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I had no idea FOSS tax software was a thing. Huh. I’ll try and play around with it at some point and let you know.
Someone interested in many things.
I had no idea FOSS tax software was a thing. Huh. I’ll try and play around with it at some point and let you know.
I guess these guys are just plain old tools.
Patching a newer version of the Youtube app resolved the issues with playback I was having.
Not if Anna has anything to say about it…
I read that in GLaDOS’s voice.
Tatsuro Yamashita was pretty impressive for several reasons: great singer/songwriter (he has some really solid range) and producer, S tier in singing English phonetically, and he’s good in Japanese, too.
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 feel like my obsession with Mavicas has just been dismissed as invalid.
We do something similar over at [email protected], but with photos. Of course, we’re using old floppy disk cameras, so the compression, aberration, and CCD weirdness is indeed authentic.
I forgot: are Lemmy’s active and hot sorts chronological? They’re pretty decent, but I do find stale content does get stuck on one that isn’t there on the other.
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.
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.
JS is one of the most fun programming languages ever created; how dare you slander its great name.
I’ve been meaning to watch more of the show, since I really love the Handyman Corner segments.