Tag Archives: AI

What might the future of AI mean for editors and proofreaders?

We asked five editors to give us their views on how they think AI might affect their work in the future (while acknowledging that none of them owns a crystal ball). Here are their thoughts…

Hazel BirdHazel Bird

Back in January, I wrote on The Wordstitch Blog that I don’t believe AI will ever replace human editors – and (spoiler alert) AI agreed with me. Nothing I’ve seen since then has changed my opinion.

I certainly think AI will have an impact by shifting how editors work. I suspect there will be a natural migration away from the less judgement-based work of ‘error checking’ towards the more nuanced, involved work of refining and enhancing text (although this doesn’t necessarily mean that I think traditional proofreaders will be out of a job; proofreading is about much more than ‘error checking’ and requires intensely refined judgement at a point in the editorial workflow where the scope for changes is often very limited).

Overall, in the long term I believe AI will have a positive (or at worst neutral) effect on our work. I believe it will do this by allowing us to be more efficient and thereby freeing us up to provide more of the gloriously messy human mix of spontaneity and personal experience that leads to great creative collaborations (remember: ChatGPT et al. cannot truly create; all they can do is predict based on what they have learned from text that already exists).

My view is that the most important thing for us to do as editors is to educate ourselves about AI. If you’re like me, reading about its new (and ever-increasing) capabilities involves a lot of mental flinching. But it’s important to set aside this fear and learn how to work with AI. If we ignore its possibilities, we only increase our chances of being replaced. In contrast, if we make it a part of our team, we might just gain a whole new lease of life in our respective businesses. We might be able to focus more on the meaningful editing we love, supported by our very own AI-powered editorial assistant.

Most of all I believe that, when it comes to the quintessentially human activity of communication, ultimately humans will always prefer to work with other humans.

Sue LittlefordSue Littleford

AI reminds me of the early days of Covid. We all know we’re facing something big – so big it may even be an existential threat (especially for editorial professionals) – and so we all want answers, but those answers simply don’t exist, yet. We have to wait for things to unfold, but as we’re so closely affected, we’re grappling at the margins of knowledge and speculation to try to get ahead of the curve.

My own view is that AI as an editing tool is, at present, pretty poor in my niche (scholarly humanities and social sciences). At present. As I write, the news has come that ChatGPT can now access the internet. Not great – it’s a move from inventing ‘facts’ to melding together an almighty mess, as it still won’t be able to distinguish between fake news, error, unfounded opinion and truth. People can’t! I imagine it will improve quickly but not so far as to replace us editors. Computers were, and are, pretty dumb things and language is a hugely complex thing to codify. The learning they do through AI is just following patterns – but who knows what trawling the internet will add to their output? Regulation will be essential.

However, AI bots will help unconfident writers, for whom getting a simple message across simply and clearly is important, rather than literary style. I suspect they’ll be better at utilitarian language than at artistry. I also suspect there’ll be a rise in mediocre writing, as people accept AI’s input without the ability to judge it for themselves and be selective.

Accordingly, I’m ensuring my marketing efforts remain focused on the quality end of the client base, targeting people and organisations who understand that AI isn’t an easy, cheap replacement for the human mind.

I’ll be asking clients about their AI policies and thinking about what additional clauses to include in my own T&Cs.

John IngamellsJohn Ingamells

At the CIEP conference, a colleague said that he had experimented with ChatGPT.

His conclusion was that it was ‘good, but not great’. This immediately brought to mind the instruction I well remember from my first proofreading course – the idea of learning when things are ‘good enough’. We would all love to produce perfect work every time. But often budgets and time constraints lead us to that ‘good enough’ conclusion.

It struck me that this could well be the niche that AI fills for the foreseeable future. The polished prose of literary fiction, the pinpoint accuracy needed in legal and commercial material – these are things that will probably be beyond AI for some time to come. But the world of ‘good enough’ is surely there for the taking. So much content nowadays is produced to tight deadlines and is only expected to have a short shelf life. Being able to get something usable far more quickly is bound to be attractive to a lot of people. How much of that sort of material is currently being put through editors and proofreaders is open to debate. So the impact on our profession may not be as bad as some fear. But the need for us all to demonstrate the value of our work and how it is worth paying for will only increase. Perhaps on our websites we now don’t just need a ‘Services I offer’ tab, but a ‘Services I offer that AI can’t’ tab as well!

a human hand touches a robot hand, mimicking Michelangelo

Erin BrennerErin Brenner

I run Right Touch Editing, an agency primarily serving small and mid-sized organisations. Our clients produce reports, marketing materials, and similar business-related copy, and they generally don’t have devoted writers on staff. Many projects are written collectively by staffers who have a lot of other tasks to do every day.

