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Cake day: August 15th, 2024

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  • The main problem with BYD cars is that they are heavily subsidizing by the Chinese government.

    If you remove those subsidies then those cars aren’t going to be very competitive. But the problem would be that by the time the Chinese government stopped subsidies, there wouldn’t be any competition left.

    Our best ways to counteract this would either be through heavy tariffs or by subsidizing our own companies in the west.

    MAGA wants to do the tariffs route which is basically a bandaid solution that would prevent the Chinese companies from owning the US market but it wouldn’t do anything outside of that. Plus it doesn’t solve is being competitive, it’s just covering its ears and “lalala”’ing the issue for later generations to deal with it. Which honestly, that tracks for basically their whole platform.

    If you do the subsidies route though, we’d have to make sure we’re not just constantly lining Musk’s pockets but Tesla is the company has the biggest head start. And Musk is a PoS but the devil’s credit is that our EV market wouldn’t exist without Tesla.

    IMO, we need to diversify our EV makers and help provide the capital to bootstrap it. And while that’s happening we need to not let cheap Chinese cars flood the market to undercut any chance we have. So basically we need a combination of both solutions.






  • john@lemmy.haley.ioOPtoFuck AI@lemmy.worldWhen A.I.’s Output Is a Threat to A.I. Itself
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    2 months ago

    This is a threat to any neural network that is being constantly trained. Hell it’s even a problem with our brain’s NN. We just call it “believing your own bullshit” or “getting high on your own supply”.

    The issue with NNs looking for cures or diseases (or anything that isn’t trained off of the internet) is that they are basically out of training data. They’ll need orders of magnitude more to get better and we just don’t have that. We haven’t figured out a way to get better off of less data and there’s no real movement on that front either.

    What we have right now is essentially a culmination of research that has been going on since the 1960’s that was finally able to be realized with us figuring out:

    • We can map our NN variables to a matrix
    • We can use linear algebra to optimize the loss on that matrix
    • We can leverage video cards to crunch the linear algebra
    • We have the largest data set ever created in order to get our loss lower than ever before