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Cake day: June 9th, 2023

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  • Something I’ve thought about a bunch re: recommendation engines is the idea of a “sweet spot” that balances exploration and safety

    Though actually I should start by saying that recommendation engines tend to aim to maximise engagement, which is why manosphere type content is so prevalent on places like YouTube if you go in with a fresh account — outrage generates engagement far more reliably than other content. I’m imagining a world where recommendation algorithms may be able to be individually tailored and trained, where I can let my goals shape the recommendations. I did some tinkering with a concept like this in the context of a personal music recommender, and I gave it an “exploration” slider, where at maximum, it’d suggest some really out-there stuff, but lower down might give me new songs from familiar artists. That project worked quite well, but it needs a lot of work to untangle before I can figure out how and why it worked so well.

    That was a super individualistic program I made there, in that it was trained exclusively from data I gave it. One can get individual goals without having to rely on the data of just one person though - listenbrainz is very cool — its open source, and they are working on recommendation stuff (I’ve used listenbrainz as a user, but not yet as a contributor/developer)

    Anyway, that exploration slider I mentioned is an aspect of the “sweet spot” I mentioned at the start. If we imagine a “benevolent” (aligned with the goals of its user) recommendation engine, and say that the goal you’re after is you want to listen to more diverse music. For a random set of songs that are new to you, we could estimate how close they are to your current taste (getting this stuff into matrices is a big chunk of the work, ime). But maybe one of the songs is 10 arbitrary units away from the boundary of your “musical comfort zone”. Maybe 10 units is too much too soon, too far away from your comfort zone. But maybe the song that’s only 1 unit away is too similar to what you like already and doesn’t feel stimulating and exciting in the way you expect the algorithm to feel. So maybe we could try what we think is a 4 or 5. Something novel enough to be exciting, but still feels safe.

    Research has shown that recommendation algorithms can change affect our beliefs and our tastes [citation needed]. I got onto the music thing because I was thinking about the power in a recommendation algorithm, which is currently mostly used on keeping us consuming content like good cash cows. It’s reasonable that so many people have developed an aversion to algorithmic recommendations, but I wish I could have a dash of algorithmic exploration, but with me in control (but not quite so in control as what you describe in your options 3). As someone who is decently well versed in machine learning (by scientist standards — I have never worked properly in software development or ML), I think it’s definitely possible.


  • I agree with you about the core of the problem, but the reason the monopoly is the thing being focussed on is because that’s the legal basis against Google that we have right now (speaking as someone who enthusiastically followed the proceedings).

    The crucial bit now that Google has been deemed an illegal monopolist is how this gets resolved, because of the possible remedies to this situation, some are better for user privacy, and some are worse. This is an opportunity to do some real good here on that front.

    Unfortunately, as I understand it, actually getting to a solution will take time, because of how Google will try to haggle down whatever remedy is suggested. This seems likely to be easier to do under a Trump administration.





  • To some extent, I don’t.

    Which is to say that in and around my field (biochemistry), I’m pretty good at sort of “vibe checking”. In practice, this is just a subconscious version of checking that a paper is published in a legit journal, and having a sense for what kind of topics, and language is common. This isn’t useful advice though, because I acquired this skill gradually over many years.

    I find it tricky in fields where I am out of element, because I am the kind of person who likes to vet information. Your question about how to identify work as peer reviewed seems simple, but is deceptively complex. The trick is in the word “peer” — who counts as a peer is where the nuance comes in. Going to reputable journals can help, but even prestigious journals aren’t exempt from publishing bullshit (and there are so many junk journals that keeping up even within one field can be hard). There are multiple levels of “peer”, and each is context dependent. For example, the bullshit detector that I’ve developed as a biochemist is most accurate and efficient within my own field, somewhat useful within science more generally, slightly useful in completely unrelated academic fields. I find the trick is in situating myself relative to the thing I’m evaluating, so I can gauge how effective my bullshit detector will be. That’s probably more about reflecting on what I know (and think I know) than it is about the piece of material I’m evaluating.

    In most scenarios though, I’m not within a field where my background gives me much help, so that’s where I get lazy and have to rely on things like people’s credentials. One litmus test is to check whether the person actually has a background in what they’re talking about, e.g. if a physicist is chatting shit about biology, or a bioinformatician criticising anthropology, consider what they’re saying with extra caution. That doesn’t mean discount anyone who isn’t staying in their lane, just that it might be worthwhile looking into the topic further (and seeing who else is saying what they are, and what experts from the field are saying too).

    As I get deeper into my academic career, I’ve found I’m increasingly checking a person’s credentials to get a vibe check. Like, if they’re at a university, what department are they under? Because a biochemist who is under a physics department is going to have a different angle than one from the medical research side, for example. Seeing where they have worked helps a lot.

    But honestly a big part of it is that I have built up loose networks of trust. For example, I’m no statistician, but someone I respect irl referenced a blog of Andrew Gelman’s, which I now consider myself s fan of (https://statmodeling.stat.columbia.edu/). Then from that blog, I ended up becoming a fan of this blog, which tends to be about sociology. Trusting these places doesn’t mean I take them at face value for anything they say, but having that baseline of trust there acts as a sort of first pass filter in areas I’m less familiar with, a place to start if I want to learn about a perspective that I know the rough origin of.

    In the context of news, I might start to see a news outlet as trustworthy if I read something good of theirs, like this piece on 3M by ProPublica, which makes me trust other stuff they publish more.

    Ultimately though, all of these are just heuristics — imperfect shortcuts for a world that’s too complex for straightforward rules. I’m acutely aware of how little spare brain space I have to check most things, so I have to get lazy and rely on shortcuts like this. In some areas, I’m lucky to have friends I can ask for their opinion, but for most things, I have to accept that I can’t fact check things thoroughly enough to feel comfortable, which means having to try holding a lot of information at arms length and not taking it as fact. That too, takes effort.

    However, I got a hell of a lot smarter when I allowed myself to be more uncertain about things, which means sometimes saying “I don’t know what to make of that”, or “I think [thing] might be the case, but I don’t remember where I heard that, so I’m unsure”, or just straight up “I don’t know”. Be wary of simple and neat answers, and get used to sitting with uncertainty (especially in modern science research).




  • A friend of mine lived with an electrolysis tech for a while, and she got basically all her legs done for free over the course of multiple years. I experienced it a few times — I imagine the pain is similar to how a tattoo would hurt.

    For me, the cost was by far, the most expensive part. Sucks to be ginger











  • You may feel silly, but this little exchange is part of what I love about the internet. This time, I got the joke, but there have been some many times when I have been the person saying “I don’t get it”, or being relieved to see someone else expressing the sentiment, because that leads to explanations. So many jokes fly over my head, so it always makes me happy to see mini conversations like this