Just like no LLM will ever be able to understand what complementary colors are. Which is one of my favorite tests because it has a 100 % error rate.
LOL
The funniest part of this is not the fact that an LLM just got 3 for 3 correct, and therefore has a 100% success rate, thus proving you wrong again, but the fact that your “favorite test” would be one that you incorrectly believe “no LLM will ever be able to” do because…
Stop trying to make a screwdriver shoot laser beams, it’s not going to happen.
^ this you??? “My favorite test is to see if the screwdriver shoots laser beams” 🙃
And why didn’t you include the name of the model in your test?
I was using standard RGB hex codes, so I didn’t really need to specify because its the assumed default. If it was something different, I would need to specify. EDIT: oh I just realized you meant the LLM model, not the color model (RYB vs RGB). It was just from ChatGPT, thought the interface would be recognizable enough.
Looks like you don’t want me to try it myself. It would be interesting to do so.
Huh? What do you mean? Go try it!
Of course with values which don’t fit perfectly into 8 bit. What if I define the range from 0 to 47204 for each color channel instead
Yeah, so this is already a thing. 24-bit color (8 bits per color channel) already gives you 16,777,216 colors, which is pretty good, but if you want more precision, you can just use decimal (floating point) numbers for each channel, like sRGB(0.25, 0.5, 1.0) (https://en.wikipedia.org/wiki/SRGB) OR even better would be to use oklch (https://en.wikipedia.org/wiki/Oklab_color_space). This is a solved problem. Or you cold just define your range as 0 to 47204.
So… we’ve gone from “no LLM will ever be able to understand what complementary colors are” to “b-b-but what about arbitrary color models I make up??” And yeah, it will handle those too, you just have to tell it what it is when you prompt it.
Each time I ask any LLM what the complementary color to red is. Then I always get green as answer instead of cyan (With cyan being the only correct answer). And a completely wrong explanation about what complementary colors are based on digital screens.
🤦 Oh… oh wow, I was giving you way more credit than what you actually meant. You do realize there is more than one color model? https://en.wikipedia.org/wiki/Complementary_colors#In_different_color_models You probably should read the explanation about complementary colors based on digital screens that they are providing to you (or just pay attention in elementary art class), because you might actually learn something new.
Red Yellow Blue and Cyan Yellow Magenta are both subtractive color models. RGB is an additive color model.
😂 alright well, you’ve been corrected and proven wrong, with sources and screenshots. And clearly you’re getting a teeny bit upset over it. Sorry! There’s nothing wrong with learning something new, and its okay that you had made a mistake.
This was a fun rabbit hole to go down! I tend to agree with most of the takes here on Lemmy but the complete AI derision is pretty wild and unfounded in reality. I have plenty of concerns about the tech but to say it’s useless you’d have to really not even have tried it out to see for yourself. I appreciate your patience, dedication to the truth, willingness to explain, and experimental attitude here.
deleted by creator
LOL
The funniest part of this is not the fact that an LLM just got 3 for 3 correct, and therefore has a 100% success rate, thus proving you wrong again, but the fact that your “favorite test” would be one that you incorrectly believe “no LLM will ever be able to” do because…
^ this you??? “My favorite test is to see if the screwdriver shoots laser beams” 🙃
deleted by creator
I was using standard RGB hex codes, so I didn’t really need to specify because its the assumed default. If it was something different, I would need to specify.EDIT: oh I just realized you meant the LLM model, not the color model (RYB vs RGB). It was just from ChatGPT, thought the interface would be recognizable enough.Huh? What do you mean? Go try it!
Yeah, so this is already a thing. 24-bit color (8 bits per color channel) already gives you 16,777,216 colors, which is pretty good, but if you want more precision, you can just use decimal (floating point) numbers for each channel, like sRGB(0.25, 0.5, 1.0) (https://en.wikipedia.org/wiki/SRGB) OR even better would be to use oklch (https://en.wikipedia.org/wiki/Oklab_color_space). This is a solved problem. Or you cold just define your range as 0 to 47204.
So… we’ve gone from “no LLM will ever be able to understand what complementary colors are” to “b-b-but what about arbitrary color models I make up??” And yeah, it will handle those too, you just have to tell it what it is when you prompt it.
deleted by creator
Green is the correct answer in the RYB color model, which is traditionally used in art and most commonly taught in schools.
And… wait for it…
And an open-weight model (qwen3:32b)
So you’re:
deleted by creator
😂 multiple LLMs literally gave the exact answer that you claim they can’t correctly give, on the very first try. Checkmate.
deleted by creator
🤦 Oh… oh wow, I was giving you way more credit than what you actually meant. You do realize there is more than one color model? https://en.wikipedia.org/wiki/Complementary_colors#In_different_color_models You probably should read the explanation about complementary colors based on digital screens that they are providing to you (or just pay attention in elementary art class), because you might actually learn something new.
Red Yellow Blue and Cyan Yellow Magenta are both subtractive color models. RGB is an additive color model.
https://en.wikipedia.org/wiki/RYB_color_model
Try giving the LLM the hex color code and the color model you’re using that code in, and it will give you the correct complementary color.
deleted by creator
There is no one “correct” color circle. And your misguided beliefs about color theory do not have anything to do with LLMs.
By the way, they’re called “additive colors”, not “active colors”. 🙃
deleted by creator
😂 alright well, you’ve been corrected and proven wrong, with sources and screenshots. And clearly you’re getting a teeny bit upset over it. Sorry! There’s nothing wrong with learning something new, and its okay that you had made a mistake.
This was a fun rabbit hole to go down! I tend to agree with most of the takes here on Lemmy but the complete AI derision is pretty wild and unfounded in reality. I have plenty of concerns about the tech but to say it’s useless you’d have to really not even have tried it out to see for yourself. I appreciate your patience, dedication to the truth, willingness to explain, and experimental attitude here.