Would you say that ML isn't a successful discipline? ML is basically balancing between "formal language" (papers/algorithms) and "non-deterministic outcomes" (weights/inference) yet it seems useful in a wide range of applications, even if you don't think about LLMs at all.
> towards magical thinking, shamanism and witchcraft.
I kind of feel like if you want to make a point about how something is bullshit, you probably don't want to call it "magical thinking, shamanism and witchcraft" because no matter how good your point is, if you end up basically re-inventing the witch hunt, how is what you say not bullshit, just in the other way?
ML is basically greedy determinism. If we can’t get the correct answer, we try to get one that is most likely wrong, but give us enough information that we can make a decision. So the answer is not useful, but its nature is.
If we take object detection in computer vision, the detection by itself is not accurate, but it helps with resources management. instead of expensive continuous monitoring, we now have something cheaper which moves the expensive part to be discrete.
But something deterministic would be always more preferable because you only needs to do verification once.
> Would you say that ML isn't a successful discipline?
Not yet it isn't; all I am seeing are tools to replace programmers and artists :-/
Where are the tools to take in 400 recipes and spit out all of them in a formal structure (poster upthread literally gave up on trying to get an LLM to do this). Tools that can replace the 90% of office staff who aren't programmers?
Maybe it's a successful low-code industry right now, it's not really a successful AI industry.
> Not yet it isn't; all I am seeing are tools to replace programmers and artists :-/
You're missing a huge part of the ecosystem, ML is so much more than just "generative AI", which seems to be the extent of your experience so far.
Weather predictions, computer vision, speech recognition, medicine research and more are already improved by various machine learning techniques, and already was before the current LLM/generative AI. Wikipedia has a list of ~50 topics where ML is already being used, in production, today ( https://en.wikipedia.org/wiki/Machine_learning#Applications ) if you're feeling curious about exploring the ecosystem more.
> You're missing a huge part of the ecosystem, ML is so much more than just "generative AI", which seems to be the extent of your experience so far.
I'm not missing anything; I'm saying the current boom is being fueled by claims of "replacing workers", but the only class of AI being funded to do that are LLMs, and the only class of worker that might get replaced are programmers and artists.
Karpathy's video, and this thread, are not about the un-hyped ML stuff that has been employed in various disciplines since 2010 and has not been proposed as a replacement for workers.
> Would you say that ML isn't a successful discipline? ML is basically balancing between "formal language" (papers/algorithms) and "non-deterministic outcomes" (weights/inference) yet it seems useful in a wide range of applications
Usefulness of LLMs has yet to be proven. So far there is more marketing in it than actual, real world results.
Especially comparing to civil and mechanical engineering, maths, electrical engineering and plethora of disciplines and methods that bring real world results.
What about ML (Machine Learning) as a whole? I kind of wrote ML instead of LLMs just to avoid this specific tangent. Are you feelings about that field the same?
> What about ML (Machine Learning) as a whole? I kind of wrote ML instead of LLMs just to avoid this specific tangent. Are you feelings about that field the same?
No - I only expressed my thoughts about using natural language for computing.
Would you say that ML isn't a successful discipline? ML is basically balancing between "formal language" (papers/algorithms) and "non-deterministic outcomes" (weights/inference) yet it seems useful in a wide range of applications, even if you don't think about LLMs at all.
> towards magical thinking, shamanism and witchcraft.
I kind of feel like if you want to make a point about how something is bullshit, you probably don't want to call it "magical thinking, shamanism and witchcraft" because no matter how good your point is, if you end up basically re-inventing the witch hunt, how is what you say not bullshit, just in the other way?