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Despite mentioning a philosopher (good pick!), this is just creative writing rehashing one side of the hard problem -- or, more specifically, restating the dogma that Turing wrote his most famous paper to debunk. It's really good creative writing, at least!
There's really not much else to say, cause it's all just begging the question by assuming that dogma. Like, here:
Sure, it's interesting if you assume that it's "just" statistical prediction. There's a link, but it's just more creative restatements of the dogma, e.g. "But the LLM is just guessing words"
Well yes, guessing words is the entirety of what an LLM is engineered to do.
Are you saying that is the entirety of what human minds do as well?
What else could it be doing? That is literally the mechanism of how a model works.
How is a fictional character the product of someone's mind, but a character generated from a massive database of words from other people's minds is not?
Silicon math can never be chemical goo math!
Why can't people just admit they believe in souls?
I frequently say what you say, but there is a more charitable reading: today's AIs are not "minds", as they are not stateful. Maybe continual learning ones (like the mini AGI exhibited today on the HN front page) will be perceived as "soulful".
I imagine it's the difference between a chef combining ingredients with intentionality vs a person going to multiple fast food restaurants and blending everything together.
I recently spent an hour asking a chatbot for kimchi recipes and variations. It reflected common choices of chefs well. It was not at all a random blend of ingredients and methods.
Because of the definitions of the words you strung together into that question.
It answers itself.
This is said all the time by AI skeptics and I think it's right in some areas and massively wrong in others.
I know (or at least assume I know) a lot about certain coding domains where frontier models also show convincing ability. And we know that frontier LLMs really do excel in some areas of mathematics (i.e. when an inexpert human was able to prompt the models to derive a closer bound on the Riemann Hypothesis).
OTOH I know those same models struggle to do things I'm not an expert in (e.g. writing English in a captivating way) because I read their output and have taste.
I think part of this comes from the fact that LLMs are surprisingly good at logic but roughly about as good as expected on information accuracy.
LLMs are not convincing to me in the domain I did grad school...but neither is Wikipedia, or Reddit, or random pop sci books. And LLMs are basically just summarizing those things.
But when made to work through difficult arbitrary logic (like coding), they are very impressive.
I think this also explains why people gripe a lot about LLM coding _style_, but concede that LLMs do totally fine on coding _correctness_ in 2026
This sounds similar (The same concept?) to Gell-Mann amnesia; substitute news/media articles for LLMs!
The more you know about a subject the better you can prompt AI, steer it toward the correct path, and recognize when it hallucinates or strays. Current generation AI is an automated memory-enhancement and thinking-accelerator tool, not a substitute for understanding or something that eliminates the need to think. A "mech suit for your brain" is the best analogy I've heard.
This is why good programmers get better results when vibe coding than non-programmers or poor programmers.
Aren't the recent results in mathematics actually stronger evidence for his point? Although the models may be capable of generating proofs they aren't coming out with the same level of quality of a human discovered and communicated proof. Providing a gobbledy-gook yet technically correct proof (generated at least in part by brute force) lacks the qualities of an expert produced proof because they fail to communicate insight or understanding about why the theorem is true.
Gaining and successfully communicating insight and understanding from a proof you discovered is additional work that human mathematicians do. It's not just some side-product of proof-finding (at least not to the degree usually needed to publish). That AI models don't provide this is mostly proof that the model wasn't asked to do this work. Either because the prompter didn't know or didn't care
But there are also plenty of examples of humans providing technically correct proofs without any elaboration. Usually they get ignored, unless they are famous or the problem they solved was famous
It seems to me that often experts from some field will think less of other experts, basically because they have built a different understanding framework. So they both may be equally competent but perceive the other as less competent, and that is just based on the material, excluding some ego stuff.
Concur. In addition to taste, we also have a point of view, a unique voice (nobody loves corporate- or group-speak), and can iterate on our message as we deliver it to an ever wider circle of people.
I think to me LLMs had the effect of noticing much more the author, the intention behind human-made works of art (books, movies etc.). Before LLMs, I used to frequently consume media in a way as it were generated by a mindless process. Now it's like everything which is not AI-generated has more meaning than ever before, a bit like hypomania.
I'm actually using that as a catalyst for my own writing; beauty/human-ness in its imperfection. Prior to LLMs and their cultural craze, I harbored a fear that my writing would allow for someone to draw a box around me and mark me as a bore, dullard or of lacking originality.
Now that the noise-floor has been artificially raised (and generated), my crappy words are starting to have their own happy little carbon-based rhythm.
If you go far enough in a field, you start to recognize areas where your personal opinion differs from the “best practices” usually recommended.
I think by design an LLM can’t do that. It’s built to reflect the distribution of the knowledge it has been trained on.
Professional tech Cassandra discovers ELIZA and Searle; coins a term for it. More at 10...
Seriously though, why did I just need to read that many words to get no really new content? We have known for decades that humans are predisposed to anthropomorphize chatbots, and questioning whether coherent linguistic output implies understanding (or intent) is equally old hat.
I wonder if the OP has read https://www.anthropic.com/research/global-workspace - it seems like it directly addresses this