Comment by f_klem
14 hours ago
> For just about every "failure mode that confirms they're not thinking", you see one of two things. The first is that a new LLM releases a few months after and the "fundamental" issue abruptly goes away. The second is that we take a good, long look at a human, and find that the human also fails like this - and thus, "not thinking". Often both! Always funny when it's both. The way machines 'don't think' or 'fail' is fundamentally different from the way humans don't think or fail. In any case, the way LLMs learn and human beings learn is completely different. There is no actual clue that we are approaching any inflection point in machine 'learning'.
> So, my bet is on "LLM but even bigger". If there's a point where LLMs begin to lag behind and novel architectures get a sharp advantage, we are yet to hit it. We are already hearing this 'we are about to hit it' since the late 60s. The difference now is that the market is willingly investing insane amounts of money to make it possible. But again, there is no philosophical, theoretical, epistemological or biological clue that we are getting any closer to human intelligence level. What we did observe in the last decade though, is that we can build enormous machines that can statistically mimic statistical human outputs. Language and images being some of them. But that is not thinking.
First, fix your formatting. It's a fucking mess.
Second, what is the difference? Is it that one thing has an immortal soul, and thus Actual Intelligence and Actual Reasoning and Actual Learning, and the other has no soul, and a Pale Imitation of Intelligence, At Best?
Because I've seen versions of this "it's not actually thinking" for actual fucking years, and the difference between "actually thinking" and "not actually thinking" always seems to boil down to "I don't want LLMs to be actually thinking, so I will bend the definitions and twist the qualifiers and move the goalposts until they aren't". No one ever made an ActualThinkingBenchmark on which humans score 100% and LLMs score 0%.
Nothing but human insecurity, in my eyes. There was never a principled difference. Just a way to operationalize some "I'm unique and special and better than a matrix math machine" vibes.
Agreed, formatting was kind of f, but there is no need to be rude.
I wasn't saying there was any difference. All I'm saying is that the claimings the AI research field does are based on false assumptions. And from false assumptions, you cannot reach a proper conclusion.
Whether an AI system can reason and think like if it where a human being, or not, I don't care. I'm fine with either: it is just technological advance. But making claims based on false assumptions, and then being fooled by how 'wonderful' or 'spectacular' the results are, is, at least, naive.
> Nothing but human insecurity, in my eyes. There was never a principled difference. Just a way to operationalize some "I'm unique and special and better than a matrix math machine" vibes.
This is just something I don't get. People ignorant of technique are insecure and afraid. People that know how technology works, and thus investigate and know how it works fundamentally*, were never afraid or insecure.
A lot of people who "know how technology works" just went looking for copium, and found some. Now, they "know" a comforting lie - something like "it's just next token prediction".
Very comforting, that, but actively harmful to understanding.
The understanding starts with: we don't actually know how LLMs do what they do. They're more grown than designed. And it only gets worse from there. Very little comfort to be found in modern AI.
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