Inside the AI Factory, It's a Lot of Work

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Content source

https://www.theverge.com/features/23764584/ai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots

As the technology becomes ubiquitous, a vast tasker underclass is emerging — and not going anywhere.​

A few months after graduating from college in Nairobi, a 30-year-old I’ll call Joe got a job as an annotator — the tedious work of processing the raw information used to train artificial intelligence. AI learns by finding patterns in enormous quantities of data, but first that data has to be sorted and tagged by people, a vast workforce mostly hidden behind the machines. In Joe’s case, he was labeling footage for self-driving cars — identifying every vehicle, pedestrian, cyclist, anything a driver needs to be aware of — frame by frame and from every possible camera angle. It’s difficult and repetitive work. A several-second blip of footage took eight hours to annotate, for which Joe was paid about $10.

Then, in 2019, an opportunity arose: Joe could make four times as much running an annotation boot camp for a new company that was hungry for labelers. Every two weeks, 50 new recruits would file into an office building in Nairobi to begin their apprenticeships. There seemed to be limitless demand for the work. They would be asked to categorize clothing seen in mirror selfies, look through the eyes of robot vacuum cleaners to determine which rooms they were in, and draw squares around lidar scans of motorcycles. Over half of Joe’s students usually dropped out before the boot camp was finished. “Some people don’t know how to stay in one place for long,” he explained with gracious understatement. Also, he acknowledged, “it is very boring.”

But it was a job in a place where jobs were scarce, and Joe turned out hundreds of graduates. After boot camp, they went home to work alone in their bedrooms and kitchens, forbidden from telling anyone what they were working on, which wasn’t really a problem because they rarely knew themselves. Labeling objects for self-driving cars was obvious, but what about categorizing whether snippets of distorted dialogue were spoken by a robot or a human? Uploading photos of yourself staring into a webcam with a blank expression, then with a grin, then wearing a motorcycle helmet? Each project was such a small component of some larger process that it was difficult to say what they were actually training AI to do.

 

[jogs]

Why are they not using AI to do this kind of job.

That because AI is not actually AI at all, its just underpaid Human Labour.

 

[Ife456]

If they have patients in what they do put their maind they will make more

 

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At a Bloomberg technology summit on Friday, several industry experts spoke out against AI's deceptive facade, warning their audience that it's not the "magic" that it appears to be. This innocuous, magical appearance, they argue, leaves the public oblivious to just how intrusive and exploitative the tech can be.

To kick things off, Alex Hanna, director of research at the Distributed AI Research Institute, called out the CEOs of popular generative AI companies — including Sam Altman of OpenAI and Emad Mostaque at Stability AI — for hiding the fact that their products are powered by human grunts."We know from reporting that there is an army of workers who are doing annotation behind the scenes to even make this stuff work to any degree," Hanna said, as quoted by TechCrunch.

"They are actually doing the labeling, whereas Sam and Emad and all these other people who are going to say these things are magic — no, there's humans," she added. "These things need to appear as autonomous and it has this veneer, but there's so much human labor underneath it."

AI Isn't Magic, It's Being Used to Spy on You, Experts Warn

Experts warned that AI's autonomy is just a "veneer," and that it's being used as surveillance through the scraped data it needs to train on.

futurism.com

 

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IT’S PROBABLY HARD to imagine that you are the target of spycraft, but spying on employees is the next frontier of military AI. Surveillance techniques familiar to authoritarian dictatorships have now been repurposed to target American workers.

Over the past decade, a few dozen companies have emerged to sell your employer subscriptions for services like “open source intelligence,” “reputation management,” and “insider threat assessment”—tools often originally developed by defense contractors for intelligence uses. As deep learning and new data sources have become available over the past few years, these tools have become dramatically more sophisticated. With them, your boss may be able to use advanced data analytics to identify labor organizing, internal leakers, and the company’s critics.

It’s no secret that unionization is already monitored by big companies like Amazon. But the expansion and normalization of tools to track workers has attracted little comment, despite their ominous origins. If they are as powerful as they claim to be—or even heading in that direction—we need a public conversation about the wisdom of transferring these informational munitions into private hands. Military-grade AI was intended to target our national enemies, nominally under the control of elected democratic governments, with safeguards in place to prevent its use against citizens. We should all be concerned by the idea that the same systems can now be widely deployable by anyone able to pay.

Military AI’s Next Frontier: Your Work Computer

Spycraft developed by defense contractors are now being sold to employers to identify labor organizing. Regulators must step up to protect workers’ privacy.

www.wired.com

 

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DeepMind’s Gemini, which is still in development, is a large language model that works with text and is similar in nature to GPT-4, which powers ChatGPT. But Hassabis says his team will combine that technology with techniques used in AlphaGo, aiming to give the system new capabilities such as planning or the ability to solve problems.

“At a high level you can think of Gemini as combining some of the strengths of AlphaGo-type systems with the amazing language capabilities of the large models,” Hassabis says. “We also have some new innovations that are going to be pretty interesting.” Gemini was first teased at Google's developer conference last month, when the company announced a raft of new AI projects.

AlphaGo was based on a technique DeepMind has pioneered called reinforcement learning, in which software learns to take on tough problems that require choosing what actions to take like in Go or video games by making repeated attempts and receiving feedback on its performance. It also used a method called tree search to explore and remember possible moves on the board. The next big leap for language models may involve them performing more tasks on the internet and on computers.

Google DeepMind CEO Demis Hassabis Says Its Next Algorithm Will Eclipse ChatGPT

The company is working on a system called Gemini that will draw on techniques that powered AlphaGo to a historic victory over a Go champion in 2016.

www.wired.com