The last pic OP posted as a more recent photo, so the age isn’t a tell.
Look at the focus shift on the cat’s body in the last pic. The center of the body is out of focus for no reason. It’s because LLMs don’t have spatial sense. It doesn’t “know” how to maintain realistic camera focus.
It’s overrun by AI slop bots too.
This was the top post of r/aww the other day. Nobody noticed the AI slop. Or maybe all the comments were bots too.
America will never jail a president. The country will end before they lose face. Which is why my bets are on the country dissolving. No matter how low or high those odds are. An American president will never face justice.
People are scared because they think they could lose their jobs. Even if they say out loud they don’t think it will happen and that they think AI is useless. They need AI to fail for peace of mind.
If anything they’re most at risk for losing their job due to a stubborn refusal to adopt technology. What people don’t realize is that we don’t live in an ideal world where the optimal solution is the only way. Even if LLMs are less than perfect, there’s a scenario where it remains commonplace because it’s what’s in style. The stubborn abstainers will find themselves left behind when everyone else is leveraging it to complete mundane tasks much faster and checking the work.
We already live in a world full of cumbersome enshittified technology. There’s nothing we can do but make do with what we have. At this point people are dreaming if they think the AI bubble will pop and the technology will be shelved. It’s not going away. It’s already demonstrated usefulness. That it’s not turning a profit is a different story.
Maybe some others are simply scared that they don’t have the expertise to check the work of LLMs in the first place.
It’s a strange phenomenon where people couldn’t fathom complex large scale computing before LLMs. Talk of botnets were met with extreme skepticism to the point of inexplicable naivety. Now that people think LLMs are almost like AGI, they’ve swung to the other extreme. There are those who think a post that is more than a few sentences must be computer generated.
If there’s one characteristic I’ve noticed of the rich, it’s to offset risk on to others. Always have someone else do you dirty work.
I wonder if millennials will ever come to grips with getting older and out of touch. Will they be more like the boomer generation and clutch to their reality and force it upon the world into their geriatric years. I suspect it will be latter.
It’s like they’ve learned nothing.
Reddit was something a solid 15 years ago. Even 10 years ago it turned to shit when the_donald overran the platform as Huffman sat idly by. These days there is no intelligence on reddit. There’s petty social media bickering.
Which begs the question. Why has nobody been talking about how an ostensibly AGI like system will need AGI level data. No such thing exists. There’s no dataset that captures the entirety of humanity’s smartest minds. They’re feeding these models random social media comments. Like, what? Am I taking crazy pills.
That was my experience playing with the older neural net models back in the day. Usually the initial models and datasets are nearly the best it will get. Trying to feed it more data or trying to tweak the model only gains marginal improvements. Unless you’ve made a critical error in the initial work or miraculously happen upon a much better dataset then not much will change. I mean the whole premise that the machine finds the optimal solution within the limits of its capabilities. Beyond that you’re, rolling the RNG until the output lands on a result you like better, but the capabilities remain the same.
That’s why I suspect LLMs have peaked and these companies must be applying smoke and mirrors to keep the this iteration of AI going. There’s not going to be another leap forward until scientists put out another revolutionary paper for everyone to copy. That seems to be the cadence of AI.
The modern day banality of evil.
Well that’s disappointing. Is there a better brand in their price range?
That’s pretty accurate to my experiences. A notable point being that these projects are seemingly made by kids with zero experience in anything. It’s like investors gave 20 year olds billions of dollars and let them do whatever. Not just any 20 year old but tech bros who think they know everything.
Project requirements flip flop from day to day depending on their mood or tea leaves or the way the wind blows. One day you’re thinking you’ve followed all the project guidelines. The next day they are the complete opposite. Guess what. You’re on the hook for submitting work that no longer meets the spec.
Quality control reviewers are genuine workers or assholes or not paying attention at all. Your quality scores are what determines whether you stay on the project or get summarily removed. As the article said, you’re instantly dropped from everything without notice. You can be in the middle of working on a task and the webpage will vanish along with all your access to everything. It’s a bit of a jump scare that workers fear the most.
It’s all shrouded in much secrecy. There layers upon layers of obfuscations (read: clueless “managers” as the article said). They’re not really managers but messengers passing along the own interpretations of communications from the layer of obfuscation above them.
Still I’m 99% sure the top of the pyramid blame their shit results on us lowest peons. It becomes very obvious why LLMs are hilariously bad at times. The datasets must be so garbage given that they keep flip flopping on their requirements. Those guidelines have been passed through who knows how many layers of other workers like us. It’s a game of telephone.
The article is wrong about managers all being Gen Z. I don’t know why they put that there other than to clickbait and stoke the pointless generational war. The mid level managers are anybody from younger to middle age workers. It’s impossible to actually get contact with any of the actual engineers behind the projects.
There have been times where I’ve pointed out glaring contradictions with in project. Nobody had been paying attention at all. Not the managers. Not other workers. It’s people mindlessly reading off documents they made based on other communications they received from higher up. I was unceremoniously removed from that one project.
