“ChatGPT is a personal research sidekick” – an interview with Animate Your Science

“ChatGPT is a personal research sidekick” – an interview with Animate Your Science

What opportunities and risks does ChatGPT create for improving communication practices in research? The science communicators at Animate Your Science recently presented a practical article with a very optimistic perspective. We reached out to authors Dr Khatora Opperman and Dr Tullio Rossi to hear more.

Based in Adelaide, Australia, Animate Your Science offers animation services and SciComm training programs. With writers such as project manager Khatora Opperman and founder/CEO Tullio Rossi the blog continuously presents useful know-how, tips, and recommendations for their followers. In February, the article How to use Chat GPT: Opportunities and Risks for Researchers defined ChatGPT as the researchers’ “Personal Research Sidekick.”

Illustration from https://www.animateyour.science/.


Hi Khatora and Tullio! What features would you like to see in future updates of ChatGPT? 

Khatora: “As a language model, ChatGPT is already much more powerful than before. However, I think several features could be added to make it even more useful for researchers. Specific training in the scientific text would be beneficial to improve its ability to understand and interpret scientific literature and technical jargon. Some have suggested features that improve ChatGPT’s ability to perform complex tasks like data analysis or literature review. However, I am skeptical about pushing language models in this direction. There is a fine line between augmenting human abilities and replacing them altogether!”

Tullio: “I think that the most significant limitation of ChatGPT today is that, although excellent at writing just about anything in a very confident way, it is not afraid to make stuff up. For example, I asked it to list the top papers in my Ph.D. field of research and it spits out a totally plausible list of article titles complete with authors and journal names but all of them were made up! Not a single one was real! It makes sense – it’s a language model, not a truth model. So, a feature I’d like to see in the future is the ability for Chat GPT to access the internet and reference its sources similarly to how Bing Chat does. Accuracy matters, and I am afraid that many people are not fact-checking the model’s outputs enough”.

How have your professional lives changed so far due to ChatGPT’s launch on 30 November 2022?

Khatora: “Conversational AI has been a game-changer for me personally. ChatGPT has become a useful day-to-day tool for brainstorming, accelerating my writing, and finding that word I just can’t think of! Whether it’s a meta description, an email, a social media post, or something else written, ChatGPT’s ability to suggest ideas, complete sentences, refine paragraphs, and offer synonyms has helped save me time and improve the quality of my writing. I also really like ChatGPT’s ability to adapt to my requested writing style, meaning I can focus on my ideas and creativity rather than on perfecting my writing. Overall, ChatGPT has become a valuable communication tool for me, and I’m excited to see how it will continue to evolve in the future”.

Are there any practical issues with ChatGPT that you find more problematic than others?

Tullio: “I’ve just returned from a business conference where somebody gave a presentation entitled “How to Use ChatGPT to Create 10 Blog Posts in Under 60 Mins”. My first reaction was, “WOW! I want to learn this method!”. Then it got me thinking…
Imagine how many people worldwide now use this tool to publish stacks of mediocre articles. It means that in a matter of months, the internet will be flooded with low-quality content with serious accuracy problems. How will search engines be able to differentiate AI-generated content from human one? I think that it will be a real problem, and it might make search engines way less reliable at providing accurate information. 

After ruminating on this idea for a couple of days, I concluded that writing high-quality, long-form human-generated content has never been more important. This is what we will stick with as a company at Animate Your Science”.

How do you think ChatGPT will change the intellectual domain of international research in the long run – seen from a more philosophical point of view?

Khatora: “This is a difficult question, it’s hard to predict how the landscape will change in the long run. ChatGPT has the potential to revolutionize the way we approach to research; however, I fear that we may become excessively reliant on language models and AI more generally, leading to a loss of independent critical thinking and basic writing skills. Much like how Google Maps has made many people reliant on technology for navigation, or how to autocorrect has made many forget how to spell. AI can assist researchers in various tasks, but it should not replace the human element that is necessary for generating new ideas, designing experiments, and interpreting results. In the end, scientific research is ultimately a human endeavor and should remain that way.”

Study shows: GPT-3 can—technically—be the author of scientific papers. An interview with Almira Thunström.

Study shows: GPT-3 can—technically—be the author of scientific papers. An interview with Almira Thunström.

A system could technically be considered a co-author—or even the main author!—of a scientific paper! This is the mind-boggling insight presented by human AI researchers Almira Osmanovic Thunström and Steinn Steingrimsson from Gothenburg – with the assistance of their co-author GPT-3.

Article: Does GPT-3 qualify as a co-author of a scientific paper publishable in peer-review journals according to the ICMJE criteria? – A Case Study.

“We tested the system against the ICMJE criteria, and we found that a system could technically be considered a co-author—or even the main author—of a paper,” says Almira Osmanovic Thunström. “The system would have to be the one to come up with the topic, write paragraphs, analyze and critically evaluate its production and be responsible—correct and discover mistakes—in its paper.”

Almira and her human co-author Stein Steinn Steingrimsson—both from the University of Gothenburg—noted that the system had no problem to fulfill three out of the four main criteria for co-authorship: “drafting the work or revising it critically for important intellectual content; final approval of the version to be published; and agreement to be accountable for all aspects of the work.” Only when it came to referencing, the system could not fulfill the criteria.

Almira Osmanovic Thunström is a project manager and researcher in child and adolescent psychiatry. Currently, she is working mostly with chatbots and virtual reality. Her background, however, is somewhat mixed.
“I am a biologist on paper, an assistant nurse in my heart, and an engineer by trade,” she says. “I get to do quite a lot of fun and exciting things, but at the moment, I am mostly interested in developing digital tools for treating anxiety disorders.”

