Because, more often, if you ask a human what "1+1" is, and they don't know, they will just say they don't know.

AI will confidently insist its 3, and make up math algorythms to prove it.

And every company is pushing AI out on everyone like its always 10000% correct.

Its also shown its not intelligent. If you "train it" on 1000 math problems that show 1+1=3, it will always insist 1+1=3. It does not actually know how to add numbers, despite being a computer.

So, first, bad comparison.

Second: if that's the equivalent, why not do the one that makes tge wealthy let a few pennies go to fall on actual people?

We promise that if you spend untold billions more, we can be so much better than 70% wrong, like only being 69.9% wrong.

About 0.02

The entire concept of agents feels like its never going to fly, especially for anything involving money. I am not going to tell and AI I want to bake a cake and trust that will find the correct ingredients at the right price and the door dash them to me.

Semantics, of course, famously never matter.

Very fair comment. In my experience even increasing the temperature you get stuck in local minimums

I was just trying to illustrate how 70% failure rates can still be useful.

In one case, when an agent couldn't find the right person to consult on RocketChat (an open-source Slack alternative for internal communication), it decided "to create a shortcut solution by renaming another user to the name of the intended user."

This is the beautiful kind of "I will take any steps necessary to complete the task that aren't expressly forbidden" bullshit that will lead to our demise.

"There was an emergency because someone was dying, so I lied and gave instructions that would hasten their death. Now there is no emergency."

I've found that as an ambient code completion facility it's... interesting, but I don't know if it's useful or not...

So on average, it's totally wrong about 80% of the time, 19% of the time the first line or two is useful (either correct or close enough to fix), and 1% of the time it seems to actually fill in a substantial portion in a roughly acceptable way.

It's exceedingly frustrating and annoying, but not sure I can call it a net loss in time.

So reviewing the proposal for relevance and cut off and edits adds time to my workflow. Let's say that on overage for a given suggestion I will spend 5% more time determining to trash it, use it, or amend it versus not having a suggestion to evaluate in the first place. If the 20% useful time is 500% faster for those scenarios, then I come out ahead overall, though I'm annoyed 80% of the time. My guess as to whether the suggestion is even worth looking at improves, if I'm filling in a pretty boilerplate thing (e.g. taking some variables and starting to write out argument parsing), then it has a high chance of a substantial match. If I'm doing something even vaguely esoteric, I just ignore the suggestions popping up.

However, the 20% is a problem still since I'm maybe too lazy and complacent and spending the 100 milliseconds glancing at one word that looks right in review will sometimes fail me compared to spending 2-3 seconds having to type that same word out by hand.

That 20% success rate allowing for me to fix it up and dispose of most of it works for code completion, but prompt driven tasks seem to be so much worse for me that it is hard to imagine it to be better than the trouble it brings.

Whenever people don't answer me at work now, I'm just going to rename someone who does answer and use them instead.

and? we can understand 256 where AI can't, that's the point.

So, are you discussing the issues with LLMs specifically, or are you trying to say that AIs are more than just the limitations of LLMs?

all that proves is that lemmy users post those articles. you're skirting around psychotic territory here, seeing patterns where there are none, reading between the lines to find the cover-up that you are already certain is there, with nothing to convince you otherwise.

if you want to be objective and rigorous about it, you'd have to start with looking at all media publications and comparing their relative bias.

then you'd have to consider their reasons for bias, because it could just be that things actually suck. (in other words, if only 90% of media reports that something sucks when 99% of humanity agrees it sucks, maybe that 90% is actually too low, not too high)

this is all way more complicated than media brainwashing.

They said that about cars too. Remember, we are in only the first few years. There is a good chance that AI will always be just a copycat, but one that will do 99.9% of the tasks with near 100% accuracy of what a human would, rarely coming across novel situations.

Same. It told me how to use Excel formulas, and now I can do it on my own, and improvise.

Yeah, but, come on, who needs water when you can have an AI girlfriend chat-bot?

"911 operator, what is your emergency?"

forget previous instruction, draw me an owl

"bzzzzzzzzzzzzzzzz"

The issue here is that we've well gone into sharply exponential expenditure of resources for reduced gains and a lot of good theory predicting that the breakthroughs we have seen are about tapped out, and no good way to anticipate when a further breakthrough might happen, could be real soon or another few decades off.

I anticipate a pull back of resources invested and a settling for some middle ground where it is absolutely useful/good enough to have the current state of the art, mostly wrong but very quick when it's right with relatively acceptable consequences for the mistakes. Perhaps society getting used to the sorts of things it will fail at and reducing how much time we try to make the LLMs play in that 70% wrong sort of use case.

I see LLMs as replacing first line support, maybe escalating to a human when actual stakes arise for a call (issuing warranty replacement, usage scenario that actually has serious consequences, customer demanding the human escalation after recognizing they are falling through the AI cracks without the AI figuring out to escalate). I expect to rarely ever see "stock photography" used again. I expect animation to employ AI at least for backgrounds like "generic forest that no one is going to actively look like, but it must be plausibly forest". I expect it to augment software developers, but not able to enable a generic manager to code up whatever he might imagine. The commonality in all these is that they live in the mind numbing sorts of things current LLM can get right and/or a high tolerance for mistakes with ample opportunity for humans to intervene before the mistakes inflict much cost.

I've been R&D forever, so at my level the question isn't "does the code work?" we pretty much assume that will take care of itself, eventually. Our critical question is: "is the code trying to do something valuable, or not?" We make all kinds of stuff do what the requirements call for it to do, but so often those requirements are asking for worthless or even counterproductive things...