he/him

Materials Science PhD candidate in Pittsburgh, PA, USA

My profile picture is the cover art from Not A Lot of Reasons to Sing, But Enough, and was drawn by Casper Pham (recolor by me).

  • 4 Posts
  • 31 Comments
Joined 1 year ago
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Cake day: June 7th, 2023

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  • In FFXIV, I’m in the post-Shadowbringers DLC content. I’ve taken a bit of a break from the MSQ to get the Nier-themed alliance raids

    Are you me? I'm just a bit into the post-ShB patches, and I just finished unlocking all three Nier raids. They're really fun (although I agree: challenging). If you happen to be on Crystal DC and want to party up for some raids or something, lmk!

    Think I might try a healer class next, just not sure which one

    As someone who is very much a non-healer main, I quite like Sage. My first healer to 90 was actually Scholar, but a lot of that had to do with the fact that I was really into Summoner for a while: when I'm going to heal I usually hop on Sage.



  • The Cosmic Wheel Sisterhood! It's a really good visual-novel-style game, but with the added element that you craft your own tarot-style divination deck and then draw cards from it during some conversations, and which cards you draw influence what kinds of readings you can give for people. It is established early on that since you were a kid your readings have never been wrong, and fittingly the game warns you early and repeatedly that your answers will affect your fate, dramatically. Well, no kidding! When I was playing yesterday I had a choice that I'd made hours earlier come back and bite me in the ass, hard. Almost made me want to quit and start over, but I've decided to see this play-through through and if by the end I still feel like I need to fix my mistakes I'll maybe play it a second time.

    tl;dr if you like beautiful pixel art, enigmatic beings from outside of space and time, witches, tarot, and/or choices that actually matter in your games, do give this one a go! I'm not done with it yet but I'd already love to chat with someone else who's played it!



  • Agreed. Strong (and effectively enforced) worker protections are just as important as tech-specific safety regulations. Nobody should feel like they need to put themselves into a risky situation to make work happen faster, regardless of whether their employer explicitly asks them to take that risk or (more likely) uses other means like unrealistic quotas to pressure them indirectly.

    There are certainly ways to make working around robots safer, e.g. soft robots, machine vision to avoid unexpected obstacles in the path of travel, inherently limiting the force a robot can exert, etc… And I’m all for moving in the direction of better inherent safety, but we also need to make sure that safer systems don’t become an excuse for employers to expose their workers to more risky situations (i.e. the paradox of safety).




  • That’s a real mood, yeah.

    I just recently decided to stick with mine. I was having a lot of doubts: feeling like I wasn’t making and progress, like I wouldn’t actually be able to finish the projects I started, impostor syndrome shit, etc. I’m happy I decided to stick with it. I just cleared some big milestones and I’m in the middle of a nice long vacation now, and I’m feeling excited again about my work.

    On the other side of things, I’ve got a friend who decided to leave his PhD program with a masters a few years ago. He’s now heading up product development for a robotics startup, doing quite well for himself.

    I don’t think there’re any wrong answers here. Do what will make you happiest. Maybe you just need a vacation, maybe you’re ready to move on. And remember that education is never wasted: even if you decide not to finish out the PhD, you’ve still learned a lot and that’s valuable with or without the piece of paper and title.

    Best wishes, friend, whichever way you decide to go ♥


  • For sure. They tend to do a good job communicating tricky science and math concepts as well. They interview experts in a coherent way, tend to take the time to properly set up the background for topics, and the writers there seem to really care about getting things right rather than being sensational. They’re one of my favorite sites for stories about math and science honestly.

    I haven’t had a chance to read the article linked in this post yet, but I’ll be sitting in an airport in a few hours (I really need to go to sleep now) and I’ll look forward to reading it then!


  • It seems like you’re working under the core assumption that the trained model itself, rather than just the products thereof, cannot be infringing?

    Generally if someone else wants to do something with your copyrighted work – for example your newspaper article – they need a license to do so. This isn’t only the case for direct distribution, it includes things like the creation of electronic copies (which must have been made during training), adaptations, and derivative works. NYT did not grant OpenAI a license to adapt their articles into a training dataset for their models. To use a copyrighted work without a license, you need to be using it under fair use. That’s why it’s relevant: is it fair use to make electronic copies of a copyrighted work and adapt them into a training dataset for a LLM?

    You also seem to be assuming that a generative AI model training on a dataset is legally the same as a human learning from those same works. If that’s the case then the answer to my question in the last paragraph is definitely, “yes,” since a human reading the newspaper and learning from it is something that, as you say, “any intelligent rational human being” would agree is fine. However, as far as I know there’s not been any kind of ruling to support the idea that those things are legally equivalent at this point.

    Now, if you’d like to start citing code or case law go ahead, I’m happy to be wrong. Who knows, this is the internet, maybe you’re actually a lawyer specializing in copyright law and you’ll point out some fundamental detail of one of these laws that makes my whole comment seem silly (and if so I’d honestly love to read it). I’m not trying to claim that NYT is definitely going to win or anything. My argument is just that this is not especially cut-and-dried, at least from the perspective of a non-expert.







  • With all due respect to Penrose – who is indisputably brilliant – in probability when you start to say things like, “X is 10^10^100 times more likely than Y,” it’s actually much more likely that there’s some flaw in your priors or your model of the system than that such a number is actually reflective of reality.

    That’s true even for really high probability things. Like if I were to claim that it’s 10^10^100 times more likely that the sun will rise tomorrow than that it won’t, then I would have made much too strong a claim. It’s doubly true for things like the physics of the early universe, where we know our current laws are at best an incomplete description.



  • Maybe all of those PhD students would have their time better spent on this task than pretending, as if often the case, they’ve done some original work on an important theory that’s found something “for the first time”.

    I mean I’m personally biased as a PhD student myself, but I think this is a great idea. I made the core of my project to basically take a picture of a phenomenon that has been inferred from spectroscopy but not observed directly. So verification, not exactly replication, but same idea. Turns out that doing something like this is very hard and makes a worthy PhD project. (I haven’t managed it yet, and am starting to wonder if my eventual paper might actually end up being in support of the null hypothesis…)

    But I’m also not looking to go into academia after I graduate, so I’m not to worried about trying for something high impact or anything like that. I think for someone angling to be a professor the idea of a replication or verification project may be a harder sell, which is largely down to the culture of academia and how universities do their hiring of post-docs and such. I mean, even in this case more people are still going to be familiar with the names of Lee and Kim than any of the researchers who put in work on replication studies (can you name any of them without checking the article?).

    tl;dr definitely a worthy goal and replications should absolutely be encouraged, but it’s going to take a while to change the whole academic culture to reinforce that they’re valuable contributions.