In the summer of 2022, Almira published an article in Scientific American that spurred some attention: We Asked GPT-3 to Write an Academic Paper about Itself—Then We Tried to Get It Published. After exploring automated writing for fun in 2021, she realized that with a little bit of luck and the right settings, she could ask the system to help her with academic work. Fascinated, she started to experiment, and in the process, it became important for her to document her thoughts. Her notes eventually became an op-ed for what is probably the world’s most famous popular science magazine.
“As someone who has been creating chatbots for the past two decades, I’ve been around for many of the twists and turns regarding natural language processing and large language models. I usually approach all these things with a childish sense of exploration and with each new model, software, or hardware I encounter, I want to do something silly with it.”

After realizing that paper-writing text robots are an interesting and philosophical dilemma, Almira noted that in a world where scientists write like robots, we are bound to create robots that write like humans.
“We wanted to raise a debate on transparency and authorship,” she says. “The question of authorship and accountability is a big pink elephant in the room we academics would rather avoid talking about. After all, that senior researcher on your paper that hasn’t even read it, is paying your bills. Any paper where your name is first will keep paying your bills in terms of grant money, which is often dependent on having a steady production rate of papers. With the help of a system like GPT-3, one could produce papers very fast; after all, if your text can be expanded and completed by a system, you can do everything in half the time.”

Hi, Almira. This is truly fascinating! Please tell us how you proceeded when you wrote the study.

“We asked the system to write about itself. Mainly because it has so little data on itself (Terminator plot headache warning: the system is trained on 175TB of text data from a variety of databases up until 2020/2021, however, very little if any data existed on GPT-3 because it didn’t exist at that time, only GPT-2 did). We wanted to test its capability. It was a case of childish curiosity and an interesting thought experiment. During the peer review of the submitted paper, we realized that the main issue reviewers had (remember this is pre-ChatGPT and ChatGPT publications!) was if the system could follow the ICMJE criteria for authorship on a paper. So we did what the reviewers asked of us: checked the system against the criteria.”

What was your reaction when you went through the results?
‘“The world should see this!’ was my first reaction. And ‘Oh no!’ was the second. How on Earth do I present this data? What will this mean for myself and others as we advance!?!”

 What should scientists over the world learn from this study?
“Have an open mind, and dare to experiment. Let us write less like robots. Let us not blindly embrace authorship on papers; both humans and systems need to pass the ICMJE criteria. ChatGPT for example did not pass the ICMJE criteria on many different levels. Our paper has a very extensive discussion part that I think a lot of scientists will appreciate.”

How do you think the advent of ChatGPT and similar technology will affect science in the long run?
“I’m not so sure they will, or at least I think it will take time. My hope was that we’d rethink how we teach, write, design studies, and collaborate. I’m seeing institutions play out the worst scenarios in their head about the consequences of these systems, which I think is valid to some point, but not as productive as acknowledging a challenge and finding an innovative way forward. The most excited I’ve seen the universities get is when they discovered Crossplag.”

“In our SciAm article, I said we hope we hadn’t opened a Pandora’s Box. I think in the eyes of academic institutions, we very much did. Unlike Pandora’s box, hope was however not left inside. I see individual teachers and academics who are looking at how we can innovate how we teach in lieu of the takeover of automated writing—that gives me hope!”


Please note: all texts on this blog are produced by me, Olle Bergman, or invited human writers. When robot-written sections are added to serve as examples or to demonstrate a point, this is clearly indicated.

Feeling like a 21st century man

Feeling like a 21st century man

Sometimes the 21st century feels overwhelming. For example when you read the The Memo from Life Architect and ponder over where mind-bending initiatives like Muse and Leta will take us.

Then I feel comfort in Ray Davies’s bluesy howl of alienation in 20th Century Man.

Play it loud and enjoy the autotune-free vocals, the energizing noises from the acoustic instruments—especially the scratching of the (metal?) bottleneck on the fretboard–and the drummer’s (Mick Avory) non-perfect but groovy beat.


”There is no doubt NLG could have a profound impact“ – The AI ONE ON ONE interview

”There is no doubt NLG could have a profound impact“ – The AI ONE ON ONE interview

Anyone interested in how AI will affect the world should have a look at the LinkedIn-based blog/newsletter AI ONE ON ONE, run by the two academic researchers Aleksandra Przegalinska and Tamilla Triantoro. The purpose of their blog is to discuss advantages and limitations of current AI systems and explore the possibilities of using AI for good. RWR got in touch for an interview, focusing on NLG in general and the advent of ChatGP3 in particular.

Behind AI ONE ON ONE are two colleagues residing at different sides of the Atlantic Ocean. Aleksandra Przegalinska is a professor and Vice-President of Kozminski University in Poland, and a Senior Research Associate at the Harvard Labour and Worklife Program. Tamilla Triantoro is a professor at Quinnipiac University in the United States, and a leader of the Masters in Business Analytics program.
“The blog has been a good run so far,” says Aleksandra Przegalinska. “In summer of the last year, we started experimenting with different features of GPT-3, and then, in December, ChatGPT took the world by storm, making these conversations even more relevant.”



Hi, Aleksandra and Tamilla! What motivated you to start this blog?
“We both have a solid background in human-computer interaction research. One of the interesting things about working with new cutting-edge technologies is to observe how humans interact with them, and what are the possible improvements that can be implemented. Right now is the first time when natural language generation has been democratized. As more people get acquainted with this technology, it becomes more relevant to explore the possibilities. Through our blog we explore the creative applications, and in our research, we focus on the applications for business, and the effect of generative AI on routine and creative processes at the workplace.”

