20 Jun How to Work with The New Wave of AI
Could AI write this chapter on its own, reflecting on the role it might play in the future of media and media innovation? For a global media publication read by executives and editors around the world? That thought might have seemed absurd six months ago, but here we are in April 2023, a mere five months after the official launch of the internet’s new viral sensation ChatGPT, contemplating just how well it might take to the task. And so here is ChatGPT answering the prompt: “How should a chapter on AI start for the Innovation in Media World Report?”
“Artificial Intelligence (AI) has become a buzzword in the media industry in recent years, promising to revolutionise the way we create, distribute, and consume content. With the explosion of data and the increasing demand for personalised experiences, AI has emerged as a powerful tool for media companies to improve their workflows, engage their audiences, and drive business growth.
However, the impact of AI on the media industry is not just about technological advancements. It raises fundamental questions about the role of journalism, the ethics of data collection and analysis, and the relationship between humans and machines. As AI technologies continue to evolve, media organisations need to understand the opportunities and challenges they present, and to develop strategies to leverage them effectively.
In this chapter, we will explore the latest trends and innovations in AI for the media industry, and examine how media companies are leveraging AI to create new forms of content, enhance user experiences, and drive business growth. We will also examine the ethical and societal implications of AI in the media, and consider how media organisations can navigate this rapidly evolving landscape to stay ahead of the curve.”
If you have used ChatGPT you’ll probably agree that watching an AI system churn out these words in fewer than 20 seconds makes you think that many, many things are going to change. As we write this chapter, as per reporting by The New York Times, more than 1,000 technology leaders and researchers, including Elon Musk who co-founded OpenAI, the company that created ChatGPT, have urged artificial intelligence labs to pause development for at least six months. They have warned in an open letter that AI tools present “profound risks to society and humanity”. The letter says that AI developers are “locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one — not even their creators — can understand, predict or reliably control”.
Whether or not you share the same views about the risks of AI, it’s hard to argue against the disruptive potential of the machine-creative revolution we
are witnessing. “The death of artistry”, “[t]he end of high school English” (and the college essay) are just some of the headlines coming out of other fields. For publishers, the mission and vision around AI has to be clear if we must, in ChatGPT’s words, stay ahead of the curve. We can do that by addressing some key questions:
1) How do we make AI work for us? Conversational AI can do many things, but they rely solely on what has happened and been documented.
Compared to virtually every other industry, news publishers have the upper hand here because only editors and human beings can tell original
stories and report from the ground. AI cannot literally write the future of our industry but it can help make the work of journalism more efficient.
2) How can newsrooms leverage the power of AI for journalistic work without becoming over-reliant on it? It’s fairly clear already that generative AI like ChatGPT can produce large volumes of content. While that may be useful, news oganisations have to formulate a code or guidelines for what it can and should not be used for.
3) How can we hold AI accountable? Generative AI systems like ChatGPT have been trained to a large degree by the content that we have produced. This raises important issues about sourcing, trust, and payments to content creators, especially as many of these AI are becoming commercialised.
THE THREE WAVES OF AI
Let’s rewind a bit here and zoom out to a larger perspective on journalism and AI. It was just one year ago, writing a chapter on AI for this book, that we
documented several use cases on AI helping publishers with personalisation and predictive paywalls. And while we started to detail some instances in which news organisations used AI to generate stories, we did wager that we were yet to reach the point of robots writing cogent stories.
ChatGPT changed that entire narrative, of course, popping up suddenly and vividly like something straight out of sci-fi. But it’s worth re-emphasising
that outlets have been using AI to support — and even produce — journalism for some time now. Speaking to the Reuters Institute for the Study of Journalism (RISJ) in a recent piece, Francesco Marconi, a computational journalist and cofounder of the real-time information company AppliedXL, categorised AI innovation in the past decade as occuring in three waves: automation, augmentation and generation.
