3 ms·

"it has an understanding of its characteristics and description" It has no access to the characteristics. It literally only has access to text. It knows that

"it has an understanding of its characteristics and description" It has no access to the characteristics. It literally only has access to text. It knows that some text is associated with the word "cat", but it doesn't know what "fluffy" means, because it has never seen fluff. There is literally an infinite regress of meaning, where you never get out of language to actual meaning, unless you have some kind of experience of "fluff" (or something else which can be related to "fluff"). A is a bit like B which is a bit like C, which is like D, which is a mix of B and E, which is quite F-like. This never gets you to meaning, because the meaning is the sensation, or the experience. And it doesn't have that. (Blind people would be able to understand the physicallity of a cat, because they can still experience them in a variety of ways. Whether they can understand that the cat is white is down to the kind of blindness, but if they've never seen colour then probably not.)

mewpmewp2 2y ago
But it’s able to describe what a fluffiness is. And for us humans, the way I understand fluffiness, is about the sight and the touch senses. We have associated this sight input and this touch input with the label “fluffiness”. Now we throughout our life have experienced this input a lot, but technically this input in its original form is just lightwaves and a combination of our nerves physically touching the fluffy object. This could in theory be represented in many data formats, it could be represented as a combination or group of input tokens. So the understanding for us is just the association of this input to this label. I don’t see how this in particular would give us any special understanding over if just raw input was given to an LLM. It’s just a special case where we have had this input many times over and over. It seems arbitrary whether LLM has been fed this data in this format or in some other format, e.g. text as opposed to lightwaves or touch feedback. This extra input could make those models more intelligent because it could help it uncover hidden patterns, but it doesn’t seem like it would be the difference between understanding and not understanding. Text is just a meaningful compression of this sensory input data.

mewpmewp2 2y ago

But it’s able to describe what a fluffiness is. And for us humans, the way I understand fluffiness, is about the sight and the touch senses. We have associated this sight input and this touch input with the label “fluffiness”. Now we throughout our life have experienced this input a lot, but technically this input in its original form is just lightwaves and a combination of our nerves physically touching the fluffy object. This could in theory be represented in many data formats, it could be represented as a combination or group of input tokens. So the understanding for us is just the association of this input to this label. I don’t see how this in particular would give us any special understanding over if just raw input was given to an LLM. It’s just a special case where we have had this input many times over and over. It seems arbitrary whether LLM has been fed this data in this format or in some other format, e.g. text as opposed to lightwaves or touch feedback. This extra input could make those models more intelligent because it could help it uncover hidden patterns, but it doesn’t seem like it would be the difference between understanding and not understanding. Text is just a meaningful compression of this sensory input data.