LLMs aren’t going to be designing anything; they’re just fancy auto complete engines with a tendency to hallucinate facts they haven’t been trained on.
LLMs are preventing real advancements in AI by focusing the attention and funding into what’s evidently a dead end.
LLMs are incapable of “recognising” any patterns they haven’t been trained on.
And they don’t really even recognise those, they’re just fancy auto complete engines, simply outputting the highest scored token from their training base based on their input.
They’re pattern matching machines; there’s no recognition, inner modelling of new knowledge, self referencing, or understanding of any kind, merely blind statistics.
They’re just bigger and fancier Eliza’s, and just as distant as Eliza was from any practical form of intelligence, artificial or natural.
While I personally do believe that achieving AGI¹, on a Turing machine is possible, LLMs and how they work are an excellent example in support of John Searle’s arguments against it with his Chinese room though experiment.
1— Or at least something equivalent to human intelligence, or better, in the measures by which we consider ourselves to be intelligent, though it’s arguable whether we can really be considered intelligent at all, or we’re just better, more complex, Chinese rooms.
But since we don’t understand how cognition works in living beings almost at all – who’s to say that’s not how ‘actual thinking’ works other than 'I know it when I see it!"
Because there are many aspects of what we understand as “actual thinking” (understanding concepts, learning, or solving puzzles, for instance) that LLMs are fundamentally incapable of achieving no matter how larger or more complex we make them or how much we optimise them.
They do one single thing (which, granted, they do relatively well): they take an input, they apply it to every token in their training data, generating a score for each of them, and they output the one with the highest score. And that’s all they do.
And that’s why, for instance, you’ll never be able to make a LLM that’s any good at playing chess, because there simply wouldn’t be enough atoms in the universe for it to store all possible states of the game, which it would need to have in its training model in order to auto complete its next move (and that’s not even accounting for the actual score computation, both in space and time).
They’re a cool fancy gimmick, possibly useful in certain cases as long as you can account for their hallucinations, but they’re not any closer to actual intelligence than Eliza ever was.
you’ll never be able to make a LLM that’s any good at playing chess,
They said that about machines and then we all laughed at the mechanical turk hoax. Now machines can almost beat you in Go.
I’ll say it again – It is hubris and you will obviously be wrong to try to predict the future or what will have value.
like come on – superpositioning exists and we’ve no clue how consciousness works (Bostrom thinks its just maths) but you have this crystal ball full of certainty. It smells…
Work a blue collar job your whole life and tell me it’s possible. Machines suck ass. They either need constant supervision, repairs all the time, or straight up don’t function properly. Tech bros always forget about the people who actually keep the world chugging.
They suck because your employer wouldn’t pay me more for a better machine. Chemical is where it is at, outside of powerplants and some of the bigger pharms the chemical operator is a dead profession. Entire plants are automated with the only people doing work are doing repairs or sales.
Why? This is a very real possibility.
LLMs aren’t going to be designing anything; they’re just fancy auto complete engines with a tendency to hallucinate facts they haven’t been trained on.
LLMs are preventing real advancements in AI by focusing the attention and funding into what’s evidently a dead end.
AGI != LLMs.
AGI is a pipedream
I hope not. I want more types of sentient beings to exist. But, I also don’t believe any company is actually working towards AGI.
No, the existence of humans inherently disproves that. We just have hardware so advanced many still think it’s magic.
Now, if you said it was a pipe dream within the next decade? I’d agree.
Exactly, but LLMs are preventing further advances in AGI.
Proof?
All the money’s going into the LLM bubble, so there won’t be any left for actual AI research until it bursts.
Saying something like that doesn’t make it true. That’s not proof.
Are you claiming that absolutely nobody is working on AGI because LLMs exist and are hot right now?
TFW you realize you’re just a fancy autocomplete engine :P
No, I’m a self-referential pattern recognition machine.
same same?
LLMs are incapable of “recognising” any patterns they haven’t been trained on.
And they don’t really even recognise those, they’re just fancy auto complete engines, simply outputting the highest scored token from their training base based on their input.
They’re pattern matching machines; there’s no recognition, inner modelling of new knowledge, self referencing, or understanding of any kind, merely blind statistics.
They’re just bigger and fancier Eliza’s, and just as distant as Eliza was from any practical form of intelligence, artificial or natural.
While I personally do believe that achieving AGI¹, on a Turing machine is possible, LLMs and how they work are an excellent example in support of John Searle’s arguments against it with his Chinese room though experiment.
1— Or at least something equivalent to human intelligence, or better, in the measures by which we consider ourselves to be intelligent, though it’s arguable whether we can really be considered intelligent at all, or we’re just better, more complex, Chinese rooms.
But since we don’t understand how cognition works in living beings almost at all – who’s to say that’s not how ‘actual thinking’ works other than 'I know it when I see it!"
Because there are many aspects of what we understand as “actual thinking” (understanding concepts, learning, or solving puzzles, for instance) that LLMs are fundamentally incapable of achieving no matter how larger or more complex we make them or how much we optimise them.
They do one single thing (which, granted, they do relatively well): they take an input, they apply it to every token in their training data, generating a score for each of them, and they output the one with the highest score. And that’s all they do.
And that’s why, for instance, you’ll never be able to make a LLM that’s any good at playing chess, because there simply wouldn’t be enough atoms in the universe for it to store all possible states of the game, which it would need to have in its training model in order to auto complete its next move (and that’s not even accounting for the actual score computation, both in space and time).
They’re a cool fancy gimmick, possibly useful in certain cases as long as you can account for their hallucinations, but they’re not any closer to actual intelligence than Eliza ever was.
They said that about machines and then we all laughed at the mechanical turk hoax. Now machines can almost beat you in Go.
I’ll say it again – It is hubris and you will obviously be wrong to try to predict the future or what will have value.
like come on – superpositioning exists and we’ve no clue how consciousness works (Bostrom thinks its just maths) but you have this crystal ball full of certainty. It smells…
Work a blue collar job your whole life and tell me it’s possible. Machines suck ass. They either need constant supervision, repairs all the time, or straight up don’t function properly. Tech bros always forget about the people who actually keep the world chugging.
They suck because your employer wouldn’t pay me more for a better machine. Chemical is where it is at, outside of powerplants and some of the bigger pharms the chemical operator is a dead profession. Entire plants are automated with the only people doing work are doing repairs or sales.