Very true. In my opinion, in case there is a way to extract "Semantic Clouds of Words", i.e given a particular topic, navigate semantic clouds word by word, find some close neighbours of that word, jump to a neighbour of that word and so on, then LLMs might not seem that big of a deal.
I think LLMs are "Semantic Clouds of Words" + grammar and syntax generator. Someone could just discard the grammar and syntax generator, just use the semantic cloud and create the grammar and syntax by himself.
For example, in writing a legal document, a slightly educated person on the subject, could just use the relevant words put into an empty paper, fill in the blanks of syntax and grammar, alongside with the human reasoning which is far superior than any machine reasoning, till today at least.
The process of editing the GPT* generated documents to fix reasoning is not a negligible task anyway. Sam Altman mentioned that: "the machine has some kind of reasoning", not a human reasoning ability by any means.
My point is, that LLMs are two programs fused into one, "word clouds" and "syntax and grammar", sprinkled with some kind of poor reasoning. Their word clouding ability, is so unbelievable stronger than any human it fills me with awe every time i use it. Everything else is, just whatever!
I think they're much more than that. Or rather, if they're "Semantic Cloud of Words", they're still a hundred thousand dimensional clouds of words, and in those hundred thousand dimensions, any relationship you can think of, no matter how obscure, ends up being reflected as proximity along some subset of dimensions.
Looking at it this way, I honestly wouldn't be surprised if that's exactly how "System 1" (to borrow a term from Kahneman) in our brains works.
What I'm saying is:
> In my opinion, in case there is a way to extract "Semantic Clouds of Words", i.e given a particular topic, navigate semantic clouds word by word, find some close neighbours of that word, jump to a neighbour of that word and so on, then LLMs might not seem that big of a deal.
It may be much more of a deal than we'd naively think - it seems to me that a lot of what we'd consider "thinking" and "reasoning" can be effectively implemented as proximity search in a high-dimensional enough vector space. In that case, such extracted "Semantic Cloud of Words" may turn out to represent the very structure of reasoning as humans do it - structure implicitly encoded in all the text that was used as training data for the LLMs.
>if they're "Semantic Cloud of Words", they're still a hundred thousand dimensional clouds of words, and in those hundred thousand dimensions, any relationship you can think of, no matter how obscure, ends up being reflected as proximity along some subset of dimensions.
Yes, exactly that. That's what GPT4 is doing, over billions of parameters, and many layers stacked on top of one another.
Let me give you one more tangible example. Suppose Stable Diffusion had two steps of generating images with humans in it. One step, is taking as input an SVG file, with some simple lines which describe the human anatomy, with body position, joints, dots as eyes etc. Something very simple xkcd style. From then on, it generates the full human which corresponds to exactly the input SVG.
Instead of SD being a single model, it could be multimodal, and it should work a lot better in that respect. Every image generator suffers from that problem, human anatomy is very difficult to get right.[1] The same way GPT4 could function as well. Being multimodal instead of a single model, with the two steps discreet from one another.
So, in some use cases, we could generate some semantic clouds, and generate syntax and grammar as a second step. And if we don't care that much about perfect syntax and grammar, we feed it to GPT2, which is much cheaper to run, and much faster. When i used the paid service of GPT3, back in 2020, the Ada model, was the worst one, but it was the cheapest and fastest. And it was fast. I mean instantaneous.
>the very structure of reasoning as humans do it
I don't agree that the machine reasons even close to a human as of today. It will get better of course over time. However in some not so frequent cases, it comes close. Some times, it seems like it, but only superficially i would argue. Upon closer inspection the machine spits out non sense.
[1] Human anatomy, is very difficult to get right, like an artist. Many/all of the artists, point out the fact, that A.I. art doesn't have soul in the pictures. I share the same sentiment.
I think LLMs are "Semantic Clouds of Words" + grammar and syntax generator. Someone could just discard the grammar and syntax generator, just use the semantic cloud and create the grammar and syntax by himself.
For example, in writing a legal document, a slightly educated person on the subject, could just use the relevant words put into an empty paper, fill in the blanks of syntax and grammar, alongside with the human reasoning which is far superior than any machine reasoning, till today at least.
The process of editing the GPT* generated documents to fix reasoning is not a negligible task anyway. Sam Altman mentioned that: "the machine has some kind of reasoning", not a human reasoning ability by any means.
My point is, that LLMs are two programs fused into one, "word clouds" and "syntax and grammar", sprinkled with some kind of poor reasoning. Their word clouding ability, is so unbelievable stronger than any human it fills me with awe every time i use it. Everything else is, just whatever!