AI Book review: Death of An Author by Aidan Marchine

Death of An Author is an ebook novella produced by Stephen Marche using AI tools, which was commissioned by Pushkin Industries. I read a couple of good interviews about the book and, while I’d found the excerpts underwhelming as prose, I was curious to read the whole thing, particularly Marche’s afterword.

The book is about 95% AI text, using three tools. The original text was generated using ChatGPT, with Sudowrite used to tidy the text, making it “more active” or “more conversational”, and finally Cohere used to generate figurative language.

Death of An Author is a sly book, one that understands its place within a larger debate, and making allusions and interventions based upon this. There are references to the act of reading, the meaning of copyright and the book’s literary context – this is very much a book produced by someone with an English PhD, and the text itself is aware of what is at stake. The pseudonymous collaborative writer (Aidan Machine) even refers to an imaginary article by Stephen Marche, the human collaborator in the text. The writing itself is brisk if superficial, but the mystery had an interesting resolution – at least it felt so to me, as someone who rarely enjoys mysteries.

An example of the book’s commentary is in the description a dream the main character has:

That night, Gus had a terrible nightmare. He was taking an oral exam in front of his mother and ex-wife. Each time he tried to answer, a different writer’s voice came out: William Faulkner, Ernest Hemingway, Jane Austen.

Hemingway is the name of another AI writing tool. The dream questions the nature of imitation for human writers, as well as referring to one of ChatGPT’s finest tricks, that of imitating well-known writers.

The book also occasionally digs into specific descriptions, as in this outline of a meal.

That night, Gus made himself a meal of fried mushrooms in a cream sauce on toast. He started by heating a pan on the stove, adding butter to it. He then sliced the mushrooms and added them to the pan, cooking until they were browned. He added cream to the pan, stirring until the sauce thickened. He placed slices of toast on a plate and spooned the mushroom and cream sauce on top.

That passage is obviously AI, right? But I can’t be sure – maybe this is part of the 5% Marche wrote by hand. In a Guardian interview, M John Harrison spoke of an ambition he has: “I want to be the first human being to imitate ChatGPT perfectly. I bet you it’s already got mimickable traits”. Either way, it’s interesting that Marche chose to leave this prosaic description in the text.

Given the way in which the book’s plot takes it to such interesting places, it’s likely that Marche gave the AI some fairly clear leads about the overall story. It would be interesting to know the actual prompts used, although Marche talks about how he generated the book’s style:

What you need is to have it write something about a murder scene in the style of Chinese nature poetry, then make it active, then make it conversational, then Select All and put it in the style of Ernest Hemingway. That gets you something interesting. Raymond Chandler, after all, was not trying to write like Raymond Chandler.

The prose is often workman-like, but some flourishes and philosophical asides stick out. One sentence I particularly liked was “The policeman at the door of Gus’s office was a tall, thin, cadaverous man wearing a dove-gray suit that did not fit him well”. It is simple but has a lovely rhythm, and fits the hardboiled style. The choice of ‘dove-grey’ is interesting when we’d normally think of doves as white. And it makes me wonder how much work was done to get this just right. Marche has spoken about how the best results come from very precise prompts that are specific about substance and style. How much work was this, compared with just writing the sentence one wants?

Marche says that he did a significant amount of sifting of the produced text. This is some way from the dream of giving an AI a short prompt and having it produce perfect, entertaining prose. However, it’s notable that when Burroughs worked on his cut-ups, a great deal of time was spent exploring the results for interesting lines. Again, I’m curious about how much work was required to get the best lines (“He wondered why there was no good English word for slimy in a good way.”).

Marche talks about how good the AI is at ’heteroglossia’, with an uncanny ability to turn its hand to specific modes of speech such as “a paragraph from a Lacanian literary critic”. Marche says that he “[defies] any writer to improve on AI at that particular skill”. While the AI struggles to produce crisp narrative prose, it is an excellent mimic. In a podcast interview , Marche made the exciting suggestion that an AI might be very good at turning out a book like Dracula, which consists of different forms of documentation.

(A discussion in Wired Magazine placed Death of an Author alongside books including Moby Dick, which used extensive found text, and Graham Rawle’s Woman’s World, which sampled 50s women’s magazines)

Discussing the debate around AI, Marche writes:

So little of how we talk about AI actually comes from the experience of using it… Like the camera, the full consequences of this technology will be worked out over a great deal of time by a great number of talents responding to a great number of developments. But at the time of writing, almost all of the conversation surrounding generative AI is imaginary, not rooted in the use of the tool but in extrapolated visions.

It is important to actually play with these tools in good faith to see what they can do. The first book I bought made using ChatGPT was shardcore’s remix of John Higgs’ writing, The Future has Already Begun. Shardcore has spoken about how having the book as a physical object changes things. Reading Aidan Machine’s book makes a compelling argument better than the thousands of thinkpieces and opinion chatter. I only wish it had been made available as an actual paperback.

