[tac19]

> ask Opus 4.5 to read adjacent code which is perhaps why it does it so well. All it takes is a sentence or two, though.

People keep telling me that an LLM is not intelligence, it's simply spitting out statistically relevant tokens. But surely it takes intelligence to understand (and actually execute!) the request to "read adjacent code".

[latentsea]

I used to agree with this stance, but lately I'm more in the "LLMs are just fancy autocomplete" camp. They can just autocomplete increasingly more things, and when they can't, they fail in ways that an intelligent being just wouldn't. Rather that just output a wrong or useless autocompletion.

[tac19]

They're not an equivalent intelligence as human's and thus have noticeably different failure modes. But human's fail in ways that they don't (eg. being unable to match llm's breadth and depth of knowledge)

But the question i'm really asking is... isn't it more than a sheer statistical "trick" if an LLM can actually be instructed to "read surrounding code", understand the request, and demonstrably include it in its operation? You can't do that unless you actually understand what "surrounding code" is, and more importantly have a way to comply with the request...

[baq]

In a sense humans are fancy autocomplete, too.

[suddenlybananas]

You know that language had to emerge at some point? LLMs can only do anything because they have been fed on human data. Humans actually had to collectively come up with languages /without/ anything to copy since there was a time before language.

[latentsea]

I actually don't disagree with this sentiment. The difference is we've optimised for autocompleting our way out of situations we currently don't have enough information to solve, and LLMs have gone the opposite direction of over-indexing on too much "autocomplete the thing based on current knowledge".

At this point I don't doubt that whatever human intelligence is, it's a computable function.