[ExI] [Extropolis] Quantitative Qualia and the Science of Redness

[Kelly Anderson]

Brent,

  In the vast world of knowledge sucked up by the AIs, there are very
specific mappings of colors to numeric equivalents that would enable AI to
"know" or "infer" what colors are despite having no eyes. Just think of the
color swatches at your local Home Depot. There are undoubtedly
international definitions (ANSI?) of colors, along with many other
standards. So I don't think this falls into your standard "qualia" argument
in the same way as "anger" or something a bit less quantifiable like that.

-Kelly


On Tue, Jun 17, 2025 at 5:34 PM Brent Allsop via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

> Hi Stuart,
> Yes, thanks for this reference.
> It just must be kept in mind, that though an abstract intelligence (words
> only) can discover and model color qualities, without a definition of the
> words being grounded with factual physical qualities it experiences first
> hand, they can't know what the words represent.
>
> [image: The-Strawberry-is-Red-0480-0310.jpg]
>
> On Sun, Jun 15, 2025 at 11:16 AM Stuart LaForge <avant at sollegro.com>
> wrote:
>
>> Here is an interesting study that reports using machine learning
>> techniques to analyze color qualia quantitatively. Basically they had
>> 426 people with typical color vision and 257 individuals who were color
>> blind take a computer survey where they judged the similarity of
>> color-pairs chosen randomly from a pool of 93 colors using a point
>> scale. The results were then used to train an artificial neural network
>> (ANN) by unsupervised pairwise alignment of individual's similarity data
>> for the color pairs without reference to the color name or label. In
>> other word, it was told to look for similarities based on the numerical
>> distance reported by each pair of individuals for each color pair,
>> without being told what the colors were and then used the alignments to
>> form clusters corresponding to a "color map". When the relative
>> differences between colors reported by the research subjects were
>> clustered without reference to the color, it nonetheless turned out that
>> clusters corresponded to the various color labels and the color maps of
>> the normally-sighted group were similar to one another. The color maps
>> of the color-blind people were, also, similar to one another. However,
>> the color maps of the color-sighted people were different from the color
>> maps of the color-blind people.
>>
>> https://www.cell.com/iscience/fulltext/S2589-0042(25)00289-5
>>
>>
>> https://www.lesswrong.com/posts/LYgJrBf6awsqFRCt3/is-red-for-gpt-4-the-same-as-red-for-you
>>
>> Using machine learning to analyze qualia like this is fascinating. Just
>> like an LLM can learn the contextual meaning of words without being
>> explicitly programmed with the definition of the words simply by
>> statistically analyzing the average numerical distances between words in
>> a corpus of text, this technique should allow AI to recognize and use
>> colors without being explicitly programmed with any particular
>> definition of say red. This would render the question of whether an AI
>> can truly see a color to be equivalent to whether an LLM actually
>> understands what it is saying.
>>
>> Brent, you have have a thing for both color qualia and surveys so this
>> paper should be right up your alley.
>>
>> Stuart LaForge
>>
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>>
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