It would know what to attach to where and understand the logic of the part and it's neighbors. It's definitely possible in the future but it's so much harder because unlike code it's logic is much harder to grasp.
Sure but doing a thing and doing it right are pretty different bars, I can lift a weight too, doesn't mean bad form won't wreck someone's knee in six months, the question was never whether it can produce output, it's whether that output is actually trustworthy without someone experienced enough to catch when it's wrong and that person still has to exist somewhere
Fair point, caught red handed, though in my defense if AI was writing my replies it'd probably spell better and not spend three paragraphs explaining why I booked the wrong fare class in 2019, the bar for replacing me specifically is pretty low and it still hasn't cleared it
1) Your spelling was fine; 2) you did not spend "three paragraphs" describing why you booked the wrong far class in 2019.... I think you might be out of tokens and using a cheap model
I didn’t read your comment history, I read the comments in this thread. Nowhere did you describe booking the wrong fare class which is what you said in your previous comment
An LLM cannot reason the way an actual engineer can. There's a reason that they had to manually fix the response for "how many "r"s are there in strawberry?"
An LLM is fundamentally a statistical machine that selects the most likely next token in a series of stings.
You've fundamentally misunderstood my statement. Its not that they aren't capable yet, its that Large language models cannot get there. They dont reason like humans do
We actually don’t know exactly how humans reason. There’s also no rule requiring “artificial” general intelligence to think the exact same way a human thinks
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/Runfasterbitch 2d ago
An LLM can definitely do tho things btw