• 4 Posts
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Joined 2 years ago
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Cake day: June 15th, 2023

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  • This phenomenon is primarily due to fears of high repair costs, lack of technical information, and long lead times for replacement parts.

    Vehicles that use batteries as structural elements are more prone to being totaled by insurance companies.

    I think you’re missing what I’m saying here. I’m pointing out that Chinese auto makers don’t have the same processes as more experienced companies. They’re just slinging out cars into foreign markets with almost no extra work.

    Besides, the article didn’t say the cars are “fine”, it quoted someone saying that they’ve seen some cars that would have been fixed quickly if it was a domestic brand because of part availability.


  • I don’t understand why you’re referring to China’s regulations all the time. They are irrelevant.

    I wish they were, but those cars are made in China. There’s a lot that gets looked the other way.

    And someone at Tesla said recently in an interview that they wanted to do a certain thing with the Cybertruck but couldn’t because “we couldn’t get the regulation changed on that one”. (I don’t remember what that specific thing was)

    Aside from the batteries and fake auto-pilot, the non-Cybertruck Tesla’s have a very good track record.








  • You’re introducing an argument as a way to undermine the viewpoint that’s opposite to yours.

    No one said it’s fine “when we do it”. That’s not the point being discussed.

    The other bigger issue here is that these new cars are coming from a region that has a horrendous track record for safety and quality. EVs when done right are still a considerable risk with battery fires, but the ones manufactured in China are much worse for quality and safety. In the next few years, as these cars flood markets around the world, it will be a massive issue.







  • people with totally different facial structures get identified as the same person all the time with the “AI” facial recognition

    All the time, eh? Gonna need a citation on that. And I’m not talking about just one news article that pops up every six months. And nothing that links back to the UCLA’s 2018 misleading “report”.

    I’m assuming Apple’s software is a purpose built algorithm that detects facial features and compares them, rather than the black box AI where you feed in data and it returns a result.

    You assume a lot here. People have this conception that all FR systems are trained blackbox models. This is true for some systems, but not all.

    The system I worked with, which ranked near the top of the NIST FRVT reports, did not use a trained AI algorithm for matching.


  • 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?

    It’s a good question, and I don’t have the answer to it. But a good example I like to point at is the ACLU’s announcement of their test on Amazon’s Rekognition system.

    They tested the system using the default value of 80% confidence, and their test resulted in 20% false identification. They then boldly claimed that FR systems are all flawed and no one should ever use them.

    Amazon even responded saying that the ACLU’s test with the default values was irresponsible, and Amazon’s right. This was before such public backlash against FR, and the reasoning for a default of 80% confidence was the expectation that most people using it would do silly stuff like celebrity lookalikes. That being said, it was stupid to set the default to 80%, but that’s just hindsight speaking.

    My point here is that, while FR tech isn’t perfect, the public perception is highly skewed. If there was a daily news report detailing the number of correct matches across all systems, these few showing a false match would seem ridiculous. The overwhelming vast majority of news reports on FR are about failure cases. No wonder most people think the tech is fundamentally broken.

    A rhetorical question aside from that: is determining one’s identity an application where anything below the unachievable success rate of 100% is acceptable?

    I think most systems in use today are fine in terms of accuracy. The consideration becomes “how is it being used?” That isn’t to say that improvements aren’t welcome, but in some cases it’s like trying to use the hook on the back of a hammer as a screw driver. I’m sure it can be made to work, but fundamentally it’s the wrong tool for the job.

    FR in a payment system is just all wrong. It’s literally forcing the use of a tech where it shouldn’t be used. FR can be used for validation if increased security is needed, like accessing a bank account. But never as the sole means of authentication. You should still require a bank card + pin, then the system can do FR as a kind of 2FA. The trick here would be to first, use a good system, and then second, lower the threshold that borders on “fairly lenient”. That way you eliminate any false rejections while still maintaining an incredibly high level of security. In that case the chances of your bank card AND pin being stolen by someone who looks so much like you that it tricks FR is effectively impossible (but it can never be truly zero). And if that person is being targeted by a threat actor who can coordinate such things then they’d have the resources to just get around the cyber security of the bank from the comfort of anywhere in the world.

    Security in every single circumstance is a trade-off with convenience. Always, and in every scenario.

    FR works well with existing access control systems. Swipe your badge card, then it scans you to verify you’re the person identified by the badge.

    FR also works well in surveillance, with the incredibly important addition of human-in-the-loop. For example, the system I worked on simply reported detections to a SoC (with all the general info about the detection including the live photo and the reference photo). Then the operator would have to look at the details and manually confirm or reject the detection. The system made no decisions, it simply presented the info to an authorized person.

    This is the key portion that seems to be missing in all news reports about false arrests and whatnot. I’ve looked into all the FR related false arrests and from what I could determine none of those cases were handled properly. The detection results were simply taken as gospel truth and no critical thinking was applied. In some of those cases the detection photo and reference (database) photo looked nothing alike. It’s just the people operating those systems are either idiots or just don’t care. Both of those are policy issues entirely unrelated to the accuracy of the tech.






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