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Joined 1 year ago
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Cake day: June 24th, 2023

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  • You don’t accept it, because that’s bullshit. You also don’t accept that it’s somehow your fault that society (and your employer) is okay with that kind of injustice.

    I think there are two sane choices, you named one that’s really a good idea cause you do not have to take that shit.

    The other one would be sharing this situation with other nurses, forming a union or joining one, and going on strike. Letting the hospital see how well it functions when only those lazy doctors doing 1% of the necessary work and getting 2 thirds of the cake show up.




  • The brain truly is a fucked up machine that wants itself to feel as terrible as possible. The way it does this is by giving large short term rewards to stuff that make you feel worse and worse long term.

    Working out and living healthily is not about seducing people, it’s about making yourself feel better by engaging in stuff that yields long term rewards, even though they feel like fruitless efforts in the moment to moment gameplay of dopamine.

    Having gone there and back my experience is that giving up feels really good, but in a much more real sense it feels terrible. And just reading your post I can see you feel terrible.

    The good news is when you’re that low, any sustained effort can make you feel a bit better. Seeking professional help is one that’s a bit hard to start but a bit easier to ritualize into a habit.











  • That’s absolutely true, generative AI is mostly a parlor trick with very few applications beyond placeholder art and faster replies to emails. But even for your kind of engineering problem, there’s still a big issue that’s often disregarded.

    If we keep your example of an AI for a city grid, an important aspect of this type of engineering problem is guaranteeing that the system has as few catastrophic failures as possible (usually guaranteeing less than 1 for every 109 hours of uptime for systems where catastrophic means a certain quantity of dead bodies or high monetary costs, like a city grid, train signalization, flight control…). AI models may very well end up being discarded in those problems because even if you observe a better accuracy in simulations and experiments, mathematically proving this 109 figure is impossible because we don’t know how they work. Proving a threshold experimentally can happen, but a 109 number would require something like centuries of concurrent testing in every city in the world… I’ve just had a class with this example for trains. They were testing a system that reads signalization with a camera in order to move towards a more autonomous train. Deep learning performed better that classical image processing, but image processing allows you to prove that the train won’t misread less than x% of the time with way higher certainty than a black box, so they had to go with that if they ever wanted to pass safety certifications.

    So I guess deep learning explainability might be a more significant challenge even that finding a dataset that isn’t racially biased…