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I Tried Using A.I. To Replicate My Creative Process. It Got Weird.
Can you turn a machine into a novelist?
The speculative-fiction writer Philip K. Dick used amphetamines and other stimulants to transform himself into a 24/7 writing machine. Powered by chemicals, he churned out 28 novels and more than 132 short stories (many of which used drugs as subject matter, including “A Scanner Darkly”). Nor was he alone: If pulp writers didn’t churn out as much copy as possible, they didn’t eat — and if that meant swallowing pills so you could write for two days straight, so be it.
In some ways, the writing business hasn’t changed much in the past century. For thousands of writers, the volume of copy you generate is proportional to how much you earn. Drugs are still a way to power through — I know more than one journalist or blogger who developed a nasty Adderall habit — but often it’s just a combination of caffeine and desperation.
I’m a journalist and editor who also writes pulp fiction on the side, so I’m as aware of the marketplace dynamics as anyone else in the writing business. Over the past year, I’ve been keeping an eye on the evolution of A.I. text generation, which is touted (by businesses) as a way of generating tons of content on the cheap, while derided (by writers) as a potential job killer.
One of the more prominent A.I. text generators has been GPT-2, a “large-scale unsupervised language model” created by OpenAI, a semi-nonprofit (it’s complicated) that wants A.I. and machine-learning tools used in virtuous ways. The relatively new GPT-3 is a further refinement of the underlying technology.
With a training dataset of 8 million web pages (featuring 1.5 billion parameters), GPT-2 was long-touted as capable of achieving “state-of-the-art performance on many language modeling benchmarks.” OpenAI initially refused to unleash it into the wild, fearful that it would be used to generate mountains of “fake news.”
Last year, I tried an experiment where I fed GPT-2 a selection of opening lines from some of history’s greatest literary works (including Jane Austen’s “Pride and Prejudice”). My conclusion at the time was that the algorithm was capable of sticking with the subject matter for a few lines, but quickly became…
