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Dems have to be in on it, that’s the only thing that makes sense. It isn’t Dem vs. Rep, it’s rich vs. poor :(
Dems have to be in on it, that’s the only thing that makes sense. It isn’t Dem vs. Rep, it’s rich vs. poor :(
If you put someone on a wall hook, would they then have been hung? Likewise if I suspend my painting with a noose, has the painting been hanged?
I fucking love beans
I said what you said verbatim out loud after I also spoke aloud the top comment, wild
What would be extremely rock and roll-- punk rock, even – is donating all of the proceeds from that show to pro-union efforts.
#DonateItDave, or something
I’m here to support.
Count #1: Guilty
Count #2: Guilty
Count #3: Guilty
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Count #6: Guilty
Count #7: Guilty
Count #8: Guilty
Count #9: Guilty
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Count #32: Guilty
Count #33: Guilty
Count #34: Guilty
The mishandling is indeed what I’m concerned about most. I now understand far better where you’re coming from, sincere thanks for taking the time to explain. Cheers
Thanks for the response! It sounds like you had access to a higher quality system than the worst, to be sure. Based on your comments I feel that you’re projecting the confidence in that system onto the broader topic of facial recognition in general; you’re looking at a good example and people here are (perhaps cynically) pointing at the worst ones. Can you offer any perspective from your career experience that might bridge the gap? Why shouldn’t we treat all facial recognition implementations as unacceptable if only the best – and presumably most expensive – ones are?
A rhetorical question aside from that: is determining one’s identity an application where anything below the unachievable success rate of 100% is acceptable?
Can you please start linking studies? I think that might actually turn the conversation in your favor. I found a NIST study (pdf link), on page 32, in the discussion portion of 4.2 “False match rates under demographic pairing”:
The results above show that false match rates for imposter pairings in likely real-world scenarios are much higher than those from measured when imposters are paired with zero-effort.
This seems to say that the false match rate gets higher and higher as the subjects are more demographically similar; the highest error rate on the heat map below that is roughly 0.02.
Something else no one here has talked about yet – no one is actively trying to get identified as someone else by facial recognition algorithms yet. This study was done on public mugshots, so no effort to fool the algorithm, and the error rates between similar demographics is atrocious.
And my opinion: Entities using facial recognition are going to choose the lowest bidder for their system unless there’s a higher security need than, say, a grocery store. So, we have to look at the weakest performing algorithms.
I like the idea of some numbers being popular hand gestures.
4 - Fuck you; 17 - Shaka (hang loose); 18 - Metal horns; 19 - “I love you”; 132 - Double fuck you
I use 8! My mom called me ridiculous for doing that :(
Yeah I got the impression it was a recoverable condition after a search found a bunch of guides for “unbricking” (Android phones). Semantics are the true enemy it seems
So then it didn’t run after the car wash – unless we’re ignoring the mandatory steps needed to get it working again, the headline is pretty accurate. Or are you considering “bricked” a permanent condition?
Crypto did unfortunate things to the space.
Wow do you even respect your dog?
It’s a freeing feeling when you decide to put money into what you want instead of what has the biggest ROI.
You could go for unst
and drop sick rave beats
unst unst unst
The cutoff after the comma suggests they’re about to embark on a long passionate rant, I think that’s the heart of the joke