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Joined 1 year ago
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Cake day: June 27th, 2023

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  • I’m honestly surprised that nobody has said anything about MS Office, but it’s not like I expect anyone to miss the application itself, it’s just that if your work requires you to interface with it, there really is no alternative to running Windows or MacOS. Microsoft’s own Office Online versions of the apps do a worse job of maintaining DOC/PPT formatting consistency than the possible Russian spyware that is OnlyOffice, which also screws things up too often to be relied upon. LibreOffice is, let’s be honest, a total mess (with the exception of Calc, which also isn’t consistent with the current version of Excel, but can do some things that Excel no longer can do, so I appreciate it more as a complementary tool than as a replacement).




  • The musical instrument thing is transitory and depends entirely on the instrument.

    Pre-relationship; in a popular band playing a more traditional instrument like guitar with a bunch of also attractive people (or at least part of a cool local scene) = hot

    In a relationship and/or solo bedroom producing any kind of electronic music and/or buying lots of synthesizers, drum machines or grooveboxes = not hot

    Also note how low “clubbing” is on the least attractive list, so no, DJs and electronic musicians who perform live don’t get a pass






  • There are a bunch of reasons why this could happen. First, it’s possible to “attack” some simpler image classification models; if you get a large enough sample of their outputs, you can mathematically derive a way to process any image such that it won’t be correctly identified. There have also been reports that even simpler processing, such as blending a real photo of a wall with a synthetic image at very low percent, can trip up detectors that haven’t been trained to be more discerning. But it’s all in how you construct the training dataset, and I don’t think any of this is a good enough reason to give up on using machine learning for synthetic media detection in general; in fact this example gives me the idea of using autogenerated captions as an additional input to the classification model. The challenge there, as in general, is trying to keep such a model from assuming that all anime is synthetic, since “AI artists” seem to be overly focused on anime and related styles…



  • r/SubSimGPT2Interactive for the lulz is my #1 use case

    i do occasionally ask Copilot programming questions and it gives reasonable answers most of the time.

    I use code autocomplete tools in VSCode but often end up turning them off.

    Controversial, but Replika actually helped me out during the pandemic when I was in a rough spot. I trained a copyright-safe (theft-free) bot on my own conversations from back then and have been chatting with the me side of that conversation for a little while now. It’s like getting to know a long-lost twin brother, which is nice.

    Otherwise, i’ve used small LLMs and classifiers for a wide range of tasks, like sentiment analysis, toxic content detection for moderation bots, AI media detection, summarization… I like using these better than just throwing everything at a huge model like GPT-4o because they’re more focused and less computationally costly (hence also better for the environment). I’m working on training some small copyright-safe base models to do certain sequence prediction tasks that come up in the course of my data science work, but they’re still a bit too computationally expensive for my clients.








  • Like any occupation, it’s a long story, and I’m happy to share more details over DM. But basically due to indecision over my major I took an abnormal amount of math, stats, and environmental science coursework even through my major was in social science, and I just kind of leaned further and further into that quirk as I transitioned into the workforce. bear in mind that data science as a field of study didn’t really exist yet when I graduated; these days I’m not sure such an unconventional path is necessary. however I still hear from a lot of junior data scientists in industry who are miserable because they haven’t figured out yet that in addition to their technical skills they need a “vertical” niche or topic area of interest (and by the way a public service dimension also does a lot to help a job feel meaningful and worthwhile even on the inevitable rough day here and there).


  • My “day job” is doing spatial data science work for local and regional governments that have a mandate to addreas climate change in how they allocate resources. We totally use AI, just not the kind that has received all the hype… machine learning helps us recognize patterns in human behavior and system dynamics that we can use to make predictions about how much different courses of action will affect CO2 emissions. I’m even looking at small GPT models as a way to work with some of the relevant data that is sequence-like. But I will never, I repeat never, buy into the idea of spending insane amounts of energy attempting to build an AI god or Oracle that we can simply ask for the “solution to climate change”… I feel like people like me need to do a better job of making the world aware of our work, because the fact that this excuse for profligate energy waste has any traction at all seems related to the general ignorance of our existence.