AI agents wrong ~70% of time: Carnegie Mellon study
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A junior developer actually learns from doing the job, an LLM only learns when they update the training corpus and develop an updated model.
an llm costs less, and won't compain when yelled at
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The comparison is about the correctness of their work.
Their lives have nothing to do with it.
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.
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Run something with a 70% failure rate 10x and you get to a cumulative 98% pass rate.
LLMs don't get tired and they can be run in parallel.What's 0.7^10?
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Search AI in Lemmy and check out every article on it. It definitely is media spreading all the hate. And like this article is often some money yellow journalism
I think it's lemmy users. I see a lot more LLM skepticism here than in the news feeds.
In my experience, LLMs are like the laziest, shittiest know-nothing bozo forced to complete a task with zero attention to detail and zero care about whether it's crap, just doing enough to sound convincing.
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That's not really helping though. The fact that you were transferred to them in the first place instead of directly to a human was an impediment.
Oh absolutely, nothing was gained, time was wasted. My wording was too charitable.
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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.
It questionable to measure these things as being reflective of AI, because what AI is changes based on what piece of tech is being hawked as AI, because we're really bad at defining what intelligence is and isn't. You want to claim LLMs as AI? Go ahead, but you also adopt the problems of LLMs as the problems of AIs. Defining AI and thus its metrics is a moving target. When we can't agree to what is is, we can't agree to what it can do.
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I actually have a fairly positive experience with ai ( copilot using claude specificaly ). Is it wrong a lot if you give it a huge task yes, so i dont do that and using as a very targeted solution if i am feeling very lazy today . Is it fast . Also not . I could actually be faster than ai in some cases.
But is it good if you are working for 6h and you just dont have enough mental capacity for the rest of the day. Yes . You can just prompt it specificaly enough to get desired result and just accept correct responses. Is it always good ,not really but good enough. Do i also suck after 3pm . Yes.
My main issue is actually the fact that it saves first and then asks you to pick if you want to use it. Not a problem usualy but if it crashes the generated code stays so that part sucks -
And let it suck up 10% or so of all of the power in the region.
And water
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It questionable to measure these things as being reflective of AI, because what AI is changes based on what piece of tech is being hawked as AI, because we're really bad at defining what intelligence is and isn't. You want to claim LLMs as AI? Go ahead, but you also adopt the problems of LLMs as the problems of AIs. Defining AI and thus its metrics is a moving target. When we can't agree to what is is, we can't agree to what it can do.
Again, you only say it’s a moving target to dispel anything favorable towards AI. Then you do a complete 180 when it’s negative reporting on AI. Makes your argument meaningless, if you can’t even stick to your own point.
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an llm costs less, and won't compain when yelled at
Why would you ever yell at an employee unless you're bad at managing people? And you think you can manage an LLM better because it doesn't complain when you're obviously wrong?
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Again, you only say it’s a moving target to dispel anything favorable towards AI. Then you do a complete 180 when it’s negative reporting on AI. Makes your argument meaningless, if you can’t even stick to your own point.
I mean, I argue that we aren't anywhere near AGI. Maybe we have a better chatbot and autocomplete than we did 20 years, but calling that AI? It doesn't really track, does it? With how bad they are at navigating novel situations? With how much time, energy and data it takes to eek out just a tiny bit more model fitness? Sure, these tools are pretty amazing for what they are, but general intelligences, they are not.
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Yes, but the test code "writes itself" - the path is clear, you just have to fill in the blanks.
Writing the proper product code in the first place, that's the valuable challenge.
Maybe it is because I started out in QA, but I have to strongly disagree. You should assume the code doesn't work until proven otherwise, AI or not. Then when it doesn't work I find it is easier to debug you own code than someone else's and that includes AI.
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America: "Good enough to handle 911 calls!"
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No shit.
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I mean, I argue that we aren't anywhere near AGI. Maybe we have a better chatbot and autocomplete than we did 20 years, but calling that AI? It doesn't really track, does it? With how bad they are at navigating novel situations? With how much time, energy and data it takes to eek out just a tiny bit more model fitness? Sure, these tools are pretty amazing for what they are, but general intelligences, they are not.
No one’s claiming these are AGI. Again, you keep having to deflect to irrelevant arguments.
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I'm in a workplace that has tried not to be overbearing about AI, but has encouraged us to use them for coding.
I've tried to give mine some very simple tasks like writing a unit test just for the constructor of a class to verify current behavior, and it generates output that's both wrong and doesn't verify anything.
I'm aware it sometimes gets better with more intricate, specific instructions, and that I can offer it further corrections, but at that point it's not even saving time. I would do this with a human in the hopes that they would continue to retain the knowledge, but I don't even have hopes for AI to apply those lessons in new contexts. In a way, it's been a sigh of relief to realize just like Dotcom, just like 3D TVs, just like home smart assistants, it is a bubble.
I find its good at making simple Python scripts.
But also, as I evolve them, it starts randomly omitting previous functions. So it helps to k ow what you are doing at least a bit to catch that.
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This is the same kind of short-sighted dismissal I see a lot in the religion vs science argument. When they hinge their pro-religion stance on the things science can’t explain, they’re defending an ever diminishing territory as science grows to explain more things. It’s a stupid strategy with an expiration date on your position.
All of the anti-AI positions, that hinge on the low quality or reliability of the output, are defending an increasingly diminished stance as the AI’s are further refined. And I simply don’t believe that the majority of the people making this argument actually care about the quality of the output. Even when it gets to the point of producing better output than humans across the board, these folks are still going to oppose it regardless. Why not just openly oppose it in general, instead of pinning your position to an argument that grows increasingly irrelevant by the day?
DeepSeek exposed the same issue with the anti-AI people dedicated to the environmental argument. We were shown proof that there’s significant progress in the development of efficient models, and it still didn’t change any of their minds. Because most of them don’t actually care about the environmental impacts. It’s just an anti-AI talking point that resonated with them.
The more baseless these anti-AI stances get, the more it seems to me that it’s a lot of people afraid of change and afraid of the fundamental economic shifts this will require, but they’re embarrassed or unable to articulate that stance. And it doesn’t help that the luddites haven’t been able to predict a single development. Just constantly flailing to craft a new argument to criticize the current models and tech. People are learning not to take these folks seriously.
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.
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The comparison is about the correctness of their work.
Their lives have nothing to do with it.
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?
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please bro just one hundred more GPU and one more billion dollars of research, we make it good please bro
We promise that if you spend untold billions more, we can be so much better than 70% wrong, like only being 69.9% wrong.
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What's 0.7^10?
About 0.02
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