the reporting (disclaimer: the observable is mid-iteration that never received a followup. the analysis itself is separate from the reporting): https://observablehq.com/d/9f09764dbbdfc4b5
Snobbery is a spectrum. You might not perceive your words as snobbery, but I do. We just have a different opinion of where you fall on that snobbery line.
But also, I hold the strong philosophy that it's important to actually read the documents that are being scanned. In that way, OCR tends to be more of a procedural step than anything.
Really, it ultimately depends on your goals.
Another way I've used image compression to identify cops that cover their body cameras while recording -- the filesize to length ratio reflects not much activity going on.
My point can be read as a recognition that ratcheting happens within the boring minutia and work is rarely done to recover from those ratchetings. Things like continuation of prosecution policies, legislation changes, staff changes, etc. There's a very strong tendency to consider those sorts of ratcheting effects as "just how things are" rather than recognizing that no, it hasn't always been that way.
Like, progressive politicians love to talk a big game about transparency, but when it comes down to it, they themselves contribute to systemic transparency failures. See Chicago's past two mayors' campaign transparency promises. Both have done a complete 180 on those promises and use never-losing lawyers to enforce that sort of thing. Chicago's mayor's office once asked me to do analysis of parking tickets' effects on poor folk... then a few months later accused my wanting a data dictionary of the parking tickets system so that I could modify the parking ticket system's data. That led to bad case law at the IL supreme court.
It's shit like that. The small-but-not-really-small things.
You are deeply, deeply misunderstanding my point if this is what you got from my post.
"Our guys" was tongue in cheek.
The new models are similarly better compared to tesseract v4. But what I'll say is that don't expect new models to be a panacea for your OCR problems. The edge case problems that you might be trying to solve (like, identifying anchor points, or identifying shared field names across documents) are still pretty much all problematic still. So you should still expect things like random spaces or unexpected characters to jam up your jams.
Also some newer models tend to hallucinate incredibly aggressively. If you've ever seen an LLM get stuck in an infinite, think of that.
Because otherwise, better than what we have now is an abysmal target and we should aim for better.