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.
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.
Yeah, that’s just not how neural networks work…
No math teacher will let students guess the result of an equation.
https://en.wikipedia.org/wiki/Universal_approximation_theoremActual math directly contradicts your beliefs. I know its trendy and you want to feel smarter than people who have spent literally decades researching NNs and staked billions of dollars developing it, but you’re wrong.
Your claim is like claiming that boolean circuits cannot do math because all they do is “true” or “false”.
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.
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? 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.
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.
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.
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.
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.
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.
Yeah, that’s just not how neural networks work…
https://en.wikipedia.org/wiki/Regression_analysis
You tried.
So you don’t know how math works.
https://en.wikipedia.org/wiki/Universal_approximation_theorem Actual math directly contradicts your beliefs. I know its trendy and you want to feel smarter than people who have spent literally decades researching NNs and staked billions of dollars developing it, but you’re wrong.
Your claim is like claiming that boolean circuits cannot do math because all they do is “true” or “false”.
And also, https://en.wikipedia.org/wiki/Recurrent_neural_network#Neural_Turing_machines recurrent neural networks are Turing complete when paired with memory, and therefore can be used to any calculations or computations that a conventional computer can do.
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.
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” 🙃
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.
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.
All LLMs still claim that green is the complementary color to red…
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.
🤦 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.
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.