Academic publisher Springer Nature has unveiled what it claims is the first research book generated using machine learning.
The book, titled Lithium-Ion Batteries: A Machine-Generated Summary of Current Research, isn’t exactly a snappy read. Instead, as the name suggests, it’s a summary of peer-reviewed papers published on the topic in question. It includes quotations, hyperlinks to the work cited, and automatically generated references contents. It’s also available to download and read for free if you have any trouble getting to sleep at night.
While the book’s contents are soporific, the fact that it exists at all is exciting. Writing in the introduction, Springer Nature’s Henning Schoenenberger (a human) says books like this have the potential to start “a new era in scientific publishing” by automating drudgery.
Schoenenberger points out that, in the last three years alone, more than 53,000 research papers on lithium-ion batteries have been published. This represents a huge challenge for scientists who are trying to keep abreast of the field. But by using AI to automatically scan and summarize this output, scientists could save time and get on with important research.
“This method allows for readers to speed up the literature digestion process of a given field of research instead of reading through hundreds of published articles,” writes Schoenenberger. “At the same time, if needed, readers are always able to identify and click through to the underlying original source in order to dig deeper and further explore the subject.”
Although the recent boom in machine learning has greatly improved computers’ capacity to generate the written word, the output of these bots is still severely limited. They can’t contend with the long-term coherence and structure that human writers generate, and so endeavors like AI-generated fiction or poetry tend to be more about playing with formatting than creating compelling reading that’s enjoyed on its own merits.
What AI can do is churn out formulaic texts by the library load. In journalism, for example, machine learning is used by organizations like The Associated Press to create summaries of football matches, earthquakes, and financial news. These are topics where creativity is, if anything, an impediment. What you need is rote robot writing.
As technologist Ross Goodwin is quoted in the introduction to Springer Nature’s new book: “When we teach computers to write, the computers don’t replace us any more than pianos replace pianists — in a certain way, they become our pens, and we become more than writers. We become writers of writers.”
But we might not even be at the stage of automated drudgery in AI writing. Speaking to The Register, Jeff Bigham, an associate professor at Carnegie Mellon’s Human-Computer Interaction Institute, said the book wasn’t the most impressive feat of AI writing.
“It is quite straightforward to take high-quality input text, spew out extractive summaries pushed up next to one another, and have it look somewhat coherent at a cursory glance,” said Bigham. “In fact, the very nature of extractive summary means it will be coherent in chunks, so long as the input texts are coherent. It’s much harder to create something that a human reader finds valuable.”
Indeed, when flicking through the text, it’s not hard to find garbled and incoherent sentences. Phrases like “That might consequence in substantially high emphasizes and henceforth cracking or delamination” aren’t just scientifically dense; they’re impenetrable. It’s one thing to publish an AI-generated academic text, but we’ll have to wait and see if that AI text ever becomes useful.
This article originally appeared in The Verge