Let’s talk NLG in general and GPT-3 in particular! You recently noted that there is “incredible hype surrounding [ChatGPT] these days”. What do you think spurred this hype?

”The hype around the GPT family of models, particularly ChatGPT, is largely due to the impressive capabilities it has demonstrated in natural language processing tasks. The key strength of ChatGPT is its ability to generate highly coherent and human-like text in the form of a conversation. Unlike the previous version of GPT, ChatGPT requires minimal fine-tuning, and we observed this during our experiments. Newer technology, GPT-3.5 that powers ChatGPT, allows for a more seamless interaction and that is truly captivating.”

“The hype is also related to the excitement regarding the transformers’ remarkable ability to perform creative tasks – such as storytelling, generating interview questions, article titles and writing programming code. The transformers such as ChatGPT perform tasks, that were once considered impossible for machines to accomplish. This allows for more innovation and creativity while making it all accessible not only to industry giants but also to small businesses and individuals.”

Have you used GPT-3 for other things than studying how it works and experimenting with its features? 

”We both have interest in the future of work. AI influences the way the work will be performed in the future, and an interesting question is in what circumstances humans will collaborate with AI. Currently we study the collaboration of humans and AI via the lens of individual acceptance. We created an application for business use that integrates GPT-3 technology. The application assists knowledge workers in performing tasks of various difficulty and levels of creativity. We measure the levels of collaboration and attitudes towards AI and devise a program of improvement of the human-AI interaction and collaboration at the workplace.”

In what situations do you think GPT-3 can be useful for professional groups such as researchers, investigating journalists, and business people?

“GPT-3 certainly has the potential to be a useful tool for a wide range of professional groups due to its ability to generate human-like text and understand natural language input. Some specific examples includes, for instance, researchers: here GPT-3 can be used to assist with literature reviews and data analysis. It could help generate summaries of scientific papers and articles, assist with data analysis and visualization, and even support writing research proposals and papers. Another example could be investigating journalists: GPT-3 could be used to assist with fact-checking and research by quickly providing additional information on people, organizations, or events mentioned in articles. It could also be used to generate summaries of complex documents such as legal filings, and help analyze and organize large sets of data.”

“Obviously, the biggest impact will be seen for businesspeople: here GPT-3 can be used for automation of repetitive and low-value tasks such as writing reports, composing emails, and creating presentations. Additionally, it could also assist with market research and trend analysis, as well as generate strategic plans, and help identify new business opportunities.”

Can you see any ethical problems arise – short-term and long-term?

“Well, it is important to keep in mind that GPT-3 is a machine learning model and the results it generates still need to be verified, but it can save time for professional groups by automating time-consuming, repetitive and low-value tasks, supporting with research and analysis, and generating new insights and ideas. We need to understand that we humans are the real fact-checkers here.”

“Other issues include the following:

  • bias – because language models like GPT-3 are trained on a large dataset of text, which can perpetuate and amplify societal biases that are present in the data;
  • privacy – because the use of large language models for natural language processing can raise privacy concerns, particularly when the models are used to process sensitive or personal information;
  • explainability – because language models like GPT-3 are complex systems that are difficult to understand and interpret, which can be a problem in situations where it is important to know how a model arrived at a certain decision or conclusion.”

Do you think that upcoming generations of NLG technology  (GPT-5…6 …7 …) may attain better results than humans in certain situations? 

“It is possible that future generations of natural language generation technology may attain better results than humans in certain situations. The capabilities of language models like GPT-3 have already surpassed human-level performance in tasks such as language translation and text summarization. As the technology continues to improve and more data is used to train these models, it is likely that they will perform even better on these and other tasks.”

“For example, GPT-5 or GPT-6 models might be able to generate news articles or summaries, they might be able to compose a novel or a poem, it might be able to compose persuasive or convincing fake news or deepfake videos, even able to impersonate a person on internet, these are areas where human are currently capable but natural language generation technology may outmatch them in future.

“However, it’s worth noting once again that such models have significant limitations. They can only perform as well as the data and the algorithms that they have been trained on, so if the training data is biased or inaccurate, the model will be too. Additionally, Language models currently lack common sense, self-awareness or creativity. So, While they may be able to perform certain tasks better than humans, they may not be able to understand context.”

How will humanity’s relation with the written word be affected by NLG technology? Is it a bigger disruption than Gutenberg’s printing technology?

“Natural Language Generation (NLG) technology has the potential to significantly change the way people interact with written text. With NLG, machines can automatically generate written content in a way that mimics human writing. This means that people may rely more on computer-generated text for tasks such as writing reports, news articles, and other types of written content.”

“It’s hard to say whether NLG will be a bigger disruption than Gutenberg’s printing press, as it depends on how widely the technology is adopted and how it’s used. Currently it’s a bit of a social experiment on mainstreaming AI, we will see where it takes us. Having that said, the printing press allowed for the mass production of books, making written information more widely available and helping to spread knowledge and ideas. In a similar way, NLG has the potential to increase the efficiency and speed of producing written content, but it’s still in early stages and it’s hard to predict how it will evolve over time.”

“There is no doubt though that NLG could also have a profound impact on how people write and consume written information, as it may change the way we think and communicate, allowing us to express ourselves in new ways, and could have implications on areas such as employment, education, and language.”


Please note: all texts on this blog are produced by me, Olle Bergman, or invited human writers. When robot-written sections are added to serve as examples or to demonstrate a point, this is clearly indicated.