It’s easy to see how AI tools can help them get these projects done. They can brainstorm, outline, and write rough drafts quickly with the right prompts. And the output can be helpful – up to a point. We’ve all heard stories of AI tools making up details. They’re mediocre at best when it comes to writing and editing, even with well-written prompts.

But this is where my team and I come in. As the writing experts for our clients, we’re key to understanding these tools and using them successfully. We can help clients use these time-saving tools to get the results they’re looking for. I see us performing heavier-than-usual edits on copy that AI has helped produce. Being aware of its weaknesses means we can edit for those weaknesses. For those clients with a little more budget, we can work directly with an AI tool to develop the content and then revise and edit it to a professional standard.

These tools aren’t going away. Of course, we need to be wary, ensuring that we’re using them ethically, but we can’t ignore them. By becoming the experts on how and when to use AI tools, we can guide our clients to better, more ethical usage.

Andrew HodgesAndrew Hodges

Overall, I’m excited about generative AI’s possibilities but angry at how authors’ and artists’ work (including my own book) has been exploited in datasets to train them. Better protections for authors must be established. But generative AI (when developed responsibly) could save lots of time on copyedits by creating macros and doing jobs like reference list formatting etc.

In the medium term, I expect a small drop in the number of human editors needed, as more work will involve reviewing AI-assisted edits. I expect this will happen quickly for proofreading of PDF proofs.

Since I set up my business, I’ve been shifting to doing more developmental editing and coaching and less line editing and copyediting. I guess editorial consulting will be less directly affected by generative AI than, say, proofreading, light copyediting or translation. Whatever kind of editing people do, I expect editors who include human connection in their workflows will continue to flourish. Electric massage chairs didn’t make regular massages obsolete.

The most exciting aspect for me is how these new technologies may change our expectations. This reminds me of Ruth Schwartz Cowan’s argument about how the invention of labour-saving household devices like the vacuum cleaner didn’t ultimately end up saving people time. Alongside the new inventions came raised standards and expectations of cleanliness. When new technologies emerge, there’s a social reconfiguring and a new balance is found. This is happening with generative AI: in the space of a few months, it became obvious when ChatGPT had been used to write a blog or social media post, and I started interpreting a smooth, correctly spelled post or email with that signature ChatGPT voice differently. Who knows, there could be an increased focus on solving structural problems with manuscripts if generative AI takes care of some of the sentence-level issues. It’s impossible to predict!

About the CIEP

The Chartered Institute of Editing and Proofreading (CIEP) is a non-profit body promoting excellence in English language editing. We set and demonstrate editorial standards, and we are a community, training hub and support network for editorial professionals – the people who work to make text accurate, clear and fit for purpose.
Find out more about:

 

Photo credits: artificial brain by geralt on Pixabay; human and robot hands by cottonbro on Pexels.

Posted by Sue McLoughlin, blog assistant.

The views expressed here do not necessarily reflect those of the CIEP.

Definite articles: AI

Welcome to ‘Definite articles’, our pick of recent editing-related internet content, most of which are definitely articles. This time, our theme is the impact of artificial intelligence (AI) on editing and proofreading. It’s a hot topic of conversation among editorial professionals, which is why some of the links in this article were sourced from a CIEP forum thread about ChatGPT. Thank you to the CIEP members who shared them.

Because nothing related to discussions about AI can be guaranteed a long shelf life, you should know that this edition of ‘Definite articles’ was put together at the beginning of June 2023. It covers:

  • What’s been happening?
  • What can AI actually do?
  • How can editorial professionals move forward with AI?

What’s been happening?

On 30 November 2022, the AI chatbot ChatGPT was released by OpenAI. Since then, people who work with words, who include editors, proofreaders and writers, have had the unnerving feeling that the fundamentals of what they do might change, at least in some areas. If you haven’t been keeping a close eye on events, Forbes has written a short history of ChatGPT and two professors have summarised some of the implications of ChatGPT in usefully easy-to-understand terms. You can get an overview of Microsoft’s Copilot, an AI assistance feature being launched this summer, from CNN and Microsoft itself.