During the first phase, “the focus was on automating data-driven news stories, such as financial reports, sports results, and economic indicators,
using natural language generation techniques”. News publishers such as Reuters, AFP and AP were automating some content.
We can chart a few prominent examples here, among many:
l The Associated Press uses AI to produce data-driven sports stories and templated game recaps.
l McClatchy has used automation to cover local real estate sales.
Financial newswires like Dow Jones, Bloomberg and Reuters have experimented with automation to streamline coverage of earnings reports and the stock market.
PA Media’s RADAR newswire has generated more than 600,000 articles since it was launched in 2018, each a local story extracted from a national dataset.
Stavanger Aftenblad in Norway covers 10,000 junior league football matches a year using automated text and a single reporter. According to Marconi, the second wave arrived when “the emphasis shifted to augmenting reporting through machine learning and natural language processing to analyse large datasets and uncover trends”. Take the Argentinian newspaper La Nación for instance, which began using AI to support its data team in 2019. It then went on to set up an AI lab in collaboration with data analysts and
developers. (It’s a fascinating example, and we’ll cover it in a separate section in this chapter so look out for that one!)
The third and current wave is generative AI, according to Marconi, and it’s “powered by large language models capable of generating narrative text at scale”. This new development offers applications to journalism that go beyond simple automated reports and data analysis. Now, we could ask a chatbot to write a longer, balanced
article on a subject or an opinion piece from a particular standpoint. We could even ask it to do so in the style of a well-known writer or publication.
Part of the reason why ChatGPT and other tools have generated so much excitement is that they are so consumer-friendly and can communicate in natural language, says
Madhumita Murgia, who was appointed AI editor at the Financial Times in early 2023, in the same Reuters Institute piece”. Her role was newly created at the FT, to lead coverage about the rapid developments in AI. “It feels like there’s an intelligence there, even though it is really still just a very powerful sort of predictive technology,” she says.
Now that the technology is so advanced and accessible, reporter Sara Fischer of Axios notes, it’s become harder for newsrooms to draw the line between leveraging AI and becoming over-reliant on it. That’s an area that has already caused some recent controversy in media organisations that have begun to test just how porous this line may be.
A quick caveat before we dive headlong into AI and the newsroom: the meteoric rise of ChatGPT has given rise to an unprecedented volume of writing and reflection on the power of AI and the implications that it could have for journalism. Some are outright doom mongering, and others have chosen instead to look at the potential value that AI can bring to publishers. In many of these articles and reflections, there is a merging of generational AI technologies, a very new wave of AI, with older iterations that automate
tasks, and can now be seen as an older wave. Does this distinction matter? “What ties this disparate collection of technologies together is their ability to process large volumes of information, to a greater or lesser degree, on their own,” media analyst Peter Houston writes. For now, as we move on to the next part of this chapter, that is a useful framework to think of all AI technology, with the acknowledgment that new tools like ChatGPT have supercharged their potential utility. (And a side note: the use of AI for things like dynamic paywalls, personalisation and understanding user preferences may already seem a little mundane, at least in this moment, but the use of machine learning for
these functions remains vital for news organisations. You can find a detailed round-up of these “older” technologies in our AI chapter from last year).
GENERATIVE AI IN THE NEWSROOM
Earlier this year, the digital outlet BuzzFeed said it was using OpenAI’s publicly available software, much like the popular generative text site ChatGPT, to automatically publish its popular quizzes. “To be clear, we see the breakthroughs in AI opening up a new era of creativity that will allow humans to harness creativity in new ways with endless
opportunities and applications for good,” the company’s CEO, Jonah Peretti, said in a memo to staffers.
BuzzFeed said it doesn’t plan to use AI to write journalistic articles. While that seems to be a line most publishers are not eager to cross, Futurism did note recently that BuzzFeed had quietly started publishing fully AI-generated articles that are produced by non-editorial staff. The publication identified 40 or so articles, all of which appear to be SEO-driven travel guides, which they noted were comically bland and alike.
