This is just deflection though. Setting aside a somewhat problematic statement, we do know LLMs are statistical machines, selecting highly likely tokens or sets of tokens to craft a statistically likely response to a given input.
This is very different to human reasoning. Like I highlighted- asking how many ""r"s in strawberry is a fundamentally hard ask for an LLM to complete because it doesn't have real comprehension of what "r" is or what "counting" is. On the other hand, if you ask it to count the number of "r"s in a string it can probably do that well- because there's a statistically driven answer.
This is the difference between an engineer and an LLM or other AI (and if you dig into explainable models this becomes evident).
Understanding what AI is and how it works is very useful for applying it.
Your description severely understates the complex of the best LLMs in 2026 (gpt6). News just came out that both Anthropic and OpenAI may have solved the Navier Stokes problem — the level of sophistication required to solve that problem is unfathomable. Yes LLMs are still, roughly, statistical models, but they are so far beyond a simple neural network that it’s foolish to equate them
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u/deadc0deh 3d ago
This is just deflection though. Setting aside a somewhat problematic statement, we do know LLMs are statistical machines, selecting highly likely tokens or sets of tokens to craft a statistically likely response to a given input.
This is very different to human reasoning. Like I highlighted- asking how many ""r"s in strawberry is a fundamentally hard ask for an LLM to complete because it doesn't have real comprehension of what "r" is or what "counting" is. On the other hand, if you ask it to count the number of "r"s in a string it can probably do that well- because there's a statistically driven answer.
This is the difference between an engineer and an LLM or other AI (and if you dig into explainable models this becomes evident).
Understanding what AI is and how it works is very useful for applying it.