As well as the obvious nervousness about whether AI would replace various categories of worker, concerns were quickly raised about the effects of AI on assessing student work and what AI might mean for copyright.

By late spring 2023, loud noises were being made about regulation of AI. As lawmakers in Europe worked on an AI Act, workers in the UK reported that they would like to see the regulation of generative AI technologies.

It’s a subject that’s currently being written and thought about on a daily, if not hourly, basis. But, in practice, and at this point in time, what can AI actually do?

What can AI actually do?

If you didn’t catch Harriet Power’s CIEP blog, ‘ChatGPT versus a human editor’, it’s an enlightening and entertaining read that went down well with our social media followers on LinkedIn, Facebook and Twitter. Harriet instructed ChatGPT to take a proofreading test, write a blog post, and edit some fiction and a set of references. In the proofreading and editing tasks, it did ‘pretty well’ and was impressive in simplifying a fiction passage while keeping its main points. It also wrote a serviceable blog draft.

The two main problems Harriet noticed in the technology were a distinct lack of sparkle in ChatGPT’s writing and editing, and its ‘tendency to “hallucinate”: it’s very good at making stuff up with complete confidence’. (This tendency was also written about by Susanne Dunlap for Jane Friedman’s website, in an article called ‘Using ChatGPT for book research? Take exceeding care’.) Weighing up her test run, Harriet concluded:

ChatGPT apparently struggles to remain coherent when responding to much longer pieces of text (like whole books). It isn’t always factually accurate: you can’t entirely trust anything it’s saying. I can’t imagine how it’d make a good development editor, or how it’d handle raising complex, sensitive author queries. It can’t track changes well. It can’t think like a human, even when it can convincingly sound like one.

However, Harriet added the caveat that in her view it may be ‘years or even months’ before ChatGPT might be able to start competing with human editors. So, how should we respond to that?

computer screen showing OpenAI logo and text

How can editorial professionals move forward with AI?

Perhaps there’s no choice but to look at the possible upsides of the AI debate. Anne McCarthy for the New York Book Forum starts us off in ‘The potential impact of AI on editing and proofreading’ by reminding us that lightbulbs and the ‘horseless carriage’ inspired dire predictions in their day. She concludes: ‘Books always have (and always will) require a human touch: it’s what draws us readers to them.’

Amanda Goldrick-Jones, in an article for the Editors Toronto blog called ‘ChatGPT and the role of editors’, offers some wise and hopeful advice: there’s a point at which we, as editorial professionals, have to trust ourselves.

If anyone is well-positioned to explore and critique the possibilities and challenges of AI-generated writing, it’s an editor … So, as with other communication technologies, editors must self-educate about its affordances, propose clear ethical boundaries, and critically engage with its limitations. It’s a tool, not our robot overlord.

Part of this consideration and engagement is understanding AI’s risks, and Michelle Garrett lays these out very effectively in a blog post from March, ‘The realities of using ChatGPT to write for you – what to consider when it comes to legalities, reputation, search and originality’.

Moving one step further, a Q&A with writer Elisa Lorello on Jane Friedman’s website talks about actively using ChatGPT to become ‘creatively fertile’. Lorello testifies that when she started using the technology in earnest, ‘It’s like I suddenly gained an edge in productivity, organization, and creativity’.

And finally, Alex Hern in The Guardian described what happened when he spent a week using ChatGPT to enhance his leisure activities. If you’re not ready to use AI at work, perhaps you could at least get a couple of recipes out of it.

With thanks to the users of the CIEP’s forums for the links they shared in recent discussions.

About the CIEP

The Chartered Institute of Editing and Proofreading (CIEP) is a non-profit body promoting excellence in English language editing. We set and demonstrate editorial standards, and we are a community, training hub and support network for editorial professionals – the people who work to make text accurate, clear and fit for purpose.
Find out more about:

 

Photo credits: robot hand by Tara Winstead on Pexels; OpenAI screen by Jonathan Kemper on Unsplash.

Posted by Sue McLoughlin, blog assistant.

The views expressed here do not necessarily reflect those of the CIEP.

ChatGPT versus a human editor

Can ChatGPT hold its own against a human editor? Can it tackle typical editorial tasks? Harriet Power puts it through its paces.

