That whole bit about color blending and transparency and LLMs "not knowing colors" is hard to believe. I am literally using LLMs every day to write image-processing and computer vision code using OpenCV. It seamlessly reasons across a range of concepts like color spaces, resolution, compression artifacts, filtering, segmentation and human perception. I mean, removing the alpha from a PNG image was a preprocessing step it wrote by itself as part of a larger task I had given it, so it certainly understands transparency.

I even often describe the results e.g. "this fails when in X manner when the image has grainy regions" and it figures out what is going on, and adapts the code accordingly. (It works with uploading actual images too, but those consume a lot of tokens!)

And all this in a rather niche domain that seems relatively less explored. The images I'm working with are rather small and low-resolution, which most literature does not seem to contemplate much. It uses standard techniques well known in the art, but it adapts and combines them well to suit my particular requirements. So they seem to handle "novel" pretty well too.

If it can reason about images and vision and write working code for niche problems I throw at it, whether it "knows" colors in the human sense is a purely philosophical question.

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geraneum 8 days ago | parent [-]

> it wrote by itself as part of a larger task I had given it, so it certainly understands transparency

Or it’s a common step or a known pattern or combination of steps that is prevalent in its training data for certain input. I’m guessing you don’t know what’s exactly in the training sets. I don’t know either. They don’t tell ;)

> but it adapts and combines them well to suit my particular requirements. So they seem to handle "novel" pretty well too.

We tend to overestimate the novelty of our own work and our methods and at the same time, underestimate the vastness of the data and information available online for machines to train on. LLMs are very sophisticated pattern recognizers. It doesn’t mean what you are doing specifically is done in this exact way before, rather the patterns adapted and the approach may not be one of their kind.

> is a purely philosophical question

It is indeed. A question we need to ask ourselves.

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Uehreka 8 days ago | parent [-]

> We tend to overestimate the novelty of our own work and our methods and at the same time, underestimate the vastness of the data and information available online for machines to train on. LLMs are very sophisticated pattern recognizers.

If LLMs are stochastic parrots, but also we’re just stochastic parrots, then what does it matter? That would mean that LLMs are in fact useful for many things (which is what I care about far more than any abstract discussion of free will).

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samrus 8 days ago | parent [-]

We're not just stochastic parrots though, we can parrot things stochastically when that has utility, but we can also be original. The first time that work was done, it was sone by a person, autonomously. Current LLMs couldnt have done it the first time

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Nevermark 7 days ago | parent [-]

They are much more than stochastic parrots.

I have never understood the stochastic parrot interpretation. LLMs (and general deep learning models) are not statistical/stochastic based models. Statistics trivially apply, as they apply to all measurements of judge-able behavior. But the models do not perform statistical operations, nor do their architectures form tunable statistically driven systems.

They learn topological representations of relationships. Entirely different from statistics/stochastics.

Within their "style" of cognition, LLMs are very creative. They readily propose solutions to problems involving uncommon or unique combinations of disparate topics.

Coming up with artificial examples is easy (and they come up naturally for me all the time).

I think the best characterization of LLM knowledge, reasoning and creativity is: extremely wide (in ability to weave topics and communication constraints - one shot), but somewhat shallow (not being able to reason too deep.)

Within those bounds, they far far exceed human capabilities.

	▲	geraneum 7 days ago \| parent [-]
		> LLMs (and general deep learning models) are not statistical/stochastic based models. Statistics trivially apply, as they apply to all measurements of judge-able behavior. But the models do not perform statistical operations, nor do their architectures form tunable statistically driven systems. And just like a LLM, confidently wrong.