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Cybercrooks use Raspberry Pi to steal ATM cash

Technology
23 20 5
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    B
    Tech archeology like this is pretty neat.
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    B
    Pretty sure they have Starlink antennas mounted onto Toyota trucks
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    dojan@pawb.socialD
    Don’t assume evil when stupidity I didn't, though? I think that perhaps you missed the "I don’t think necessarily that people who perpetuate this problem are doing so out of malice" part. Scream racism all you want but you’re cheapening the meaning of the word and you’re not doing anyone a favor. I didn't invent this term. Darker patches on darker skin are harder to detect, just as facial features in the dark, on dark skin are garder to detect because there is literally less light to work with Computers don't see things the way we do. That's why steganography can be imperceptible to the human eye, and why adversarial examples work when the differences cannot be seen by humans. If a model is struggling at doing its job it's because the data is bad, be it the input data, or the training data. Historically one significant contributor has been that the datasets aren't particularly diverse, and white men end up as the default. It's why all the "AI" companies popped in "ethnically ambiguous" and other words into their prompts to coax their image generators into generating people that weren't white, and subsequently why these image generators gave us ethnically ambigaus memes and German nazi soldiers that were black.
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    tal@lemmy.todayT
    While details of the Pentagon's plan remain secret, the White House proposal would commit $277 million in funding to kick off a new program called "pLEO SATCOM" or "MILNET." Please do not call it "MILNET". That term's already been taken. https://en.wikipedia.org/wiki/MILNET In computer networking, MILNET (fully Military Network) was the name given to the part of the ARPANET internetwork designated for unclassified United States Department of Defense traffic.[1][2]
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    B
    But do you also sometimes leave out AI for steps the AI often does for you, like the conceptualisation or the implementation? Would it be possible for you to do these steps as efficiently as before the use of AI? Would you be able to spot the mistakes the AI makes in these steps, even months or years along those lines? The main issue I have with AI being used in tasks is that it deprives you from using logic by applying it to real life scenarios, the thing we excel at. It would be better to use AI in the opposite direction you are currently use it as: develop methods to view the works critically. After all, if there is one thing a lot of people are bad at, it's thorough critical thinking. We just suck at knowing of all edge cases and how we test for them. Let the AI come up with unit tests, let it be the one that questions your work, in order to get a better perspective on it.
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    J
    It’s DEI’s fault!