For several hours a week, I write for a technology company worth billions of dollars. Alongside me are published novelists, rising academics and several other freelance journalists. The workload is flexible, the pay better than we are used to, and the assignments never run out. But what we write will never be read by anyone outside the company.
That’s because we aren’t even writing for people. We are writing for an AI.
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The core part of the job is writing pretend responses to hypothetical chatbot questions. This is the training data that the model needs to be fed. The “AI” needs an example of what “good” looks like before it can try to produce “good” writing.
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François Chollet, a bestselling computer science textbook author and the creator of the Keras training library (which provides building blocks for researchers to create their own deep learning models), told me he estimates there are “probably about 20,000 people employed full-time just creating annotated data to train large language models”. Without manual human work, he says the models’ output would be “really, really bad”.
The goal of the annotation work that I and others perform is to provide gold-standard examples for the model to learn from and emulate. It’s a step up from the sorts of annotation work we’ve all done in the past, even unknowingly. If ever you’ve been faced with a “captcha” problem asking you to prove you aren’t a robot – eg “select all the tiles with pictures of a traffic light” – you were actually doing unpaid work for a machine, by helping to teach it to “see”.
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If chatbots can pretend to write like humans, we can also pretend to write like chatbots … it’s unclear how many outside the field understand that the “secret sauce” behind these celebrated models relies on plain old human work.
— ‘If journalism is going up in smoke, I might as well get high off the fumes’: confessions of a chatbot helper, by Jack Apollo George at The Guardian.