Björn Lundberg, historian, about ChatGPT: “In philosophical terms, the implications are huge”

Björn Lundberg, historian, about ChatGPT: “In philosophical terms, the implications are huge”

Just some days after the launch of ChatGPT, Björn Lundberg, a historian at Lund University, wrote an analysis that was widely shared on social media in Sweden. Primarily, Björn was concerned about the challenge that university teachers now face when it comes to written examinations and papers. But, as a historian, he naturally sees the wider implications. “It’s a revolution, and it happens now,” he writes. 

“I have seen what AI can do, and it’s time to be afraid”—Björn Lundberg’s blog post was widely shared on social media in Sweden.


Björn Lundberg [Lund University website] is a researcher and teacher at the Department of History, Lund University, mainly interested in the modern era. Before returning to the university in 2012 to begin his doctoral studies, he worked as a journalist for the popular history magazine Allt om Historia for a few years. Since then, he has wanted to combine his academic interests with writing for a wider audience.
“I am a curious person, and I tend to find almost any topic fascinating once I delve a little deeper into it – from the philosophy of history to early modern discovery,” Björn says. “That said, my expertise lies in twentieth-century history. My latest book was a biography of Gunder Hägg, the world’s most successful middle-distance runner during the 1940s and the first true sports star in Sweden. My next book will be about the space race during the Cold War.”

Tell us a little about your blog – purpose, themes, audience!

“I actually pursue two parallel blog projects at the moment. One is a Substack in which I try to make sense of the development of political affairs in Sweden by describing some underlying currents in modern history. The other is a blog about historical writing (broadly construed), in which I share my thoughts on the subject. The idea is to describe elements of style that can enhance academic writing as well as popular narratives: chronology, dialogue, setting, and so forth. My ambition is to make visible the strategies and decisions involved in the writing of history, whether it’s intended for scholarly publication or a local heritage organization.”

What were your first impressions when you started experimenting with ChatGPT? How come you say that it is ”time to be afraid”?
“To be honest, my first impression was “Wow!” Of course, I realized that it has obvious limitations. But its sheer confidence and its ability to address abstract problems in a clear style impressed me. Sure, I’ve seen AI applications do impressive things before, but this was something different. At least in terms of writing. That’s also the reason I said it’s time to be afraid. Mainly because I wanted to send out a wake-up call to fellow university teachers. I think higher education in Sweden, in general, is ill-prepared for this challenge. We need to think about our teaching methods, and how we write exams.”

What is your reflection as a historian when it comes to NLG technology?
“In philosophical terms, the implications are huge. This isn’t new, of course. We’ve talked about intelligent computers for more than 50 years. But in the last few years, we’ve seen a progress that is truly impressive. It’s not science fiction anymore, just science. First of all, this goes to the very heart of what it means to be human. Carl Linnaeus gave us the name Homo sapiens, ‘wise man’. For centuries, humans have defined their role in the world in terms of intelligence, the capacity for self-reflection, etc. What happens when we manage to create intelligence that surpasses us in terms of intellectual capacity? Will we pad ourselves on the back for this achievement, or rather re-identify what it means to be human? Will our uniqueness rather be our capacity for complex emotions?”

Do you see any similarities with other technical breakthroughs? Does this one stand out in any particular way?
“Yes, there are obvious similarities to the industrial revolution and the fear of mechanization. In narrow terms, the Luddites feared unemployment, but industrial societies have dealt with the social and cultural effects of industrialization – that is, modernity – for two centuries. One obvious difference is that Luddites feared that mechanization would replace manual labor. Now we fear that AI will replace intellectual labor, leaving only manual labor left for us.”

How do you think AI in general and NLG, in particular, will shape the course of history and the human condition?
“The key question is if AI will be used to address key global challenges or primarily an instrument to improve economic productivity. Our inability to address global warming has made me rather cynical about this at the moment. But there is of course also great potential. At least, in theory, we can hope to use technology to improve the quality of life. Maybe the six-hour working week is just around the corner? On the other hand, if we ever create artificial intelligence with a will-to-live or a will-to-power, we are likely to be in deep trouble.”

ChatGPT is changing the game

ChatGPT is changing the game

Recently, I was writing about the great changes brought upon by two events in the mid-1400s: firstly, the fall of Constantinople and, secondly, the European (re-)invention of the movable-type printing press by Johannes Gutenberg.

Whereas the fall of Constantinople has a well-defined date—29 May 1453—it is hard to say when the innovation of the printing press in itself took place. Gutenberg is known to have worked on his concept for more than a decade before his Magnum Opus was printed: the Gutenberg 42-line Bible, known as the first known book to be printed using mass-produced movable metal type in Europe. Unfortunately, it has no specific publishing date; we only know that finished copies were available in 1454-55.

When it comes to Natural Language Generation, though, the history books of the future may propose a very specific date when the game was changed regarding mankind’s use of written language: 30 November 2022.

Sure — many still see ChatGPT and its underlying technology as a toy and laugh at its shortcomings. But, as the Nobel Prize winner Bob Dylan once wrote, “it will soon shake your windows and rattle your walls”. Personally, I am just as fascinated as I am disgusted and just as curious as I am scared.



”AI copywriting doesn’t integrate well with my process” – An interview with (human) copywriter Giada Nizzoli

”AI copywriting doesn’t integrate well with my process” – An interview with (human) copywriter Giada Nizzoli

“I have my own process, and AI copywriting doesn’t really integrate well with it.” Giada Nizzoli, a UK-based copywriter, doesn’t consider AI copywriting a threat – at least not for the true craftsmen. In a recent blog post, she explained why. BY OLLE BERGMAN.

Working from Chester in England, Giada Nizzoli is a copywriter with a special focus on serving female entrepreneurs. Under the brand name Crafty Copy, she combines “marketing, SEO, and literary tricks to create texts that please both human readers and search engines”. In addition, she is a published author, writing both poetry and prose.

