To be honest, I feel like what you describe in the second part (the monkey analogy) is more of a genetic algorithm than a machine learning one, but I get your point.
Quick side note, I wasn’t at all including a discussion about energy consumption and in that case ML based algorithms, whatever form they take, will mostly consume more energy (assuming not completely inefficient “classical” algorithms). I do admit, I am not sure how much more (especially after training), but at least the LLMs with their large vector/matrix based approaches eat a lot (I mean that in the case for cross-checking tokens in different vectors or such). Non LLM, ML, may be much more power efficient.
My main point, however, was that people only remember AI from ~2022 and forgot about things from before (e.g. non LLM, ML algorithms) that were actively used in code completion. Obviously, there are things like ruff, clang-tidy (as you rightfully mentioned) and more that can work without and machine learning. Although, I didn’t check if there literally is none, though I assume it.
On the point of game “AI”, as in AI opponents, I wasn’t talking of that at all (though since deep mind, they did tend to be a bit more ML based also, and better at games, see Starcraft 2, instead of cheating only to get an advantage)
To be honest, I feel like what you describe in the second part (the monkey analogy) is more of a genetic algorithm than a machine learning one, but I get your point.
Quick side note, I wasn’t at all including a discussion about energy consumption and in that case ML based algorithms, whatever form they take, will mostly consume more energy (assuming not completely inefficient “classical” algorithms). I do admit, I am not sure how much more (especially after training), but at least the LLMs with their large vector/matrix based approaches eat a lot (I mean that in the case for cross-checking tokens in different vectors or such). Non LLM, ML, may be much more power efficient.
My main point, however, was that people only remember AI from ~2022 and forgot about things from before (e.g. non LLM, ML algorithms) that were actively used in code completion. Obviously, there are things like ruff, clang-tidy (as you rightfully mentioned) and more that can work without and machine learning. Although, I didn’t check if there literally is none, though I assume it.
On the point of game “AI”, as in AI opponents, I wasn’t talking of that at all (though since deep mind, they did tend to be a bit more ML based also, and better at games, see Starcraft 2, instead of cheating only to get an advantage)