I was on one project where a reviewer was giving out a high rate of bad scores and entering random justifications. All of us workers spotted the trend. It took the longest time for anyone above to notice because of the layers of obfuscation.
It’s actually impossible to get in contact with anyone of consequence. The managers can only say they’ll pass along whatever concerns you might have. It the equivalent of putting your message into a suggestion box that never gets read.
I’m pretty sure most projects stack ranked. So you’re easily dropped from a project if you encounter problematic reviewers. There really isn’t any training or anything. It’s sort of a Black Mirror like world. Where workers are monkeys on keyboards sitting in individual pods. Every day a few of those pods are randomly dropped out of the system because that monkey pulled the wrong sequence of levers. Sometimes people say it’s like Severance. Nobody knows what’s really going on because we’re all kept in the dark. We just know the work is very important so we keep clicking the numbers on the screen.
Quite often the datasets are so bad, it’s impossible to annotate the data. The article pointed out the regional accent. Sometimes the images, videos, audios are so bad it’s impossible do anything with them. But it’s work that must be done so you try to invent some way to justify annotating the thing based on the guidelines. What even is the point of using that useless data but to create an LLM model for the sake of having something to show for the investors. It’s a sham.
The quality control process needs to be much better but as the article said, it’s other workers who were picked because they supposedly had better quality scores. The pay remains the same too like the article said. The whole system is a mess to begin with so they might as well be picking reviewers at random. Also keep in mind the project guidelines can change constantly. All things considered, it’s garbage in garbage out as someone already commented.
A lot of workers are grinding time to collect paychecks rather than actually care about a project. It’s a shitshow at both ends. I’ve heard workers remark on how they’ve lost faith in AI after working these jobs.
This is where a portion of the billions of investment money is going. They’re exploiting a work force to churn out datasets because they’ve already devoured the existing internet. The lowest paid I’ve seen so far is $1.50 USD for certain workers in India. On the same project, people in other countries were getting up to $20. The same projects are farmed out to different data brokers so the pay can be drastically different depending on the company.
There’s so many scammers too. Given the disparity in pay, there are people from India and Africa who will farm accounts from other regions of the world and work through VPNs. There have been speculations that some of these workers even get to be reviewers. That’s where people think is a source much of the bogus reviews that are written in broken English.
Same. Internet Karens are enabling these authoritarian times.
I’ve often heard Americans say that one of their greatest strengths is how bipolar they are (with a more positive spin). Well now we’re seeing the insane side in control of everything.
Watching For All Mankind makes me sad. It’s an alternate timeline where humanity focused on space exploration instead of ad tech. They have the internet but it’s a pure utility for communication. There’s no surveillance capitalism, no social media, none of this junk.
We’ve wasted the past 3 decades using science and technology to enrich the American capital class.
I take it those Polymarket accounts still have a perfect record. How many billions have they stolen by now?
A long time ago in my country there was a minor scandal with a mid-level government employee sending about $1 million dollars to an obviously fake firm to purchase equipment. The website was made earlier in the year. The site was a thin veneer, with a few incomplete product documents.
The money was never recovered. Nobody was ever held responsible. A million dollars of tax payer money disappeared into somebody’s pocket just like that.
Anyone remember Dr. John Campbell on Youtube. He got popular by posting videos during COVID. Then veered off into full blown anti-vax conspiracies.
He’s a “doctor” because he has a PhD in making Youtube videos. No, seriously. His doctorate thesis is based on using Youtube as a teaching method. He’s not a real medical doctor.
His bullshit antics prompted actual doctors and scientistics to regularly post their own videos response.
I’m in one of the data annotation projects for this. It’s a dumpster fire.
It’s one of the reasons I still keep a few reddit accounts but I’ve come to the same conclusions. Nobody listens. They’d rather mental gymnastic their way around the contradictions before their very eyes than see the plain truth that reddit is a hive of right wing troll jobs.
Humans are error prone. That goes for both sides of these jobs. I mean the engineers who run these projects.
It’s important to not get carried away with the allure of the tech industry. Especially the LLM hype. The people making these LLM models are human too. They’re not unicorn tech wizards.
I’ve seen projects where the examples they gave us on what to submit were AI slop. They did not notice. By far the most common error with them is unclear and constantly changing guidelines. I’ve seen projects where their training videos were made by someone whispering nervously into the microphone. We had to crank the volume to hear them stumble over their words while trying to explain the project.
Ultimately most of these jobs exist to harvest data for projects that aren’t that important. Forget about AGI or whatever. Think more along the lines of your weekend project. There’s investor money right now so they have to use it.
They won’t be paying people (read: impoverished third world countries) more than a few dollars an hour to grind out mountains of training data to feed into models. They’re not chasing unicorns here. It’s just slop generating LLMs. There’s investor money so they have to use it. Why would they split more of the loot with clickworker tier peons.
Here’s a bonus anecdote. One of the projects showed us literal shit in their training materials. A wet turd. I think it must have been a disgruntled employee. After hearing about how much Facebook employees hate working in the AI division, I think it must have been.
I really doubt the typical tech worker has that much conviction in LLMs themselves. It’s just what’s in style right now and it’s what’s getting them richer.