Recently, Giada presented five reasons why she’s not worried about AI copywriting stealing her job in a blog post with the title Why, as a Human Writer, I DON’T Find AI Copywriting a Threat. Her conclusion: “In my not-so-humble opinion, NO: AI won’t take over copywriting any time soon. Not for professional copywriters, at least.”

This sounded interesting, so I decided to get in touch!

Hi Giada! Who are your favorite human writers?

When it comes to fiction and poetry, I have waaaaay too many. I’ll try and narrow it down to Gabriel García Márquez, Emily Brontë, Lemony Snicket (a childhood favourite), Cesare Pavese, Edgar Allan Poe, and Donna Tartt.

In the marketing and copywriting world, I’m gonna go with Vicki Maguire, Mary Wear, David Abbott, and Julian Koenig.


When did you first hear about AI copywriting as a technology that could actually be useful? What was your reflection?

Fairly recently. Maybe a couple of years ago?

I was a little confused so I decided to try and look into it. To be honest, I got the same vibe as content mills that employ underpaid writers: a focus on speed, cheapness, and quantity over quality.

I also got approached by an agency that wanted me to churn out lots of weekly articles using a specific AI tool (and pay for that tool myself, would you believe that?!).

Needless to say, I refused.


Is there any (potential) AI copywriting functionality that could actually help you in your daily work?

Not at the moment, but … never say never!

The thing is: I have my own process, and AI copywriting doesn’t really integrate well with it.

I do however rely on other types of AI. For example, I use tools like Grammarly to catch typos (a lifesaver when I have to keep switching from British to American English for my clients!) and Hemingway to spot wordy sentences.

Like all AI, though, they should only be used with a pinch of salt. Some Grammarly suggestions are completely off the mark.

Basically, a handy starting point, but they still need a pair of human eyes.


My own thought is that ”if you compare the output of an NLG robot with that of a skilled writer, the difference you get is the essence of quality.” Do you agree?

I do if we’re talking about fiction or feature articles. However, in my industry (marketing and copywriting), I’d say the real difference is RESULTS.

Some AI copywriting tools can actually write pretty good-quality copy if we measure ‘quality’ by grammatical correctness and readability.

However, copywriting goes beyond that! It’s not about writing pretty words. It’s about crafting the right ones for a particular audience so that we can compel them to follow through with a specific action.

And, when it comes to stirring pain points or getting someone to feel a certain emotion, I don’t think a robot can do that successfully.

So, in my opinion, AI copywriting can read nicely but won’t convert into sales as much as the copy of a professional writer. A human writer, that is.


You claim that ”Writing is actually the smallest part of my job as a copywriter.” How is this relevant when we’re talking about AI copywriting?

Well, here’s the thing: that’s actually one of the main reasons why I can’t personally use AI copywriting.

There’s plenty I do before typing a single world. While this can change depending on the type of project, it usually involves having a conversation with my client, asking them specific questions through my project planner, taking the time to fully understand their brand and what sets them apart, analysing their target audience and their current stage of awareness, lurking on forums like Quora and Reddit to figure out what they think of the type of products or services that my client offers, researching the actual subject, analysing competitors to figure out how I can make my client stand out…

With AI copywriting, I’d still have to do all that and then find a way of turning it into info that can actually be processed by this tool.

Doing the latter sounds almost more time-consuming than writing the actual copy after having already conducted all the research and brainstorming.

But let’s say it works well for you: you add everything to your favourite AI writing software, and press a button. Once it churns out some words for you, you’ll start editing them, right?

In my opinion, that’s where you’d be compromising on creativity…. because you’d be editing what was already written by a machine instead of thinking outside the box and coming up with something truly unique.


Do you think you and I will have to regret our human hybris regarding text quality one day?

If we ever do, regret should be the least of our concerns.

I mean, if AI can write better than humans and produce juicier marketing results for my clients, then it’s probably taken over the world by then.

Run, Olle. RUN!


Bonus question: As an English native speaker, what do you think about ABBA’s lyrics?

Well, first of all, plot twist: English isn’t actually my first language. I’m Italian (does the stereotypical hand gesture), but I only run my business in English as that’s now the language I’m most comfortable with.

Let’s get to the point, though.

I love EVERYTHING about ABBA, from their melodies to their outfits and … yes, their lyrics, too. While some songs are quite light-hearted, some others are nothing short of emotional bombs, in my opinion. Like, the everyday imagery and melancholy in Slipping Through My Fingers. It genuinely makes me tear up more often than not.

Don’t miss Giada’s blog – it offers a lot of practical advice and interesting reflections which are relevant to all kinds of writers.

How to Live with the Robots –  a reflection by a professional writer

How to Live with the Robots –  a reflection by a professional writer

The text robots are here, bringing significant disruption, for better or for worse. Services based on Natural Language Generation, NLG, are popping up like mushrooms on the web—automated news writing, computer-generated product descriptions, and tools for creative writing are just some of the applications. The time has come for human writers to ask themselves: ”What makes my texts so unique compared to the output from well-written algorithms and powerful machine learning”?

To be honest, I didn’t see this coming. But, sitting at the computer typing away, I get distracted by a disturbing thought. Although I have been a paid writer for three decades, a little voice in my head keeps asking this stinging question: “Would this writing task be performed better by a text robot?”

What I am referring to is a digital tool—or AI, model, robot, or whatever you choose to call it – which produces natural language. State of the art is stunning: in all kinds of genres, the text generators are spewing out tailor-made texts which are perfectly coherent and icy logical, with no errors whatsoever when it comes to spelling and grammar. Undoubtedly, this technology has climbed out of its cradle and is entering the commercial arena – not toddling but rather swaggering across the floorboards.

