Earlier quoted context omitted.
> This is such a weird point to make I think it is a great point to make, because if everyone really believed that AIs will do everything without human intervention in a handful of years, as the marketing repeats again and again (AGI, singularity, etc.) and have been saying for years... why then get bothered? Because we DO know LLMs have their hallucinations, limitations, perform tasks not previously seen way worse t…
No we don't know your 'points' The Hallucinations are becoming less, significantly by now. It also might be already were it is cheaper for one of the big few companies to spend millions and billions to teach the LLM / creating the training data necessary for an LLM to do something which it is not yet good enough due to the fact, that they sell this capability then to everyone who wants to use this capabilitiy. We hav…
Yes? What is the mega-solid technique that is used for it? Armies of people using curated data and reviewing it by hand? That is exactly one of my points: shifting the work elsewhere for specialized tasks. More replicable, improved, but, it scales infinitely and is autonomous? Can you assert that?
I am not denying there is some use (a lot of uses!) for this, but this is more nuanced than just: oh, they will replace us. Not at all, that day, with the current technology, is not going to arrive. This is just a systematization, fitting and tweaking of human knowledge by curated data. It is not the one true superintelligence they are selling us. To begin with, they do not have a concept of truth, but of probabilistic truth. Only that poses already a very, very big problem for the path to perfection.
> We have not seen the end of Reinforcement Learning, which does need a lot less training data but more compute.
Noone said the opposite, but I would like to know at which cost and if it is feasible. We do not have even enough compute power for current technology.
> . Its very capable of stringing together enough things so it can clearly follow handwavy things i tell it to do, analyse error messages, analysing screenshots etc. all by itself.
I use it every day for these tasks and it works well BECAUSE I review the output and makes me go faster. It finds a lot of things I would have not found and it also hallucinates another handful of them, which confirms my point about AIs not being able to be fully autonomous in any future point in time unless tweaked exactly for the task, and even then, it can still miss judgement a human could have for edge cases. So I am not sure of how bad or good it can be compared to a human but I am pretty sure it cannot be more reliable than an expert in many situations.
> Chinese models becoming very good as open models
I think they will be better in the long term if they follow this path. Not absolutely better but when mixing with economics and the fact that no frontier model is totally reliable anyway... why pay a lot for something that needs human inspection anyway?
> There is not a single real ceilling in sight, we only have clear barriers like compute but constant fast progress.
The ceiling is the paradigm itself, as I mentioned above. There is not a single chance with current technology that something could become "generically knowledgeable" and "reliable" both at the same time. If it becomes generically knowledgeable and reliable, it is bc of data fed into it and curated and tweaked by humans. This is not an original idea from myself, there are armies of people doing this every day around the world, you can check. This is where a lot of improvement comes from. Can this be reused? Of course. It is a generic solution? No way.
> Only half a year ago I had to babysit an LLM, now i tell it 1-3 sentences and it just goes and does it. And that stuff runs without compile errors etc.
Yes, I also do one-off scripts like this and code snippets, even reviews and others. Now go design a full distributed system. Use agents if you want. We come back in six months and compare it to a system that was properly written and tested by humans and we can compare the quality on some grounds:
1. how long it takes to add new features?
2. which ones act more according to spec once added?
3. when adding features, which ones have more bugs?
4. in the face of an error, will the agent delete my whole AWS infra (count the money losses if possible also)?
5. will I understand (or need to understand, but I bet yes) this code at some point in the future?
Not easy at all.