It really depends on the context. Sometimes there are domains which require solving problems in NP, but where it turns out that most of these problems are actually not hard to solve by hand with a bit of tinkering. SAT solvers might completely fail, but humans can do it. Often it turns out that this means there's a better algorithm that can exploit commanalities in the data. But a brute force approach might just be to give it to an LLM and then verify its answer. Verifying NP problems is easy.

(This is speculation.)

obviously

The problem is they are not i.i.d., so this doesn't really work. It works a bit, which is in my opinion why chain-of-thought is effective (it gives the LLM a chance to posit a couple answers first). However, we're already looking at "agents," so they're probably already doing chain-of-thought.

I'm impressed you can make strides with Rust with AI. I am in a similar boat, except I've found LLMs are terrible with Rust.

Hitler liked to paint, doesn't make painting wrong. The fact that big tech is pushing AI isn't evidence against the utility of AI.

That common parlance is to call machine learning "AI" these days doesn't matter to me in the slightest. Do you have a definition of "intelligence"? Do you object when pathfinding is called AI? Or STRIPS? Or bots in a video game? Dare I say it, the main difference between those AIs and LLMs is their generality -- so why not just call it GAI at this point tbh. This is a question of semantics so it really doesn't matter to the deeper question. Doesn't matter if you call it AI or not, LLMs work the same way either way.

I would definitely bet it's made up and poorly designed.

I wish that weren't the case because having actual data would be nice, but these are almost always funded with some sort of intentional slant, for example nic vape safety where they clearly don't use the product sanely and then make wild claims about how there's lead in the vapes!

Homie you're fucking running the shit completely dry for longer then any humans could possible actually hit the vape, no shit it's producing carcinogens.

Go burn a bunch of paper and directly inhale the smoke and tell me paper is dangerous.

Agreed. 70% is astoundingly high for today’s models. Something stinks.

Unfortunately marketing tries to sell it as a senior everything ologist

That's even better, I can just jam something in before it and churn the documents through an embedding model, thanks!

I use it for very specific tasks and give as much information as possible. I usually have to give it more feedback to get to the desired goal. For instance I will ask it how to resolve an error message. I've even asked it for some short python code. I almost always get good feedback when doing that. Asking it about basic facts works too like science questions.

One thing I have had problems with is if the error is sort of an oddball it will give me suggestions that don't work with my OS/app version even though I gave it that info. Then I give it feedback and eventually it will loop back to its original suggestions, so it couldn't come up with an answer.

I've also found differences in chatgpt vs MS copilot with chatgpt usually being better results.

And let it suck up 10% or so of all of the power in the region.

I've had good results being very specific, like "Generate some python 3 code for me that converts X to Y, recursively through all subdirectories, and converts the files in place."

the industrial revolution could be seen as dangerous, yet it brought the highest standard of living increase in centuries

I mean, sure, in that the expectation is that the article is talking about AI in general. The cited paper is discussing LLMs and their ability to complete tasks. So, we have to agree that LLMs are what we mean by AI, and that their ability to complete tasks is a valid metric for AI. If we accept the marketing hype, then of course LLMs are exactly what we've been talking about with AI, and we've accepted LLMs features and limitations as what AI is. If LLMs are prone to filling in with whatever closest fits the model without regard to accuracy, by accepting LLMs as what we mean by AI, then AI fits to its model without regard to accuracy.

I was 0/6 on various trials of AI for Rust over the past 6 months, then I caught a success. Turns out, I was asking it to use a difficult library - I can't make the thing I want work in that library either (library docs say it's possible, but...) when I posed a more open ended request without specifying the library to use, it succeeded - after a fashion. It will give you code with cargo build errors, I copy-paste the error back to it like "address: <pasted error message>" and a bit more than half of the time it is able to respond with a working fix.

Except you yourself just stated that it was impossible to measure performance of these things. When it’s favorable to AI, you claim it can’t be measured. When it’s unfavorable for AI, you claim of course it’s measurable. Your argument is so flimsy and your understanding so limited that you can’t even stick to a single idea. You’re all over the place.

I have been more successful with baby steps like: "Write a python 3 program that converts X to Y." Tweak prompt until that's working as desired, then: "make it work recursively through all subdirectories" - and again tweak with specifics like converting the files in place, etc. Always very specific, also - force it to fix its own bugs so you can move forward with a clean example as you add complexity. Complexity seems to cap out at a couple of pages of code, at which point "Ooops, something went wrong."

an llm costs less, and won't compain when yelled at

Human lives are the most important thing of all. Profits are irrelevant compared to human lives. I get that that's not how Besos sees the world, but he's a monstrous outlier.

What's 0.7^10?