Comment by skeledrew
2 months ago
LLMs deliver pretty well on their intended functionality: they predict next tokens given a token history and patterns in their training data. If you want to describe that as fully intelligent, that's your call, but I personally wouldn't. And adding functionality that isn't directly related to improving token prediction is just bad practice in an already very complex creation. LLM tools exist for that reason: they're the handles, sheaths, sharpeners, etc for the knife. Teach those adults who're getting themselves cut to hold the knife by the handle and use the other accessories that improve user experience.
> given a token history and patterns in their training data. If you want to describe that as fully intelligent
No, I would call (an easy interpretation of) that an implementation of unintelligence. Following patterns is what an hearsay machine does.
The architecture you describe at the "token prediction" level collides with an architecture in which ideas get related with better justifications than frequent co-occurrance. Given that the outputs will be similar in form, and that "dubious guessers" are now in place, we are now bound to hurry towards the "certified guessers".
> Following patterns is what an hearsay machine does.
That's also how the brain works, at least partially. Primary differences are it takes and processes (trains itself on) raw sensory data instead of character tokens, and it continually does so for every conscious moment from at least birth until death.
> how the brain works, at least partially
With the difference, which have us go back to the original point, that the human mind has a crucial property of going beyond "pattern-based" intuition and check mental items lucidly and consciously.
> and it continually does so
It also persistently evaluates consciously and "store" and "learn" (which must be noted because it is the second main thing that LLMs don't do, after the problem of going past intuition).
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