Automated writing = skillful writing?

Accordingly, a growing number of companies are serving their customers with tools for automatic text generation. Here are some examples:

  • Game reports in sports
  • Weather forecasts
  • Real estate descriptions
  • Product copy
  • Financial reports, and analysis

When it comes to this kind of content, the reader doesn’t expect any creativity or “out-of-the-box thinking”–quite the contrary! The British writing consultant Susannah Ross once wrote that ”Effective writing is writing that does its job.” Thus, for these texts to serve their purpose, they should be brief, well-structured, and written in clear, plain language. 

The writing coach within me doesn’t have much to put up against this. Reluctant as I may be, I have to admit that this new generation of clever text algorithms is apparently based on the same writing type of instructions and advice I am trying to convey during workshops and courses. As a matter of fact, I am very much for boilerplate-based communication! In the box below, there is an example of a manual protocol that ”mechanically” gives a useful output in a reliable way. It would be straightforward to demonstrate a similar recipe for a press release or a web page.

The elevator pitch—using a pre-NLG “manual algorithm”

I often give courses about practical communication skills to early-career scientists and young entrepreneurs, and one of the most popular topics is ”The elevator pitch.” During the research for this article, I realized that I had used an approach similar to the one you use for automated, data- and questionnaire-based journalism and marketing.

This is what the protocol looks like:

1. Regarding your project, reply to the following questions. (Be spontaneous and don’t think about the purpose or the end result!):

  • What is it about?
  • What problems does it solve?
  • How is it different?
  • Why should I care?

2. Go and fetch a coffee and sit down with your replies. Edit them into a coherent text, about 100 words long.

3. Read your written text aloud and make the changes needed to give it some personality and some flow. 

4. Congratulations – you now have a text you can use not only as an elevator pitch but also as a LinkedIn summary or a bio! (Of course, you should always adopt the text for any new context in which it is used.)

Acknowledgment: the method described here is based on the teachings of the American communication coach Carmine Gallo, for example, in his book Presentation Secrets of Steve Jobs (2009).


GPT-3 brings “creativity” to automated writing

But how about creative writing? With this, I mean texts that should show a particular style, tonality, or originality to fulfill their purpose. For example, it could be a cover letter that should mirror the applicant’s personality in order to land them their job. Or it could be marketing or PR copy that should affect the feelings and attitudes of the reader—and ultimately, the reader’s behavior. What about literature? Would it be possible to automize the generation of customized romance novels, where the reader is the love object, or weekly episodes of a never-ending fantasy series, where the reader has designed the main characters like in an RPG (role-playing game)? To explore these questions (as time is still too early for proper answers), we must bring GPT-3 to the discussion. 

You’ve probably heard about GPT-3. This digital super toy from OpenAI—a California-based AI research and deployment company—has created quite a rave since its launch in May 2020. The acronym stands for ’Generative Pre-trained Transformer 3’, which doesn’t say much to most people. Techies would describe it as an “autoregressive language model that uses deep learning to produce human-like text.”

Basically, GPT-3 works like this. If you feed it with a prompt, for example, a couple of sentences, it will return sentence after sentence of natural language. As the model thoroughly has studied how humans tend to combine words in millions of example texts, the output will, in many cases, be flawlessly well-written and make perfect sense. 

Hence, the first minutes with freeform GPT-3 exercises are mind-blowing for text professionals. If you simulate a conversation, it will return interesting replies which are eerily human-like. If you use a piece of Eliot’s or Dickinson’s poetry as the prompt, something surprisingly engaging will emerge. If you add marketing copy, GPT-3 will play the role of a gifted junior colleague delivering the first drafts, ready for human refinement.

However, the problem with GPT-3 soon breaks the surface of this river of words and stares us in the face. The darn thing is not only a chatterbox but a compulsory liar. Without hesitation, it makes statements that are just not true. It’s a beast, no doubt about it, but a beast that should be shackled – just as the Norse gods put a magical chain around the neck of the monstrous Fenrir wolf.

Some useful yet ethical ways of using natural language technology

There is no doubt that NLP technologies in general (where “P” stands for “processing—see fact box) are very useful for professional writers. As a matter of fact, we use it all the time, for instance, when we let Google or Facebook translate other’s web pages or our own snippets of text. Another example is when typing assistants like Grammarly review spelling, punctuation, grammar, clarity, and engagement.

NLP – a caleidoscope of applications

NLP, Natural language processing, is “a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language” (Wikipedia). It is often divided into NLU, where “U” stands for “Understanding,” and NLG, where “G” stands for ”Generation.”

NLP has become a vast area when it comes to applications:

  • Autocompletion & -correction
  • Grammar checkers
  • Voice assistants
  • Information search 
  • Translation
  • Data mining
  • Chatbots
  • Automated journalism
  • Targeted advertising
  • Text analytics & summarization
  • Social media monitoring
  • Recruitment
  • and so on …


Here are some situations—wherein my personal opinion—text robots can be our friends without us crossing any lines regarding neither work ethics nor professional dignity.

Just putting the words together.
Probably, there are very few writers who feel passionate about writing standardized texts where the data and information are served from the beginning, the format is set, the style should lack all traces of personality, and no creative initiatives are allowed. Accordingly, I do not see it as a sign of a decaying civilization if we let robots write weather reports, product descriptions, and commonplace local news.

