LLMs are a subset of AI though. They’re a use case of generative AI, which is a subset of deep learning, which is a subset of machine learning, which is a subset of AI.
Obviously, but all machine learning systems that I am aware of - and definitely the generative ones - lack the “I” in AI in their core processing. I won’t dispute that there’s actual deterministic algorithms implemented before and after the core processing, to handle grammar rules etc. But there’s a huge danger in accepting the term “AI” for the slop machines of today. Gullible people will mistake linguistically sophisticated output for actual understanding, and trust the output to be correct.
Less gullible people have the obligation (imo) to set that record straight every time the topic comes up. Because gullible people vote, too - and they will otherwise be fine with slop machines controlling critical infrastructure the malfunction of which may be lethal.
And OP made the outrageous claim that reviewing and correcting LLM output would be less effort than “also writing it”. Even if LLMs did not, as they do, tend to generate a lot of garbage code to throw at tiny problems, and even if they did not MASS VIOLATE LICENSES AND COPYRIGHTS (yes, that makes me angry), the time spent on developing an all but trivial algorithm is a constant back and forth between writing it, reviewing it and tuning it. The actual writing of code is negligible compared to the amount of time a serious developer has to spend thinking about what a line of code does, and what it does not do.
The only people that “benefit” from LLM generated code are people who would otherwise not be able to piece together a stable bit of software beyond a “Hello World”, and now they break out of their sandboxes and waste the time of actual developers with their slop pull requests. One could almost assume this was an intended side effect by big corporations to kill the trust and efficiency in open source projects.
And “AI” in general, and in a technical sense, is just a label for a set of techniques which have the common goal that computers solve certain, hitherto unsolved, complex tasks. The content of the set, and thus the exact meaning of the label changes over time. For example recognition of handwritten digits, graph algorithms e.g. for route planning, or backtracking algorithms playing board games were once considered AI, but aren’t today.
Which is separate from the mere belief that computers will become as “intelligent” as humans - this is a kind of belief system with some quasi-religuous aspects.
It would be better to not call it “AI” but “experimental novel computing methods”, leaving out the quasi-religuous stuff…
LLMs are a subset of AI though. They’re a use case of generative AI, which is a subset of deep learning, which is a subset of machine learning, which is a subset of AI.
Obviously, but all machine learning systems that I am aware of - and definitely the generative ones - lack the “I” in AI in their core processing. I won’t dispute that there’s actual deterministic algorithms implemented before and after the core processing, to handle grammar rules etc. But there’s a huge danger in accepting the term “AI” for the slop machines of today. Gullible people will mistake linguistically sophisticated output for actual understanding, and trust the output to be correct. Less gullible people have the obligation (imo) to set that record straight every time the topic comes up. Because gullible people vote, too - and they will otherwise be fine with slop machines controlling critical infrastructure the malfunction of which may be lethal.
And OP made the outrageous claim that reviewing and correcting LLM output would be less effort than “also writing it”. Even if LLMs did not, as they do, tend to generate a lot of garbage code to throw at tiny problems, and even if they did not MASS VIOLATE LICENSES AND COPYRIGHTS (yes, that makes me angry), the time spent on developing an all but trivial algorithm is a constant back and forth between writing it, reviewing it and tuning it. The actual writing of code is negligible compared to the amount of time a serious developer has to spend thinking about what a line of code does, and what it does not do.
The only people that “benefit” from LLM generated code are people who would otherwise not be able to piece together a stable bit of software beyond a “Hello World”, and now they break out of their sandboxes and waste the time of actual developers with their slop pull requests. One could almost assume this was an intended side effect by big corporations to kill the trust and efficiency in open source projects.
And “AI” in general, and in a technical sense, is just a label for a set of techniques which have the common goal that computers solve certain, hitherto unsolved, complex tasks. The content of the set, and thus the exact meaning of the label changes over time. For example recognition of handwritten digits, graph algorithms e.g. for route planning, or backtracking algorithms playing board games were once considered AI, but aren’t today.
Which is separate from the mere belief that computers will become as “intelligent” as humans - this is a kind of belief system with some quasi-religuous aspects.
It would be better to not call it “AI” but “experimental novel computing methods”, leaving out the quasi-religuous stuff…