graphic designers have been out of work for almost 18 years. the job market was flooded by mediocre no talent hack designers. this drastically dropped the annual salary to basic minimum wage.

now, those same “improved” mediocre designers are losing their jobs to AI.

if the guy can’t read the writing on the walls, he won’t learn until it blows up in his face.

True, and I don’t see how we make that leap. Nobody is going to give that type of tech and investment over to humanity for free.

(also, I thought Musk had already hit 1 trillion, but not quite yet. The high end of his estimated net worth is 988 billion, so give it a month or two.)

This is the truly scary part for students using LLMs. We’re going to end up with civil engineers who don’t know how to calculate weight distribution. Doctors who don’t know how to read an EKG. Lawyers who lose cases they should have won, because they didn’t follow basic procedures.

I work in live events. I do things like hanging like array speakers for concerts. Just last month, I was on a show that was hanging an LED wall behind the stage. A LED wall is made up of individual panels, which lock together and form a solid screen in the size and shape you need. There have been several high-profile LED wall collapses, because they’re huge, heavy, and easy to fuck up. And when they collapse, people get seriously injured.

Typically, you build them by hanging truss from chain motors, and then mounting the screen to the truss. The screen gets built in rows, with one row getting snapped together across the truss, then the truss is raised slightly, then you snap the next row on. Repeat until the screen is at the desired size. So this means the screen gets heavier and heavier as you build it. Part of the reason why collapses can be so dangerous is because it often happens while the screen is actively being worked on. You’ll have crew underneath the truss, hanging panels to build the next row. And then suddenly the chain motors start to slip because they quietly blew right past their weight limits as the crew added additional weight with each row.

While working that gig, I overheard the lead rigger (the one in charge of calculating weight distribution on the hanging motors, designing the truss system to hold everything, deciding exactly where the motors should be mounted, etc) utter the words “yeah, ChatGPT says these panels are only 25 pounds each. That means we’re right at our weight limit. We should be good.”

In stagehand work, we use 25 pound sandbags all the time. I know what 25 pounds feels like. The same way a farmer would be able to feel if a hay bale is too light. I had been snapping these panels together all morning, and I knew without a doubt that they didn’t feel like 25 pounds. I looked up the actual tech specs directly from the LED panel’s manufacturer. Each panel was 35 pounds, not 25.

ChatGPT just hallucinated the 25 pound weight, and the dumbass rigger didn’t bother to double check any of it. We were going to be ~40% over our chain motors’ posted weight ratings, and needed like four extra motors to help carry the load. The screen was already halfway built on the truss, so hanging new motors was a giant pain in the ass. It required a scissor lift and climbing riggers to go install new pick points. It easily added an extra hour to the install, while we just sat and waited for the riggers to work.

That rigger is still working in the industry. I hope that it was a learning experience for him.

That already happens, doge used LLMs to decide which programs to cut.

I’m sorry you’re not paid to think, you’re paid to hit enter when the LLM wants to do something sketchy and take the blame when it blows up in our face.

Machine learning is a monkey bashing keys on a typewriter. Improvements will result in a better typewriter, so the monkey will more often hit the good keys instead of the bad ones, but it won’t result in a smarter monkey. It will still be random because probability is inherent to the technology, which means that AI will never be trustworthy. Would you ever trust a calculator that sometimes says 1+1=3? Even if it only did that 1% of the time?

You aren’t wrong about GenAI replacing jobs though, because there are people willing to use faulty, untrustworthy tools. Your manager, people in the C-suite, Business Idiots who have no idea what the the job actually needs to function properly are being tricked into thinking that chat boxes will let them cut operational costs without incurring any significant consequences.

It doesn’t really matter how good or not AI is; the reason jobs are being cut is because Business Idiots with more money than sense fire people based on vibes. This has been a known problem since the 1980’s when Jack Welch destroyed GE through his idiotic “Rank and Yank” strategy. His foolish business practices made a lot of money in the short term, which is why he is lauded as the CEO of the Century, even though those same business practices destroyed one of the greatest American companies in a handful of years.

Without any regulation I don’t see it happening either :(

Yeah, I mean, I like laughing at AI failing in funny ways or venture capitalists hyping it to oblivion, but it seems clear that it is just getting better and we need to plan for it.

tbf lemmy users are first to complain and also will be first in line to have clunker girlfriends because it understands them better than real girls

They don’t have to make an LLM that can actually do your job to get you fired, they just have to convince your boss that it can.

The two are not contradictory positions: the problem with AI is societal - all the jobs being given over to it is a problem that’s only compounded by the larger issue of people fundementally not understanding the sharp limitations of AI, and how it cannot actually be used to replace most jobs.

It’s almost as if Lemmy isn’t a homogenous entity with a single viewpoint… Wow! Who would’ve thought?

I bet even an llm could haven given a better overview of the discussion around ai on lemmy

It’s useful enough to fire huge chunks of the workforce and then make the leftovers carry the extra load, but corpos been doing that way before LLMs. Now they just have a plausible excuse

Anyone who says the first is lying to you. LLMs are actually incredibly useful tool in the tasks they were initially designed for like machine translation, natural sounding text-to-speech and accurate speech-to-text.

In trying to generate hype (or more rather revenue), the companies responsible for these models have been throwing LLMs into all sorts of functions they just weren’t designed for - often to haphazard results.

It’s like asking a really well-trained parrot to fact-check for you, code for you, write stories to you. It knows what these things look like, so can make really convincing copies and mash-ups that look right on first read - but it can’t iterate and make new things because it doesn’t actually know what training data it has is fact/fiction, it doesn’t know what code actually does, and it has no idea what a cohesive story is.

The problem is that executives and shareholders are only aware of what’s being hyped up about LLMs, and not of the technical limitations underneath that make them rather unreliable compared to specialised neural networks or just plain trained professionals.

So it is simultaneously robbing people of their jobs because of hype, while doing an absolutely terrible job of it because it is fundamentally limited in what it can replace.