ChatGPT describes itself as an ‘AI language model’: it’s essentially a clever chatbot that you can have human-like conversations with. It’s been trained on enormous amounts of text data (such as websites and books) to learn how language works. ChatGPT tells me that when someone gives it a question or prompt, it ‘breaks down the text into individual words and analyses them to understand the user’s intent. It then generates a response by predicting the most likely next words based on its training data’.

It turns out that ChatGPT is quite good at a variety of things, from writing marketing copy to summarising policy documents to creating computer code, and there’s been a fair bit of noise online about how it’s going to take over all our office-based jobs. But could it take over editing and proofreading? Could it start writing posts for the CIEP blog? I decided to put ChatGPT-4 through its paces with some typical editorial tasks.

Take a proofreading test

How does ChatGPT fare with the CIEP’s proofreading test? This test is available here; it’s a 270-word piece of text with 20 ‘common’ errors. I pasted the text into ChatGPT with the prompt ‘Please point out the proofreading errors in this text’ (it’s hard to let go of ingrained politeness, even when talking to a machine).[1]

If we ignore the 3 formatting errors that didn’t copy across, ChatGPT caught 15 out of 17 errors. It did well at spotting spelling mistakes (such as ‘peaking’ rather than ‘peeking’) and repeated words (‘There had certainly had been one or two eccentric characters’). It spotted that Anne’s cup of tea had morphed into a cup of coffee three paragraphs later, which according to my programming boyfriend is an impressive catch to make.

It missed a hyphen that should have been an en dash, and didn’t change ‘Jones’ geraniums’ to ‘Jones’s geraniums’, although that’s arguably just a style choice (as the test itself acknowledges). Another thing it didn’t do was query how Ann spells her name: it assumed ‘Anne’ was right (probably because it was spelled this way where it first occurs in the text).

So perhaps ChatGPT’s main failing here was to not question the text in a way a human would: to ask about style or spelling preferences. Plus of course it can’t see formatting errors, or track changes in a useful way, but both of those issues might be solved once Microsoft’s Copilot is launched sometime ‘in the months ahead’. This will basically integrate ChatGPT into Microsoft programs. At that point it might be perfectly possible for Copilot to proofread text in Word and mark up corrections using Track Changes and use a style guide for reference. Scary, right?

Write a blog post

How about writing posts for the CIEP blog? ChatGPT is certainly much quicker at this than us slow and plodding humans, taking under a minute to write a post. The results are quite bland and generic, but are also pretty serviceable. For example, here’s the start of a blog post about how to start out as a freelance copyeditor:

Embarking on a freelance copyediting career can be both an exciting and daunting prospect. With the right preparation and know-how, however, you can set yourself up for success in this rewarding field. In this blog post, we’ll explore the essential training and resources you’ll need to get started, as well as tips for finding your first copyediting jobs.

Part A: Training for Success

Understand the basics

Before diving into the world of freelance copyediting, it’s essential to familiarize yourself with the basic principles of the English language. This includes understanding grammar, punctuation, syntax, and spelling rules. There are numerous online resources and books available to help you strengthen your foundation in these areas.

Formal training

While it’s not strictly necessary to have formal training to become a freelance copyeditor, it can help you refine your skills and lend credibility to your services. Many organizations offer courses and certifications to help aspiring copyeditors learn the ropes. Some recommended courses and programs include:

    • The Chartered Institute of Editing and Proofreading (CIEP): CIEP offers a range of courses, from basic proofreading and copyediting to more advanced courses on specific topics. Their Introduction to Copyediting and Proofreading courses are great starting points.
    • The Publishing Training Centre (PTC): The PTC provides a comprehensive copyediting course called ‘Basic Editing by Distance Learning’ that covers grammar, punctuation, and syntax, as well as editing techniques and best practices.
    • The Editorial Freelancers Association (EFA): EFA’s ‘Copyediting: Beginning to Intermediate’ course is designed to help you build skills and knowledge in various areas of freelance editing, including copyediting, with a focus on practical applications and exercises.

With a little tweaking that could turn into a passable (if not sparkling) blog post. The main problem with it is that no courses exist with the exact titles ‘Basic Editing by Distance Learning’ or ‘Copyediting: Beginning to Intermediate’. This illustrates one of ChatGPT’s main limitations, which is its tendency to ‘hallucinate’: it’s very good at making stuff up with complete confidence, rather than simply admitting when it doesn’t know something.

