this post was submitted on 02 Aug 2026
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The current examples of AI could never be described as a general intelligence let alone Superintelligence. These LLMs are definitely just a typing monkey (that was eloquently put).
They are literally generating text word by word with a next-word-prediction methodology.
With some tweaking they can be made to produce mostly coherent code and apparently also equations that balance. This does not make these algorithms geniuses. A calculator can also balance an equation, so that alone is not impressive.
The techniques LLM designers use to leverage statistical models in order to generate coherent sentences are actually rather impressive. It is legitimately quite a feat to make LLMs work as well as they do. But that does not mean that these outputs are anything like "thinking", they're just outputs. Data goes in, the machine churns, does some mystery math, and different data comes out.
These systems are not worth anthropomorphizing.
Depends how you define superintelligence, what I meant is a more specialized superintelligence than AGI. We already seen that with Go and Chess. But if AI overtakes humans in mathematics it takes on a different quality. But we will see. I doubt it stops here.
Yeah, the sauce is in the mystery math. See my other reply. You're making a category error.
My understanding was that any hitherto success of LLMs in mathematics was in its trying approaches and information from different fields where they weren't traditionally applied (within mathematics), they have surfaced potential links but haven't created anything in any real sense. Still, a potential legitimate use for them that I personally hadn't anticipated.
I mean that math is so far beyond me I have no clue if or how the proofs would "create anything" or lead to anything useful. I'm sure they do, besides adding a little bit to our civilization's knowledge.
I do suspect that LLMs think very "broadly" with a very broad knowledge but can't really reason very deep without training for a special problem. So rapidly trying different approaches does sound like how they do it. So maybe it is a little bit similar to a typing monkey except that it apparently did these proofs with a relatively limited budget.