Some select examples:
l “Now, I know what you’re thinking —
‘Cape May? What is that, some kind o mayonnaise brand?’” in an article about Cape May, in New Jersey.
l “Now I know what you’re thinking —
‘but Caribbean destinations are all just crowded resorts, right?’” in an article about St Maarten, in the Caribbean.
l “Now, I know what you’re thinking. Puerto Rico? Isn’t that where all the cruise ships go?” in an article about San Juan, in Puerto Rico.
l “Now, I know what you’re thinking — bigger isn’t always better,” in an article about Providence, in Rhode Island.
l “Now, I know what you’re probably thinking. Nepal? The Himalayas? Haven’t we all heard of that already?” in an article about Khumbu, in Nepal.
Do these cases show us where generative AI is currently at and how it can fit into a news operation? In its first iterations at least, AI of this kind will be an efficiency tool used to generate new output based on what it was trained on. But who knows what subsequent iterations might bring and how fast they may come at us.
BuzzFeed wasn’t the only one to introduce AI by a little bit of stealth. Earlier this year it came to light that CNET, a massively popular tech news outlet, had been quietly employing the help of “automation technology” in its financial explainer articles. It seemed to have started doing this around November 2022.
According to the science and tech news platform Futurism, the news sparked outrage. Critics pointed out that the experiment felt like an attempt to eliminate work for entry-level writers, and that the accuracy of AI text generators, at least at the time, was notoriously poor. The Verge reported meanwhile that the AI tools were being used with little transparency to readers or staff. CNET never formally announced the use of AI until readers noticed a small disclosure. “We didn’t do it in secret,” CNET editor-in-chief
Connie Guglielmo told the group. “We did it quietly.”
Following the backlash, and after being called out for inaccuracies in articles written by AI tools, CNET later said that it would pause its AI publishing experiment.
ACCEPTING AI
By the time the CNET story wound down, it was already quite clear that the generative AI wave was gaining momentum and it would be a hard thing for publishers to disavow. The debate surrounding the CNET debacle was surprisingly nuanced, as the reactions below demonstrate.
Gael Breton, the digital marketing and SEO expert who first spotted CNET’s use of AI thought it was time for everyone to start adapting to this new technology. “Is it good or bad? [It] doesn’t matter because it’s happening anyway. What matters is how we use this technology and how we make sure it’s not used to spread fake news,”
he told Euronews.
“Not all content is equal, so the impact will not be equal,” Eddie Kim, founder and CEO of Memo, a company that uses AI to analyse new articles, told
Axios.
“I do think that the less crucial to our democracy, the less politically sensitive, the less divisive the topic of news, the more you can afford to try
something,” Ariane Bernard, a media product expert who leads the Smart Data Initiative at the International News Media Association, also told
Axios. “The ability to audit how the AI did what it did is very thin, and that’s always going to be a concern for the news industry,” she added.
Others took a harder line on using AI to write articles, though keeping the door open for other types of uses. “We will never have an article written by a
machine,” Neil Vogel, CEO of Dotdash Meredith, one of the largest print and digital publishers in the US, told Axios. “We’re not denialists,” he added.
“We actually think it’s an incredible opportunity for us,” noting that the company has already begun to use AI for some tasks like sourcing images. Which brings us to some
comprehensive guidelines, suggestions, and insights for how to adapt to generative AI and how to use it to help journalism and make organisations more efficient.
AI AND IMPROVING THE WORK OF JOURNALISM
Marcela Kunova, in a piece for journalism.co.uk published earlier this year, writes of some of the reliable and ethical ways in which journalists and newsrooms could leverage the power of ChatGPT on a daily basis, ranging from generating summaries, emails and social posts to providing quotes:
1.Generating Summaries of Large Texts and Documents ChatGPT is fairly good at summing up long pieces of text. This comes in handy when you need to quickly scan new reports, studies and other documents. You can even ask the tool to give you the most important points, pick up a quote or find information about the author(s).