A pair of glasses and a smartphone with ChatGPT open are lying on a desk

Edit fiction

I amused myself with writing some terrible fiction, then asked ChatGPT to improve it. Here’s what we started with:

Outside, the trees swayed ominously in the howling wind while dark, menacing clouds raced overheard. Rachel was gazing wistfully out of the window longing to be out in the paddock with her horse Ophelia irregardless of the fetid weather, when there was a resoundingly loud crash and a blood curdling screaming from above and she jumped ten feet in the air. Panicked thoughts flew threw her mind as she wrenched open the kitchen door, which persistently stuck as it grazed the uneven flagstones, and pelted downstairs where she found her brother tangled on the floor of the hallway in the rich velveteen curtains with the bust of Artemis, the goddess of love, shattered and lying in forlorn pieces around his beautiful golden-flecked hair.

“Jasper!” whispered Rachael frantically. “What happened!”

She roughly shaked him until he groaned and groggily opened one eye, gazing not at her but staring inwards at the visage that had shaken him to his very core.

“Rachel”, he moaned. “I saw him.”

“Saw who? Who did you see?” she exclaimed fearfully.

Asking ChatGPT to ‘line edit’ this led to a simple copyedit, where it corrected things like ‘overheard’ to ‘overhead’, ‘fetid weather’ to ‘foul weather’, ‘Artemis’ to ‘Aphrodite’ and so on. I then asked ChatGPT to ‘simplify the text a little’, and questioned whether the fact that Rachel heard the crash from upstairs but then ran downstairs is inconsistent. Here’s ChatGPT’s reply:

Yes, it is inconsistent. The story indicates that the crash and screams come from above, suggesting that Rachel should run upstairs instead of downstairs. Here’s the revised text to fix the inconsistency:

Outside, the trees swayed in the strong wind as dark clouds raced overhead. Rachel gazed out of the window, wishing she could be in the paddock with her horse, Ophelia, despite the poor weather. Suddenly, a loud crash and a scream from above startled her. Panicked, she opened the kitchen door, which stuck as it scraped the uneven flagstones, and rushed upstairs. She found her brother on the floor, entangled in the curtains, with the broken bust of Aphrodite scattered around his golden-flecked hair.

“Jasper!” whispered Rachel anxiously. “What happened?”

She gently shook him until he groaned and opened one eye, not looking at her but appearing deeply shaken.

“Rachel,” he murmured. “I saw him.”

“Saw who? Who did you see?” she asked nervously.

This clearly isn’t prize-winning fiction, but I was impressed that ChatGPT could take my original text and rewrite it while still keeping the essential bits of the narrative, and present them in a coherent way. It’s an example of how powerful ChatGPT can potentially be when it comes to simplifying text.

Edit references

I took a list of references, introduced a bunch of mistakes, and then asked ChatGPT to edit them so they were consistent.

ChatGPT did pretty well. It made corrections like replacing ‘and’ with ‘&’, moving the year to the correct place, changing the volume and issue number from ‘18:2’ to ‘18(2)’, and deleting the word ‘pages’ before the page range. The thing that impressed me was when I gave it a reference where I’d deleted one of the author’s initials, and ChatGPT filled them in correctly. It managed to do that correctly a couple more times, so it didn’t feel like a fluke. (Though if ChatGPT is simply making educated guesses about how to fill in the blanks then this is far from infallible.)

If ChatGPT could track changes (and my guess is that it will be able to do this in Microsoft’s Copilot) then I’d happily use it to help me edit references.

Close up of a ChatGPT screen

Can ChatGPT replace me?

ChatGPT is remarkable, in that it can edit and write text and you can hold a semi-intelligent conversation with it when its basic function is simply to guess what the next word should be.

As things stand, there are some stumbling blocks when it comes to using it as an editor or proofreader: ChatGPT apparently struggles to remain coherent when responding to much longer pieces of text (like whole books). It isn’t always factually accurate: you can’t entirely trust anything it’s saying. I can’t imagine how it’d make a good development editor, or how it’d handle raising complex, sensitive author queries. It can’t track changes well. It can’t think like a human, even when it can convincingly sound like one.