Marche concludes his afterword by saying that “What makes a good painting and what makes a good photograph are different. That transition required a complete reevaluation of the nature of visual creativity” The best AI art will not come from reproducing writing that humans can do better, but from finding new forms for this medium.

Is AI/ChatGPT as exciting as people say?

On this page, I’m collecting together some links about AI that I want to refer back to. Those are below; but first I wanted to discuss what I find interesting about AI right now.

I’ve been suspicious of the hype around AI for a long time, but I keep reading articles saying that AI models will revolutionise the economy, replacing millions of jobs. I’m even seeing serious articles saying that these new AIs are approaching sentience and are an existential threat to humanity.

One theory I’ve read is that the AI hype is so loud because the crypto grifters have moved onto this: there is always a profit to be made from hyping the next big thing. It was also an amazing marketing move from OpenAI to claim that their early models were too dangerous for people to have open access.

At the same time, very smart people I know, people I trust, are telling me there is something important here. And I’ve had several dreams about prompting AIs – so I should not be dismissing this too easily.

I’ve been playing a little with ChatGPT recently. Showing a friend how it could generate an email was an enlightening moment. He doesn’t like writing formal messages and having a detailed text produced from a prompt seemed revolutionary. My own experiments show that ChatGPT is good at certain types of output, but its grasp on facts is hazy. Asking it about things I know well, like hiking routes, it returns plausible information, but lacks telling details and gets significant facts wrong. This is beguiling, miraculous technology, but it (currently) has very clear limits.

Links on AI

The following are some good pieces that I have read on AI:

My current view

I find it hard to see how these huge statistical models are related to ‘true intelligence’, even as they raise questions by doing things that we once thought relied on intelligence. One notable thing is that (as with machine translation) these models are entirely reliant on human-produced work. This has led to the ethical questions around the model incorporating copyrighted works – and I note (via the Washington Post) that this blog is one of the sources for Google’s C4 data set:

I also wonder how much further these models can go. It won’t be long before the deluge of AI content begins to be absorbed into the models, which may undermine their effectiveness.

I also suspect there is a limit to the effectiveness of LLMs for problem solving. Matt Webb’s Braggoscope is the most compelling experiment I’ve seen, where ChatGPT was used to classify the thousands of In Our Time podcasts into the dewey decimal system. It’s a task where small inaccuracies will cause little harm, and Webb estimates that the automation of this was 108x faster than doing it manually.

But for tasks like programming, much of the art is not in producing the code, but figuring out what code needs to be written. It’s possible that a new paradigm of programming emerges from AI, but for any form of programming as we currently understand it, the trick is not writing the code but defining what code we want written, and making sure that we have achieved our aims.

The difficulty with AI is in producing very specific text. Producing remarkable sonnets about odd subjects is breath-taking, but getting an AI to write Allen Ginsberg’s Howl or Pierre Menard’s Selections from Quixote would be a different matter.

Writing and ChatGPT

As far back as 2008, Kenneth Goldsmith was saying that, through the Internet, writing had ‘met its photography’ – referring to the supposed crisis painting faced once realistic images could easily be produced. ChatGPT is another part of this long-running crisis, rather than something new.

Like most people, I find the output of tools like Midjourney, Stable Diffusion and ChatGPT miraculous. Being able to put a few words into a system and receive a picture matching that description is incredible. I keep reading claims that ChatGPT can pass the bar exam, or can think at the age of a small child, or generate computer code.

While ChatGPT can produce very good undergraduate essays on certain themes, it is not able to generate spontaneous writing about obscure texts. And while it might be able to create specific examples of code, that is not the main problem in programming. (Describing what a programme should do, and seeing whether it works are far more time-consuming). These tools are remarkable but they cannot easily synthesise new things.

I’ve had a lot of debate with a friend about whether these tools are creative. They definitely do some tasks that would be described as genuinely creative. However, this is a brute-force approach to only one type of ‘creativity’. These models are huge statistical analyses of existing content, a huge multi-dimensional data table. They are not artificially intelligent in the way we normally understand that term, rather they are reliant on a huge pool of imported data.

ChatGPT is very good at is producing styles of writing seen on the Internet. It can automatically generate the sort of text that provoke reactions, but it has trouble producing sustained and detailed texts. This tool will be able to flood the Internet with the sort of writing that already appears on the Internet. It is wickedly good at listicles, short blog posts that seem to say something, and arguments about major franchises.

This sort of language was already being crafted for the Internet. People were writing web copy to fit in with SEO. Buzzfeed was producing headlines that would be popular, and then crafting the stories to fit them. Twitter was promoting a particular style of discourse. The algorithmic ranking of text was a problem long before, because it was shaping the sorts of writing being produced.

ChatGPT arrives at an significant time. More people are reading than ever before, but they have changed what they are reading, moving on from novels and newspapers to smaller pieces of text. This is an fascinating time to be writing stories. ChatGPT is going to make certain types of content worthless (it’s a bad time to be producing small blog posts to increase engagement). It’s time to leave basic writing to the machines and move on to more interesting things.