LLMs don't have any senses, not merely fewer. LLMs don't have any concepts, not merely named ones.
A concept is a sensory-motor technique abstracted into a pattern of thought developed by an animal, in a spatio-temporal environment, for a purpose.
LLMs are just literally an ensemble of statistical distributions over text symbols. In generating text, they're just sampling from a compressed bank of all text ever digitised.
We aren't sampling from such a bank, we develop wholey non-linguistic concepts which describe the world, and it is these which language piggy-backs on.
The structure of symbols in a book has nothing to do with the structure of the world -- it is we who have stipulated their meaning: there's no meaning to `i`
> A concept is a sensory-motor technique abstracted into a pattern of thought developed by an animal, in a spatio-temporal environment, for a purpose.
Hi, since human linguistics is the sole repository of linguistic conceptualism, can you please show me which of the neurons is the "doggie" neuron, or the "doggie" cluster of neurons? I want to know which part of the brain represents the thing that goes wag-wag.
If you can't mechanically identify the exact locality of the mechanism within the system, it doesn't really exist, right? It's just a stochastic, probabilistic model, humans don't understand the wag-wag concept, they just have some neurons that are weighted to fire when other neurons give them certain input stimuli tokens, right?
This is the fundamental problem: you are conflating the glue language with the implementation language in humans too. Human concepts are a glue-language thing, it's an emergent property of the C-language structure of the neurons. But there is no "doggie" neuron in a human just like there is no "doggie" neuron in a neural net. We are just stochastic machines too, if you look at the C-lang level and not the glue-language level.
There's a pile of work on multimodal inputs to LLMs, generally finding that less training data is needed as image (or other) data is added to training.
Text is an extremely limited input stream, but an input stream nonetheless. We know that animal intelligence works well enough with any of a range of sensory streams, and different levels of emphasis on those streams - humans are somehow functional despite a lack of ultrasonic perception and primitive sense of smell.
And your definition of a concept is quite self-serving... I say that as a mathematician familiar with many concepts which don't map at all to sensory motor experiences.
Sensory-motor expression of concepts is primitive, yes, they become abstracted --- and yes the semantics of those abstractions can be abstract. I'm not talking semantics, i'm talking genesis.
How does one generate representations whose semantics are the structure of the world? Not via text token frequency, this much is obvious.
I dont think the thinnest sense of "2 + 2 = 4" being true is what a mathematician understands -- they understand, rather, the object 2, the map `+` and so on. That is, the proposition. And when they imagine a sphere of radius 4 containing a square of length 2, etc. -- I think there's a 'sensuous, mechanical, depth' that enables and permeates their thinking.
The intellect is formal only in the sense that, absent content, it has form. That content however is grown by animals at play in their environment.
A concept is a sensory-motor technique abstracted into a pattern of thought developed by an animal, in a spatio-temporal environment, for a purpose.
LLMs are just literally an ensemble of statistical distributions over text symbols. In generating text, they're just sampling from a compressed bank of all text ever digitised.
We aren't sampling from such a bank, we develop wholey non-linguistic concepts which describe the world, and it is these which language piggy-backs on.
The structure of symbols in a book has nothing to do with the structure of the world -- it is we who have stipulated their meaning: there's no meaning to `i`