2.Generate Questions and Answers When you are working on a topic you are not that familiar with or looking for new angles, ChatGPT can help you conduct research. For instance, you can ask it to give you names of experts to interview about a given topic and it generally comes up with sound suggestions. However, if the topic is too niche, the tool may generate totally plausible names that sound like they could be experts but are not in fact real.
3.Providing Quotes You can ask ChatGPT to look for quotes from a particular individual and chances are that it finds them. However, check where a quote comes from as it
could be another writer’s work — and this is plagiarism — or it could be made up. Since AI is not bound by the same ethical standards as human journalists, it may include quotes from sources that do not actually exist, or even attribute fake quotes to real people. This could lead to false or misleading reporting, which could damage the credibility of the news organisation. It will be important for journalists and newsrooms to carefully fact check any articles written with the help of AI, to ensure the accuracy and integrity of their reporting.
4.Generating Headlines If you are doing A/B testing, challenge yourself with your headlines vs those of the AI. You can ask it to make the headline funny, negative, or positive, remove the jargon or use a specific number of words. Shame that ChatGPT really struggles with maths always count the words in the final result and ask it to rewrite it if it makes a mistake (it almost certainly will).
5.Translating articles into different languages Like any other AI-powered translation tool, it is very approximative and you will have a few good laughs along the way. However, it can come in handy if you need to get a general sense of the text in a different language. If you want to get something a bit more accurate, you better stick with Google Translate.
6.Generating Email Subjects and Writing Emails Outsourcing one of the most tedious office tasks to a machine sounds like a dream. ChatGPT can speed up the process of emailing your sources or colleagues as you can generate a sound message with one swift prompt. Just fill in the blanks and send it away. A genuine time-saver. But remember, you will need to edit the final version.
7.Generating Social Posts Like emails, posting on socials is useful but terribly time-consuming. You can ask ChatGPT to write a tweet or a LinkedIn post on a topic, freeing
your time and brainpower for more worthwhile writing.
8.Provide Context for Articles You can ask ChatGPT to give you context about a news story, say, why members of the UK rail unions are on strike this year, and it can find
quite accurate information. But the usual warning applies: double-check everything. It can also explain howsomething works, which can be useful
if your piece needs a short explainer in plain language. Kunova’s excellent guide was a much needed resource for journalists and particularly useful coming at a time
when the fear around AI had reached a bit of a fever pitch. More articles of a similar vein followed, because of course, ChatGPT is just one of the many AI tools in the journalism landscape. Generative AI can be used in several other ways too, especially in our swiftly evolving world of multimedia.
9.Image Generation Generative image models like DALL-E 2 and Stable Diffusion could deliver custom stock photos to newsrooms that cannot afford subscriptions to photo services. These tools have also allowed users to generate realistic images based solely on text prompts. Some technology-focused reporters have already begun using AI-generated images to illustrate their stories, more as a gimmick than anything else. But as these technologies continue to improve and enter the mainstream, they are likely to
reshape newsroom debates over photo manipulation, photo illustrations, and misleading stock photos.
10.Transcribing Interviews The latest automatic speechrecognition models will allow reporters to easily and accurately transcribe their interviews for free.Many journalists already upload their interviews to paid auto-transcription services like Otter and Trint, which rely on machine learning models to quickly produce fairly accurate transcriptions.
The Whisper model, released by artificial intelligence company OpenAI in September, is even more accurate, doesn’t require an internet connection, and is completely free.
11.Other Multimedia Generative AI is becoming pretty handy in the generally timeconsuming world of multimedia production. Descript — an audio and video editing app that got investment from OpenAI last year — works a bit like Otter.ai in generating transcripts from audio, but also lets you edit the audio from a text document rather than an audio waveform.
