this post was submitted on 11 Feb 2024
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No, they do not "create" their own "ideas". You can relax.
The concept of intelligence is tied to both information generation and information validation. LLMs are extremely fancy smoke and mirrors (very similar to what pseudo-random algorithms are in respect to entropy) meant to dazzle us, but they are not capable of generating new information (only to generate new combinations of existing information). They are, also, currently unable to reliably validate said information, which is why they so commonly, hilariously say trivially verifiably wrong things with the utmost apparent confidence.
While you're right, let's not incorrectly imply that ML (especially Deep Learning) has never come up with new ideas.
Yes, it comes up with new ideas from old information, but some have argued that's what humans do. We all stand on the shoulders of giants, who themselves tood on the shoulders of nature.
As I said:
They're basically fuzzing the goals we give them with random combinations of the information we feed them.
There is undeniably a value in that (we commonly use fuzzing for security and QA already, for example), but let's not kid ourselves that "AI" is somehow actually intelligent.
However, the question we ought to ask ourselves is: does actual intelligence really matter? If pseudo-randomness is good enough for cryptographic applications, is pseudo-intuition (eventually) coupled with proper rationalization (the only part of intelligence computers can systematically do) enough to replace most tasks humans do?
That's not really an accurate take of how machine learning typically works. Neural Networks (allegedly) learn in a way similar to how humans do, taking the data they are fed and building a weighted matrix of resolutions that seems most compatible. A historically interesting trait is that neural networks are often better pattern-discoverers than humans.
But note, the outcome of a neural network is NOT a "random combination of the information we feed them">
I feel like this is a hard question to answer since it is based off controversial takes about ML. I am not a brain-is-a-computer hypothesis adherent, but we're talking about specific learning mechanisms that are absolutely comparable to human learning. Is "the learning humans do" enough to replace "the learning humans do"? I would say obviously yes.
The implementation details of how they represent their information doesn't really matter.
It isn't random, it's selected (or "weighted", if you wanna be more precise, yes)
And don't confuse things. We're talking about intelligence here. Not learning. Learning can be done without intelligence (that's how insects can learn behavior) and intelligence can be done without learning.
My question was uniquely about information generation (since the validation part is fully rational, and can be very efficiently done by a machine).
Are we? Alright. Can you describe a definition test for intelligence that we could agree upon that humans pass and no NN or other ML is capable of passing? I suspect you're confusing things. Not an
intelligence,learningcomparison, but anintelligence,consciousnessconfusion.