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WHY THIS MATTERS IN BRIEF

As AI gets better at creating realistic content, of any form, without human assistance it will have an immeasurable and revolutionary impact on every corner of society and business.

 

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We all know that Artificial Intelligence (AI) is getting much better at creating synthetic content, whether it’s writing articles, scripts for books, such as the Game of Thrones, and adverts, and fake news content, generating its own photorealistic imagery, and video content, and much more. But now, in a world first, academic publisher Springer Nature has unveiled what it claims is the first research book generated using machine learning.

The exciting book which is a riveting read for anyone with insomnia, is titled Lithium-Ion Batteries: A Machine-Generated Summary of Current Research. And obviously it isn’t exactly a snappy read. But, it is a world first and a big sign of things to come as we now begin to stare what some are calling “on demand papers” in the face.

 

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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, and while yes it might be boring, the fact it was all written automatically by a robot is amazing.

Writing in the introduction, Springer Nature’s Henning Schoenenberger, who’s 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 – a challenge that is also shared with other professionals in other disciplines such as healthcare and so on. 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.”

 

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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 endeavours 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 though by the bucket load is churn out formulaic texts by the library load. In journalism, for example, machine learning is used by organizations like The Associated Press and Wall Street Journal to create summaries of elections, 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.”

 

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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.

About author

Matthew Griffin

Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, CNBC, Discovery, RT, and Viacom, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.

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