Rewriting short fact texts.
Let’s be honest: as writers, we all need to write short captions or dry fact boxes sometimes, where the most effective way to proceed is to copy-paste from a trusted source and just knead the prose a little so that your version clearly differs from the original. Personally, I think it would be ok to let a text robot do these kinds of tasks as the texts in question are there just to convey facts and not to bring along a style, an attitude, or a story.

Creating varieties of headers, slogans, and taglines
Some of the new tools are designed to spew out a number of different versions of the same text snippet. This can, for example, be used for brainstorming suggestions for headers or to perform so-called A/B testing of an internet advertisement (“Which alternative, A or B, gives the highest number of clicks?”)

Paraphrasing styles
Let’s say you need placeholder text in the form of sloppy business writing or teenager lingo in social media. Here, text robots can serve as intelligent boilerplate text (“lorem ipsum”) generators.

Literary inspiration and exploration
For centuries, authors have experimented with different ways to create poetry, prose, and drama. A number of these experimental methods are based on imitation, appropriation, free associations (sometimes under the influence of drugs), and so-called aleatoricism – that is, artistic compositions resulting from “actions made by chance.” An example of the latter is the so-called cut-up technique, in which a book page is cut up with a pair of scissors and rearranged in new ways. Obviously, authors and artists will find new ways to create literature and art with NLP-based technology.

Getting ready to face change

As of 2022, how should we, professional writers, approach this development? Obviously, it is hard to make any confident predictions as we are rushing into uncharted territory with a few similar scenarios from history to learn from. In addition,  the professional discourse still is restricted within the tech field, and the practical and professional body of experience is minimal. Still, I dare to contribute my 50 cents in the form of some reflections.

Automated texts will be a part of everyday life. We will read a lot of texts written by robots without knowing it; the output of a well-programmed NLG system may just be as clear and structured as a text written by a rushed junior at a news office – if not better!

There will be a tidal wave of shallow and pointless “content.” Marketers and SEO consultants will boost their output of texts that pull us into some kind of sales funnel. Probably there will soon be tools that on a day-by-day basis can spot trending subjects in the same way as, for example, Twitter spots trending hashtags. From these observations, the device can instantly collect text material from all over the web and put together utterly short-lived articles that no one cares about, but many skim-read anyway.

Plagiarism will explode. NLG tools make it extremely easy to rewrite texts—same factual content, same structure, and disposition, but everything reordered and re-formulated. This will be a nightmare for a number of professions—from journalists to university teachers.  

We will get rid of poorly translated product descriptions. As any speaker of a small language has noted, many web shops use poor machine translation from English instead of proper sales copy and item descriptions. Hopefully, the customer experience will be enhanced by automated product descriptions generated by language-specific algorithms.

For the human writer, who takes pride in being a person of letters, style, clarity, and knowledge, the message should be: “Get ready to change your mindset!” NLP technology will not go away, but it will change the way you look at different types of text assignments. To end this on a positive note, I think NLP can serve the same purpose as the lamp-based pacing system used in athletics: to perform at our best, we must make sure that we keep at least one step ahead of technology. 

So, be open-minded and keep learning new tools & techniques. In addition, stay eager to define, produce & identify quality. Your most enviable traits as a skilled writer are still tough to imitate: a context-based sense of style, purposeful imagination, and sound judgment at all times. And perhaps most important of all: being a human writer, you also understand the human reader – the person we all serve in the end.

Thanks to:

  • Jakob Klöfver, Cowrite, and Jonas Jaani for valuable explanations and enlightening discussion. 


Please note: all texts on this blog are produced by me, Olle Bergman, and invited human writers. The only AI-based language tool used is Grammarly – the smartest typing assistant you can imagine. When robot-written sections are added to serve as examples or to demonstrate a point, this is clearly indicated.

“We have weaved a 2000-year rhetorical tradition into our algorithm” – an interview with Magnus Paues, co-founder of Cowrite

“We have weaved a 2000-year rhetorical tradition into our algorithm” – an interview with Magnus Paues, co-founder of Cowrite

Stockholm-based Cowrite has created an NLG platform for what they call “augmented writing”. Their Cover Letter Builder, which is founded on wisdom from classical rhetoric, has been quite a success; according to one of the biggest clients, their users get employed 48 days faster than average.

Friends, contacts, and students who follow me, know that I am energized by the forcefield between tradition and novelty, between the enduring and the disruptive. Or as Swedish poet Gunnar Ekelöf once put it: “Det bestående är bra. Oppositionen mot det bestående är bra.” (‘The established is good. The opposition towards the established is good.”). Accordingly, I am fascinated by the way the NLG-focused tech companies are creating alliances between, on the one hand, computer techies and, on the other hand, linguists and rhetoricians.

After studying Cowrite’s template-based Cover Letter Builder and finding it very cool, I got in touch with co-founder Magnus Paues for some questions.

Hi Magnus! Please tell me the background story about Cowrite!
Cowrite started in 2014 with the objective to solve the problem of writer’s block. So much content is written in the world every day, and so many people think it is really hard. We wanted to provide a service to help them overcome the obstacles. We started out with real estate ads and recruitment ads and added cover letters and CVs after the outbreak of Covid-19.

What is your most popular service today, and why?
By far, it is our Cover Letter Builder that is the most popular service. It is provided in both English and Swedish, thus directed to a huge market. And, it solves a big problem. People, in general, seem to have an issue with describing themselves in a positive way. Also, few people know how to structure a text in order for it to be sold. Our Cover Letter Builder helps them with that.

