Comment by prymitive
6 hours ago
I think the problem here is that LLMs aren’t really “intelligence models” but more like “knowledge models”. LLMs don’t “think”, they just use a clever trick to make it seem like they do. I might not understand a lot about current state of AI, but that’s what they seem to be. Give it information and ask to organise it and make links, and they’ll do it, but that’s it, they don’t continually try to get out of the knowledge box they’re at, they don’t even know there’s a box.
When you watch it solve complex problems and use the browser and do internet searches, and use the entire surface area of the console tools on a linux box every day the idea that there are no major Homomorphisms with biological thinking is just completely out of the question.
I also never understand what the difference between a thinking trick, and "real" thinking is supposed to be.
I used to agree with you but overtime I’ve changed my mind.
For reference I created predictive linguistics at Google in the first products and this is a many order scale up of that, with new complexities of course.
The best analogy I can give you is that it is a really advanced synthesis machine, which looks like human thought but is more of a hyper advance “replay” of human thought in various contexts.
Where you begin to see it fail is when it has no awareness of false paths in long walks, less awareness of getting stuck, and of course no unprompted intrinsic motivation.
This of course calls into question human thought being more than the rational mind but a mix of whole body input, biological needs, complex chemical behaviors and stored DNA information playing out after millions of years of evolution to build many different cooperating models of our “consciousnesses” and biological motivations .
Where as an LLM is more of an advance replay of the stored knowledge we bothered to record, synthesized into an execution in code.
It can do the things you’ve quoted because it has many recorded observations of those
Stick it in a robot and see how “smart” it is as everyday tasks. Give it a self oriented task and watch it mirror itself into oblivion.
It’s an advance thought extension system based on our history.
I feel like that's more saying they can't train on the fly, and also that serializing spatial data and world models is something we haven't really done fully.
For me all neural networks synthetic or otherwise are replay machines or stream prediction machines. Nerve signals in, and nerve signals out. If I create output signals to the muscle nerves like this when my eyes see signals like that, good things happen, i get a reward, so it happens again the next time. We have a a more complex messier architecture, but it seems pretty much the same in the input and outputs being linear signals.
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I frame it as a document extender trained on other documents. Any "mind" we perceive is an illusion in our heads as we experience a story about a character, and the "intelligence" is reflected at us back out of our collective writings.
I can make a program that writes a stories involving Santa Claus, and I can make another program that takes the hidden script and performs certain lines... but at the end of the day I have not made him real.
Feels like a distinction without a difference. What is any intelligence but a sum of its knowledge?
> Feels like a distinction without a difference. What is any intelligence but a sum of its knowledge?
In humans, there is a standard distinction between fluid intelligence (ability to solve problems in the absence of background information) and crystallised intelligence (having more facts and learned skills in your head)
Intent? Motivations? Incentives?