Windows is the only OS that managed to go back in time and reach its alpha stage again. Fascinating.

I’ve also heard positive feedback from people who switched from Mac to Mint.

I would choose Mint.

And as in the previous experiment, it also had some self-awareness.

(Facepalm)

And you still ignore what I wrote. Because you can’t process how wrong you and your AI are.

Additive colors -> active light emitter. Which should be obvious. But yeah, you simply lack the ability to think beyond what AI tells you. You understand nothing. You’re nothing mote than a stochastic parrot yourself. Enjoy your daily rock.

All LLMs still claim that green is the complementary color to red…

And you showed that you don’t understand complementary colors, just like AI. Because the above color circle is wrong. Why? Because of tests like the afterimage test (Example: https://i.pinimg.com/originals/da/7c/fb/da7cfba87ffdc8f426953397162329b4.gif), proving that purple (like pictured above) can never be the complementary color to yellow, it always has to be a deep blue. It doesn’t matter if it’s additive colors or subtractive colors you’re using (Afterimage tests work both passive and active) because in the end, it’s all only about light hitting our L/M/S-cones and how our brains work when it comes to interpreting the signals from those cones (https://en.m.wikipedia.org/wiki/Metamerism_(color). Metamerism explains why engineers chose perceptually equidistant cyan/magenta/yellow for (simple) printing (“Subtractive colors”) and perceptually equidistant red/green/blue for active emitting devices like cameras and displays (“Active colors”). And if you now say “But bro, I see a green shifting towards blue in the afterimage test” - didn’t you wonderful AI tell you about the Abney effect? Weird. It’s all well known and documented on the web which has been used to train your wonderful AI. But yeah - without being able to understand all of that, there is no way your wonderful AI can tell you which one of all those color circles is the correct one (And there is only one because it does not violate the CIE 1931 color space). It’s up to you to either learn and understand - or to blindly follow a LLM which sticks to green being the complementary color to red. Because all the LLM can do is repeating the garbage it has been trained with. Because it’s nothing more than a stochastic parrot. Your choice.

And why didn’t you include the name of the model in your test? Looks like you don’t want me to try it myself. It would be interesting to do so. 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? What if I would use CMY(K) instead of RGB? A good “great” AI must be able to handle all of that. And of course correctly explain what complementary colors are (which you didn’t include either). So yeah - what you provided does not go beyond the output from htmlcolorcodes.com - a very simple website with very simple code. I doubt it requires much power either.

Funny. 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. So yeah - LLMs still fail miserably at language-based tests. And rearranging complex equations doesn’t work either.

And you don’t know what a circular argument is either…

No, 2+2 is never “about 4” nor is it 4 in most cases. It’s always exactly 4. And no LLM can ever come to this conclusion. LLMs fail at math in a truly spectacular way. 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.

So you don’t know how math works.

What makes you think that using single letters as tokens instead could teach a stochastic parrot to count or calculate? Both are abilities. You can’t create an ability only from a set of data no matter how much data you have. You can only make a model seem to have that ability. Again: All you can ever get out of it is something that resembles human language. There is nothing beyond/behind that, by design. Not even hallucinations. Whenever a LLM gives you the advice to eat a rock per day it still works. Because it outputs a correct sounding sentence purely and entirely based on probability. But counting and calculating are not based on probability which is something everyone who ever had a math class knows very well. No math teacher will let students guess the result of an equation.

As if a stochastic parrot could ever be able to count or calculate…

LLM work really well. You get something out of it that resembles human language. This is what it has been designed for and nothing else. Stop trying to make a screwdriver shoot laser beams, it’s not going to happen.