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Cake day: June 16th, 2023

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  • All the new ideas in Starfield fall into one of two categories:

    • The technology doesn’t exist to implement it.
    • The talent at Bethesda is incredibly ill-suited to implement it.

    The Bethesda response to fans saying their main storyline was trash was to make a game where the main storyline is the primary focus and draw of the game? That’s a bold move.

    The NG+ stuff is a cool idea, but again, Bethesda just fundamentally lacks the talent to implement it. You can’t hit what they were aiming for with a handful of gimmicks. I wouldn’t even trust the team behind New Vegas, or whoever writes at Larian, to do it justice.



    • Fallout 3 releases and it’s good
    • Fallout New Vegas releases and it’s great
    • Fallout 4 releases and it’s disappointing but it’s okay because it’s just a blip. They had some good new ideas in there, they were just balanced out in the other direction by a lot of bad ones. Bethesda’s track record is still solid, if somewhat tarnished.
    • Fallout 76 releases and it’s disappointing but that’s because they’ve never made (and shouldn’t have made) an MMO before. A lot of the coverage is centred around the shoddy launch, which doesn’t really matter for a non-MMO title.







  • powerful isn’t the same as well-structured

    it was written to be a language that anybody could read or write as well as english, which just like every other time that’s been tried, results in a language that’s exactly as anal about grammar as C or Python except now it’s impossible to remember what that structure is because adding anything to the language to make that easier is forbidden

    when you write a language where its designers were so keen for it to remain human readable that they made deleting all rows in a table the default action, i don’t think “well structured” can be used to describe it





  • Primarily0617@kbin.socialtoTechnology@lemmy.mlethinically ambigaus
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    11 months ago

    the point of the original post is that artificially fixing a bias in training data post-training is a bad idea because it ends up in weird scenarios like this one

    your comment is saying that the original post is dumb and betrays a lack of knowledge because artificially fixing a bias in training data post-training would obviously only result in weird scenarios like this one

    i don’t know what your aim is here


  • any AI person training their algorithms on AI generated data is liable to get fired

    though this isn’t pertinent to the post in question, training AI (and by AI I presume you mean neural networks, since there’s a fairly important distinction) on AI-generated data is absolutely a part of machine learning.

    some of the most famous neural networks out there are trained on data that they’ve generated themselves -> e.g., AlphaGo Zero