EuphoricPenguin

Morton up in here spreading free salt.

Not if Anna has anything to say about it…

Perhaps, but I sucked at touch typing when I was younger.

No idea; does autocorrect even exist in an inbuilt fashion on Windows? I’ve never really tried using anything like that.

Oh, and here’s a one-off test I just did without autocorrection turned on. With a few more tries, I’m sure I could get up to 100+.

Ironically, I can almost type as fast on my phone (102 WPM PB) as I can on most keyboards (110 WPM PB), and that’s with my weird improper method of touch typing. These scores are for the 15 second word test on MonkeyType.

The good ol’ Linus parrots. Squawk “Steve Burke is a bad journalist because he pointed out errors publicly that affected consumers.” Screech “Linus didn’t sell the employees internally on the idea that he and his wife were a substitute for HR, he auctioned it.”

More often than not, people who are passionate about something, such as Linux, take personal offense when someone says something incorrect or offensive about said thing. Oh, and blud is just to call someone a poser.

I love this comment so much. “You crossed Linux? Now you’ve crossed me, blud.”

To be fair, the comments and posts you leave are technically being collected for display across the lemmyverse. In that sense, there’s never going to be a zero data collection Lemmy client. Still, Liftoff currently has my vote. A decent little FOSS fork of Lemur, I believe.

Heck, even my college Sociology textbook from OpenStax basically has nuclear fear-mongering baked into one of the later sections.

Unfortunately, there’s still that one guy in the comments trying to say that hypothetical, largely unproven solutions are better for baseload than something that’s worked for decades.

I think it’s a very specific case that needs to be taken in a very narrow context; it’s essentially an innocent mistake that needs to be recognized as such. The moment you step outside of that, I see no reasonable arguments for decriminalizing anything.

Yeah, that’s fair. The early versions GPT3 kinda sucked compared to what we have now. For example, it basically couldn’t rhyme. RLHF or some of the more recent advanced seemed to turbocharge that aspect of LLMs.

I don’t really think it’s something people should do, but I can honestly see it happening to ordinary people if they aren’t thinking about what they’re doing.

Picking and choosing isn’t the game I want to play, I’m just highlighting that there are circumstances that can result in actually innocent people doing things without thinking. Pornographic content of any kind (drawings or otherwise) that depicts underage people in any context is something I think should be illegal and avoided at all costs, but I’m highlighting that there is edge-cases in everything.

I mean, perhaps in the most general sense that is technically true. For example, there have been cases about this that have come from parents taking pictures of their kids in the bathtub, even if the charges were eventually dropped. If that particular court case had gone differently, it might’ve set a very destructive precedent that served only to rip apart families.

Still, 99% of the cases that produce this material are done so in an exploitative and abusive context; definitely not arguing with that. No idea what Aaron was talking about in that particular link, but this is the one counterexample that I think of that is valid, assuming it went a different direction in court.

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.

Unless I’m mistaken, aren’t GANs mostly old news? Most of the current SOTA image generation models and LLMs are either diffusion-based, transformers, or both. GANs can still generate some pretty darn impressive images, even from a few years ago, but they proved hard to steer and were often trained to generate a single kind of image.

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.

If I remember correctly, 0 and 1 are considered falsy and truthy respectively, so it should be falsy and truthy and false which I believe would return false.

Tried it out to double-check, and the type of the first in the sequence is what ultimately is returned. It would still function the same way if you used it in a conditional, due to truthy/falsy values.