So I don’t think that it currently competes with a human proofreader or editor (or blog writer), but I do wonder how many years or even months it might be before it can. Things are moving at speed: in the time it’s taken to write this blog post, ChatGPT has already announced two upgrades (one from ChatGPT-3.5 to ChatGPT-4, and one to launch plugins). And when Microsoft’s Copilot launches sometime in the coming months, that’ll be another giant leap forwards.

John Naughton, professor of the public understanding of technology at the Open University, has described ChatGPT like this: just as Google became a prosthesis for memory, so will ChatGPT (or similar AI) become ‘a prosthesis for something that many people find very difficult to do: writing competent prose’. I’m certain that it’s going to have a seismic influence on an industry that helps people to write and publish competent prose; the question is just how quickly, and what editors and proofreaders can or should do about it.


[1] When I asked ChatGPT later on, ‘Do you appreciate it if I start my requests with the word “Please”?’ its answer was: ‘As an AI language model, I do not have the ability to feel emotions. However, using “please” in your requests is a courteous and polite way to ask for something, and it can help make the conversation more pleasant and respectful.’


About Harriet Power

Harriet Power develops and copyedits nonfiction books and educational materials. She is a commissioning editor for the CIEP information team, and a Professional Member of the CIEP.

 

 

About the CIEP

The Chartered Institute of Editing and Proofreading (CIEP) is a non-profit body promoting excellence in English language editing. We set and demonstrate editorial standards, and we are a community, training hub and support network for editorial professionals – the people who work to make text accurate, clear and fit for purpose.
Find out more about:

 

Photo credits: Motherboard by Pixabay on Pexels; Glasses and smartphone by Mateus Bertelli on Pexels; ChatGPT screen by Jonathan Kemper on Unsplash.

Posted by Sue McLoughlin, blog assistant.

The views expressed here do not necessarily reflect those of the CIEP.

Talking tech: Can a machine use conscious language?

In this Talking tech column, Andy Coulson delves into the world of artificial intelligence to find out how it might be able to consider the use of conscious language or edit text in the future.

For this issue of The Edit my column is going to be a little different from normal. Usually, I try to highlight how technology can help you with the theme of the issue. This issue’s theme, conscious language, proves to be a bit of a challenge on that front. What I am going to do instead is to get the crystal ball out and do a bit of speculating about how technology might develop to help ensure more conscious language use.

Natural language processing

Natural language processing (NLP) is the term used to describe a field of computer science that covers developing computer systems to understand text and speech in a comparable way to a human. This is a branch of artificial intelligence (AI), and I will get into some more detail about that later. This enables tools like Google Translate or the digital assistants Siri or Alexa to work. This is the field from which any tools (or indeed our competitors!) will come that will be able to improve how conscious the language in a text is.

Just to simplify things (slightly) I am going to ignore speech and all the computational issues that speech recognition brings. Let us concentrate on text and look at how machines are taught to understand that and make decisions about how to respond to it. To date, a lot of the NLP development has focused more on teaching a machine to respond to some text, whereas what we are trying to think about is how a machine would understand and amend a text. Microsoft and Grammarly both use AI to help improve their editing tools, so you can be sure there are other tech companies experimenting with this.

While language is to a degree rule based, it is also full of subtleties and ambiguities. The rules allow tools like PerfectIt to work – we can describe and recognise patterns and so teach a machine to do this too. This only takes us so far, as NLP then needs to pick the text apart to find the meaning within it. It must undertake a range of tasks on the text to enable the computer to ‘understand’ it. These include:

  • Speech or grammatical tagging, where the computer figures out the role of each word. This would be where it would identify ‘make’ being used as a verb (make a jacket) rather than a noun (the make of jacket).
  • Recognising names, so it can identify a proper noun. It knows Lesley is likely to be someone’s name rather than a thing, so ‘picking Lesley up on the way’ can be interpreted in the right sense.
  • Resolving co-references, where it relates a pronoun to a previously named object, so it recognises that ‘she’ is ‘Kathy’ from a previous sentence. This task can also be involved with dealing with metaphors or idioms – recognising that someone who is cold may not want an extra jumper but might not be much fun to talk to.
  • Sentiment analysis, which is also known as opinion mining. Here the computer is attempting to recognise more hidden aspects of the text, such as whether the tone is positive or negative.

