Well, they do think, in that they produce output that is indistinguisable from thinking. If a person produced the same output to the same questions, we'd considered them thinking, maybe dumb sometimes, or paranoid at others, but still a thinking person.
We can argue about the quality and depth of the thinking that LLMs do (and we can say it's much cruder than a human thinking architecture, and of course not real time), but an LLM quacks like a thinking duck and looks like a thinking duck.
It does not receive dopamine as a result for a good answer, and a split second after finishing your answer the very same GPU is probably translated french or something for someone in another state. This is a language generator which has a corpus of information and has been tuned to appear correct.
It does for all intents and purposes. The rest is semantics and metaphysics.
That how we know another person is thinking too. By their output. We don't put a debugger into their brain.
>That how we know another person is thinking too. By their output. We don't put a debugger into their brain.
We know thoughts exist in their brain between the ones they choose to verbalize. Avoiding the distraction of solipsism.
For the LLM the "thinking" phase is just a preamble output for creating the answer. It just gets appended to the context window. Remove the context windows from your models and you will see how much of a mind they truly have. None.
Between answer it's thinking something else, somebody else asked :) You think that hardware sits idle?
That aside, what is a human thinking while unconscious? Does having been unconscious (e.g. for an operation, or fainting or whatever) means somebody doesn't think in general?
>We know thoughts exist in their brain between the ones the choose to verbalize
And we also know that if we run an LLM in a loop, didn't give it a cutoff for stopping their output, and didn't force it to print everything in the end, thoughts would exist in their "brain" too between the ones they chose to verbalize.
In fact, that's exactly how some LLMs in "thinking mode" appear.
When there’s no activity we declare them brain dead.
It's really just all mathematics and physics. There's no metaphysical anything about LLMs or how they do what they do. It's all just a bunch of fancy math "behind the curtain". An LLM can actually explain a lot of how it works "under the hood" if you ask it just the right questions in just the right ways. ;)
That's my point, but about the human brain as well. It's just a bunch of fancy math, just ones expressed with chemicals and electrical activations instead of, well, logic gates and electrical activations.
A human brain on the other hand is constantly processing 24/7 (even while you sleep), and always learning / changing until the day it dies. An LLM never changes (under the hood it's weights stay the same) unless you outright alter it's weights somehow (training / download an updated version of the model / etc). If you could somehow get an LLM to run constantly, in training mode, and give it ridiculous amounts of RAM and ultra-fast storage, and a series of fancy realtime inputs (audio, camera, etc) and maybe wheels so it could explore, and hands so it could do stuff, and access to it's own code so it could improve itself, it might eventually learn to closely approximate a really good simulation of actual thinking, but that's a bit of a scary road to go down. So many Sci-Fi movies and books end up going so very badly when the lead character starts playing in that particular sandbox. I doubt reality would go a whole lot better. ;)
Even a dog will learn from recent stimuli, these things don’t. The prompt just modifies.
Aside from the cost and slowness, nothing about an LLM prevents feeding recent stimuli in and using it to update the models/retrain.
The problem here is not doing tasks and outputting garbage output.
Aside from the cost, nothing about an LLM prevents feeding recent stimuli in and using it to update the models/retrain.
One can even do it in a makeshift way without modifying the weights, just keeping a complete version of any prompt + vector search on disk memory of it.
The point of the article stands: if providing more info than the model can access causes it to turn argumentative and refuse to comply, then it's a worse performance and a waste of money.
Basically the complaint is about how Claude is being trained.
You're so totally 1000% right about that, but they're really good at faking it, to such a degree that entirely too many people (even including some so-called "experts" in the field) have been utterly fooled by the mathematical "trickery" that performs the illusion of "intelligence".