• 4 Posts
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Joined 3 years ago
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Cake day: June 9th, 2023

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  • I get what point you’re making in distinguishing between pedophile and ephebophile, but personally I don’t find the distinction particularly relevant. As an adult, the level of grossed out I feel at the prospect of sexual interactions with a young teenager Vs a literal child is approximately equal, because it’s not their physical attributes that cause ick, but rather the exploitation and power dynamics involved.

    Edit: I guess what I’m arguing is that in practice, we see the term “pedophilia” used as an umbrella term that encompasses pedophilia, hebephilia and ephebophilia, and I think that is a reasonable use of the term. It does muddy the waters a tad, given that pedophilia does still have its more specific use of referring to sexual attraction to pre-pubescent children, but I don’t think that an issue in the majority of contexts. When it comes to the law, an adult having sex with a child is equally illegal as an adult having sex with a 15 year old. Sure, we can split this hair and distinguish between the terms, but we don’t need to



  • I see your point, but as you say, there would still be the tradeoff of missing more recent stuff. That might only involve missing a couple of years’ worth of stuff now, but AI isn’t going away any time soon, so it would mean that there’d be an increasing amount of human made music not being archived; One of the things I like about Anna’s archive is that they seem to look at this problem as a long term, informational infrastructure kind of way, so I imagine they wouldn’t be keen on stopping the archive at 2023.

    It seems they’ve opted for a different tradeoff instead: lower popularity songs are archived at a lower bitrate, and even the higher popularity stuff has some compression. Some archives go for quality, and thus prioritise high quality FLACs, so Anna’s archive are aiming to fulfill a different niche. I can respect that.


  • I agree with the ethical standpoint of banning Generative AI on the grounds that it’s trained on stolen artist data, but I’m not sure how tenable “trained on stolen artist data” is as a technical definition of what is not acceptable.

    For example, if a model were trained exclusively on licensed works and data, would this be permissible? Intuitively, I’d still consider that to be Generative AI (though this might be a moot point, because the one thing I agree with the tech giants on is that it’s impractical to train Generative AI systems on licensed data because of the gargantuan amounts of training data required)

    Perhaps it’s foolish of me to even attempt to pin down definitions in this way, but given how tech oligarchs often use terms in slippery and misleading ways, I’ve found it useful to try pin terms down where possible



  • I’m not so much talking about machine learning being implemented in the final game, but rather used in the development process.

    For example, if I were to attempt a naive implementation of procedurally generated terrains, I imagine I’d use noise functions to create variety (which I wouldn’t consider to be machine learning). However, I would expect that this would end up producing predictable results, so to avoid that, I could try chucking in a bunch of real world terrain data, and that starts getting into machine learning.

    A different, less specific example I can imagine a workflow for is reinforcement learning. Like if the developer writes code that effectively says "give me terrain that is [a variety of different parameters], then when the system produces that for them, they go “hmm, not quite. Needs more [thing]”. This iterative process could, of course, be done without any machine learning, if the dev was tuning the parameters themselves at each stage, but it seems plausible to me that it could use machine learning (which would involve tuning model hyperparameters rather than parameters).

    You make a good point about procedural generation at runtime, and I agree that this seems unlikely to be viable. However, I’d be surprised if it wasn’t used in the development process though in at least some cases. I’ll give a couple of hypothetical examples using real games, though I emphasise that I do not have grounds to believe that either of these games used machine learning during development, and that this is just a hypothetical pondering.

    For instance, in Valheim, maps are procedurally generated. In the meadows biome, you can find raspberry bushes. Another feature of the meadows biome is that it occasionally has large clearings that are devoid of trees, and around the edges of these clearings, there is usually a higher rate of raspberry bushes. When I played, I wondered why this was the case — was it a deliberate design decision, or just an artifact of how the procedural generation works? Through machine learning, it could in theory, be both of these things — the devs could tune the hyperparameters a particular way, and then notice that the output results in raspberry bushes being more likely to occur in clusters on the edge of clearings, which they like. This kind of process would require any machine learning to be running at runtime

    Another example game is Deep Rock Galactic. I really like the level generation it uses. The biomes are diverse and interesting, and despite having hundreds of hours in the game, there are very few instances that I can remember seeing the level generation being broken in some way — the vast majority of environments appear plausible and natural, which is impressive given the large number of game objects and terrain. The level generation code that runs each time a new map is generated has a heckton of different parameters and constraints that enable these varied and non-broken levels, and there’s certainly no machine learning being used at runtime here, but I can plausibly imagine machine learning being useful in the development process, for figuring out which parameters and constraints were the most important ones (especially because too many will cause excessive load times for players, so reducing that down would be useful).

    Machine learning certainly wouldn’t be necessary in either of these examples, but it could be something that could make certain parts of development easier.



  • Might also be a context switching thing

    Like, when I have a dedicated space to go for work, then I find that really helps me to get into the right headspace. My productivity has always been shit when I’ve lived somewhere that doesn’t have enough space to do this.

    Maybe what’s happening is that the different language forces you to be in a different headspace, which for some reason, helps you to focus better.

    This theory is weakened somewhat by the fact that your mother tongue is Portuguese, and you don’t find your focus to be improved by English.

    It does feel intuitively plausible to me that there is some underlying linguistic thing going on here. There might be some research studying the link between different languages and ADHD experiences, because it does seem like there’s something interesting there. If there isn’t currently any such research, I have no doubt that it’s just because it hasn’t been done yet (the wide domain of “academic research on autism and/or ADHD that respects the personhood of the people being studied” is unfortunately, a relatively recent development, but I have been pleased to see that it has been growing rapidly in recent years). If I find anything, I’ll report back (which may be in many weeks or months)



  • This always irks me, like if you’re going to harvest my data, could you at least use some of your immense repository of data insights to improve your product? No? You’re just going to enclose the data commons in your ridiculous quest to make the line go up, without giving any value back to the people who facilitated your growth? Yeah, I thought that’d be the case. Disappointed, but not surprised that this is the case.

    The context in which this most often annoys me is that nearly every Tuesday, I go to a philosophy discussion group at a nearby pub. I usually get the route up on Google maps through Android Auto because the optimal route depends a lot on traffic, and each time, I have to manually type in the name of the pub.

    It especially annoys me when sometimes, on a day that isn’t Tuesday, the pub will be listed near the top of the suggested destinations when I first launch Google maps. I literally never go to that pub for any reason other than the philosophy group.

    It’s such a trivial thing to be annoyed by, but equally, it appears to me that actually giving useful suggestions in straightforward cases such as this is equally trivial. It reveals that they truly don’t give a fuck about improving products (and indeed, when it comes to Google’s offerings, so much of it has gotten worse. Google assistant and its voice recognition used to be way more reliable and powerful in the past. I first started using Android 10 years ago and I had so much fun tinkering with automation on my Nexus 6; there are things that I could do before that I no longer can, and it annoys me to no end)



  • Yeah, I’ve been seeing an increasing number of artists who are pro piracy, who basically say “steal our music, save your money, and if you want to support us, come to a gig and buy some merch”.

    I’ve also seen more and more artists staying off Spotify entirely. One such artist is the wonderful folk artist Lucy & Hazel . This was the first time I actually bought music in years, and a big part of that was because I wanted to support their active choice to stay off Spotify.

    An unexpected side effect of this is that because I’m aware these guys are situated less optimally for algorithmic discoverability, I find myself actively recommending them to people. It feels nice compared to the more passive mode of algorithmic music discovery