All of these, and other functions we would need in order to make judgements about how conscious the language used in a text is, do not lend themselves to rules. Rather, they rely on a knowledge of context and conventions. Acceptable language in a novel set in 1960s Alabama would be quite different from that used in a modern social sciences paper about the same city and its inhabitants, but understanding the context will frame and shape language choices.

How machines learn

So, we have realised we are not going to be able to fix this one with a clever macro. What sort of computation do we need? Step forward AI – a term that covers a number of fields that involve machines that mimic human intelligence. One of the main aspects of this that NLP uses is machine learning, a field of computing covering machines that learn a task or tasks through different approaches.

One of the best-known AI companies is Google’s DeepMind division. They have made a name for themselves by approaching AI from the perspective of learning to play games using machine learning. To understand how they have progressed in the field we need a bit of a history lesson.

In 1997 an IBM project called Deep Blue beat the then World Chess Champion, Garry Kasparov. What Deep Blue did was to search all possible moves in the game and then pick the best next move. What is different about DeepMind’s AlphaGo is that they had to follow a different approach, as the game of Go has so many more possible moves than chess. This version of AlphaGo used neural networks (a brain-like arrangement of computing elements with lots of connections between each element) to compare the best move from the current position and the likelihood of winning from that move, which gave a more efficient way of narrowing down the choice of moves. AlphaGo was trained by playing vast numbers of games of Go to improve its ability to select moves and predict its current chance of winning. Eventually, in 2016, it beat Lee Sedol, widely regarded as one of the best players of all time.

DeepMind have since developed AlphaGo further and, instead of playing against experienced players, it learns from scratch by playing against itself. It uses a technique called reinforcement learning, where the system tries to optimise a reward called a Q-value. It has been able to play and master various video games from scratch (the Atari benchmark). Here AlphaGo tries to gain positive awards (and avoid negative ones) by, for example, collecting a game’s currency or surviving for a certain amount of time. It can then use the information about what it did and what reward it received to alter its strategy and see if that improves the Q-value.

Why is this important? It shows a progression from a very controlled environment with a limited (although large) number of variables, to a more complex one (Go) and then to a more generalised one (more varied games). We are still not at the point where this could be applied to a problem (like our language one) with very few constraints, but this certainly shows a progression. The latest version, AlphaZero, has apparently taught itself chess from scratch to a world champion level in 24 hours.

This technique of using neural networks and reinforcement learning seems to me to offer the potential to create tools with a more subtle understanding of learning. One issue that can cause problems is that AI often uses huge datasets to train the systems, but using already acquired data can bring with it historical problems. Microsoft created an AI chatbot for Twitter called Tay, designed to mimic the speech patterns of a 19-year-old girl, which it did very well right up to the point it learned to be inflammatory and offensive and had to be shut down. Microsoft believe that the trolling the bot experienced taught it how to be offensive. Similarly, Amazon developed an AI system to shortlist job candidates, and this showed a distinct bias against women. Amazon tracked the problem down to an underlying bias in the training data.

Given the increasing pressure on social media companies to filter offensive content, platforms like YouTube and Facebook are undoubtedly trying to use AI to recognise problematic language, and some of this may lead to tools we can use to highlight issues. However, as editors and proofreaders we are looking to improve poor language choices and make it more conscious. Looking at how the Editor function in MS Word and Grammarly have developed, they certainly point to a way forward. While I am not convinced a machine is going to take my job for some time, I can certainly see where it could make progress. I think the challenge of issues like conscious language is that they have too many subtleties, and the human ability to make judgements about these, and even to have a productive discussion with an author about a passage, means a human editor will continue to be able to add something a machine cannot to a piece of writing, for the foreseeable future.

About Andy Coulson

Andy Coulson is a reformed engineer and primary teacher, and a Professional Member of CIEP. He is a copyeditor and proofreader specialising In STEM subjects and odd formats like LaTeX.

 

 

About the CIEP

The Chartered Institute of Editing and Proofreading (CIEP) is a non-profit body promoting excellence in English language editing. We set and demonstrate editorial standards, and we are a community, training hub and support network for editorial professionals – the people who work to make text accurate, clear and fit for purpose.

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Photo credits: chess by Bru-nO on Pixabay, robot by mohamed_hassan on Pixabay, Go by Elena Popova on Unsplash.

Posted by Harriet Power, CIEP information commissioning editor.

The views expressed here do not necessarily reflect those of the CIEP.