Please tell me how the Cover Letter Builder came to be!
In March 2020, the world came to change in a very short time with the outbreak of Covid-19. Momentarily (thank god), our existing business areas more or less stopped dead in their tracks and we needed to find an alternative that produced revenue quickly. We realized that the unemployment rates rose, and therefore also the need for people to get help with job applications. We released the service to the Swedish and international markets in May 2020. And, wow, what a journey we were in for. Even with our former business areas back on their feet, the Cover Letter Builder came to be our biggest business area, and it just keeps on growing.

How does the CLB work from a user perspective?
Easy! The user answers questions about herself/himself, chooses tonality, and the cover letter magically appears in the adjacent document, in real-time. The cover letter is divided into text blocks that can be varied, which means that no two cover letters will be alike. The user can, of course, edit the text anytime in the process.

Please tell me about the structure of the letter created and how it is connected to classical rhetoric.
We have weaved a 2000-year rhetorical tradition into our algorithm, which means the text will be structured in the classical rhetoric disposition. The user’s cover letter will be built on the same building bricks that Aristotle used to persuade his fellow Greek citizens to believe whatever he had in mind when he woke up on a particular day. In short, the text has a background, an introduction, a statement of facts, arguments (proof of the fact), and a conclusion.

How do you think services like this will influence professional life and society in general? What do you see in the crystal ball in, for example, ten years?
I think there will be a lot of really great, automatically written content out there. Regarding cover letters: I think that our service will render cover letters obsolete in the long run. Today, cover letters are generally used as a “Sorting Hat” for candidates who can’t write. Many jobs don’t require writing skills, but your writing skills (or lack thereof) are what makes you miss or gain your opportunity. I think our service will make recruiters realize that the interview is the only forum where they can make a real assessment of a candidate since all cover letters will be absolutely perfect. So, less discrimination and more interaction, that’s what we aim for in the long run.

Please note: all texts on this blog are produced by me, Olle Bergman, and invited human writers. The only AI-based language tool used is Grammarly. When robot-written sections are added to serve as examples or to demonstrate a point, this is clearly indicated.

“Even when GPT-3 failed, it was producing thought-provoking poetry” – an interview with Jukka Aalho, author of Aum Golly

“Even when GPT-3 failed, it was producing thought-provoking poetry” – an interview with Jukka Aalho, author of Aum Golly

Last autumn, Jukka Aalho, writer and freelance marketer from Oulu, Finland, sat down and wrote a poetry collection together with GTP-3 in 24 hours. The result was “Aum Golly: Poems on an Artificial Intelligence” (2021). 

if kites flew like boomerangs
and all the stars were made of glass
the moon’s face was a silver mask
and the sun a golden apple
and I saw a storm of diamonds
sow the sky with light
I saw a river of stars
dance down the night
I saw a hundred flying horses
streaming through the sky

The poem you just read is taken from a poetry collection with two authors on its cover. Firstly, Jukka Aalho, who is a Finnish writer, TEDx speaker, and full-stack marketer. Secondly, GPT-3, who is an autoregressive language model that uses deep learning to generate text that imitates the way humans write.

As an avid poetry reader (with favorites like H. Martinson, Inger Christensen, and R. Carver), I had to learn more about this cooperation.

Hi Jukka! Please tell me about your background and ambitions as a writer and a marketer!

I’m always looking to try new things, learn something new and have fun while doing it. Both are possible as a writer and as a marketer, but not inevitable.

Please mention three favorite writers (copywriters, non-fiction writers, poets, or novelists)!

I’ve enjoyed tremendously reading Haruki Murakami, the short fiction of Lydia Davis, and the wrapping up of the Wheel of Time by Brandon Sanderson.

How did you get the idea to produce an AI-generated poetry collection?

I tried the beta version of GPT-3 and was astounded by how good it was. I tried generating different genres (articles, poems, fiction …) and realized that poetry was the best fit for me. Even when GPT-3 failed, it was producing thought-provoking poetry.

How did you proceed from a practical point of view?

I set myself a deadline of 24 hours to generate the book. One Saturday morning, I brewed myself a cup of coffee and started banging out poems with GPT-3. Twenty-four hours later, Aum Golly was finished and ready to be shipped to publishers.

I was the weakest link in terms of stamina. I had to take breaks and even sleep (gasp). GPT-3 would’ve gone on tirelessly.

Imagine that you met Pentti Saarikoski or Elmer Diktonius. How would you present your project, and how would you persuade them that this is a useful technique?

I would show them what using GPT-3 is like. We would laugh at the poor quality. And cry at the mediocre. By the end of the night, we’d be hitting each others’ backs and telling ourselves that AI will never replace good old-fashioned human creativity. Then I would reveal that they’re actually not real but rather AI-generated spirits themselves.

What kind of feedback have you received from the current literary arena?

Both good and bad. And totally neutral. Many have found the project to be an interesting trial. Some have said that AI-generated poetry is garbage. Some have said that the poems are pure gold.

Has your project changed your view of creative writing? Do you read texts differently now?

I have a more realistic view of what creativity will look like in the future. Especially when it comes to marketing and commercial texts, the future is already here. If your only goal is to produce as much mediocre content as possible, machines are already much better at it than humans.

Is there a bridge between marketing and poetry, between copywriters and poets? If so, please describe!

Oh yes. Copywriting is poetry with a goal.

There’s a saying that every copywriter has an unfinished manuscript lying in their drawer. I tend to think that every poet has a slogan hidden somewhere in their notebook.

What is your message to writing colleagues?

You’re the best! Writing is a great hobby, and just because machines keep getting better at it, it’s not a reason to stop writing.

Aum Golly (2021) can be purchased online here

Please note: all texts on this blog are produced by me, Olle Bergman, or invited human writers. When robot-written sections are added to serve as examples or to demonstrate a point, this is clearly indicated.