Someone interested in many things.

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Joined 1 year ago
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Cake day: June 15th, 2023

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  • So a few tidbits you reminded me of:

    • You’re absolutely right: there’s what’s called an alignment problem between what the human thinks looks superficially like a quality answer and what would actually be a quality answer.

    • You’re correct in that it will always be somewhat of an arms race to detect generated content, as lossy compression and metadata scrubbing can do a lot to make an image unrecognizable to detectors. A few people are trying to create some sort of integrity check for media files, but it would create more privacy issues than it would solve.

    • We’ve had LLMs for quite some time now. I think the most notable release in recent history, aside from ChatGPT, was GPT2 in 2019, as it introduced a lot of people to to the concept. It was one of the first language models that was truly “large,” although they’ve gotten much bigger since the release of GPT3 in 2020. RLHF and the focus on fine-tuning for chat and instructability wasn’t really a thing until the past year.

    • Retraining image models on generated imagery does seem to cause problems, but I’ve noticed fewer issues when people have trained FOSS LLMs on text from OpenAI. In fact, it seems to be a relatively popular way to build training or fine-tuning datasets. Perhaps training a model from scratch could present issues, but generally speaking, training a new model on generated text seems to be less of a problem.

    • Critical reading and thinking was always a requirement, as I believe you say, but certainly it’s something needed for interpreting the output of LLMs in a factual context. I don’t really see LLMs themselves outperforming humans on reasoning at this stage, but the text they generate certainly will make those human traits more of a necessity.

    • Most of the text models released by OpenAI are so-called “Generative Pretrained Transformer” models, with the keyword being “transformer.” Transformers are a separate model architecture from GANs, but are certainly similar in more than a few ways.



  • I was incorrect; the first part of my answer was my initial guess, in which I thought a boolean was returned; this is not explicitly the case. I checked and found what you were saying in the second part of my answer.

    You could use strict equality operators in a conditional to verify types before the main condition, or use Typescript if that’s your thing. Types are cool and great and important for a lot of scenarios (used them both in Java and Python), but I rarely run into issues with the script-level stuff I make in JavaScript.






  • Well, framework has one cool side-effect of their repair-friendly approach: their laptop mainboard can be used as an SBC. I’ve seen a few projects use it in this way, and I believe they even sell an official plastic case for it. It’s a well-documented piece of computer hardware that is regularly refreshed and can be fitted easily into slim chassis.

    Oh, and another cool thing is that their screens have magnetic bezels. ThinkPads are a PITA to fix if you just want to replace an LCD panel; framework makes it trivial to keep the upper chassis and only replace the part that’s actually broken. That’s the real pitch with Framework: replace anything easily and upgrade your computer for only the cost of the mainboard or socketable component. Some of their newer devices have a socketable PCIe expansion bay, which could be used for things like socketable GPU upgrades.