This saves loads of time as it’s much easier to edit words by looking at them written out, rather than looking at them represented by some squiggly bumps on a waveform. It also highlights the “umms” and “uhhs”, letting you remove those automatically. Generative AI is also speeding up the task of finding music for podcasts. If you don’t have the budget to pay a composer to write a score for your episode, you would have previously had to trawl through libraries of royalty-free music in search of a suitable track. Having to listen to endlessly badly made songs before finding the right one was both timeconsuming as well as soul-sucking. AIVA is a Luxembourg-based startup that lets you generate songs based on preset styles of music, and even lets you choose the key. Even if you’re not a musician, you can select major keys for more upbeat tracks and minor keys for songs that are sadder or heavier. Our experience has shown that we were able to find suitable music much quicker with such generative tools.
AI’S NEW FACT CHECKING ROLE
AI is notorious for producing incorrect information that needs to be vetted through human intervention. But experts like Sam Guzik, who leads product strategy for WNYC and is a foresight expert advisor at the Future Today Institute, predict that algorith- mic fact checking will go mainstream in 2023. Teams of computer scientists around the globe have already developed AI systems designed to detect manipulated media, misinformation or fake news. A team from Drexel University recently published a new approach for detecting forged and manipulated videos. Their system combines forensic analysis with deep learning to detect fake videos that would slip past human reviewers or existing systems.
Of course, fact checking isn’t usually so straightforward. As Guzik tells NiemanLab, “A quote might be accurate, but misleading. Every news story is built on a subjective frame of what’s included — or excluded. That nuance, however, is lost when researchers test a new AI model against benchmark datasets that catalogue posts as true
or false.”
In an article for Poynter, Borja Lozano and Irena Larraz from the fact checking organisation Newtral write: “For us, the question is also how these generative AI systems can contribute to the automation of fact-checking. For example, it is possible that tools like ChatGPT could improve the language models we are already working on for claim detection, check-worthiness, claim matching to compare claims with what is already verified, and data validation to check facts, all in an automated way. There is even more potential if the model’s answers could include not only citations to reliable sources, as OpenAI expected in 2021, but also their links.”
AI AND REVIVING LOCAL PUBLISHING
Aside from the mundane tasks that can be accomplished using AI, AI can revolutionise the working of media in other ways. One example is the fillip it can give to local news and publishing. Bill Grueskin, a professor at the Columbia Journalism School, tells NiemanLab that editors at struggling metro dailies, or thinly staffed nonprofits that are charged with covering government bodies, will someday look at this as a boon. “Local newspapers and sites are getting thin these days… They also aren’t covering
as many school boards, legislative committees, real-estate sales, newbusiness openings, and the rest of the grist that used to fill the back pages of newspapers. Even obituaries are largely relegated to paid notices from relatives. And as this information dries up, citizens feel more estranged from the agencies that govern their lives and the officials who set their tax rates and hire their superintendents.” If this commodity news can be automated by AI, we can provide a lot more information — much of it useful, if not
sexy — to people who need it. In NiemanLab’s Predictions for Journalism 2023, Joe Amditis, assistant director of products and events at the Center for Cooperative Media at
Montclair State University, offers his two cents: that this kind of new tech could impact small- and mediumsized local news organisations — particularly those with fewer than
five full-time people on staff. Cecilia Campbell, chief marketing officer, United Robots echoes this, predicting that local outlets will now embrace “the opportunities the tech offers to increase the breadth of topics and the number of stories they can cover, and the size of the audience.”
AI’s advantages could save independent and hyperlocal news publishers an incredible amount of time and effort. The ability to quickly generate documents, instructions,
guides, and various elements of internal infrastructure, generating summaries of public meetings and documents, creating tweets and social posts from news stories, drafting scripts for news broadcasts, even suggesting different headline variations — all at the click of a button — would be a game-changer for news organisations that are already strapped for people and resources. The same thing goes for generating invoices, public records requests, and even basic outreach emails.
Amditis has created a new ebook called the Beginner’s prompt handbook: ChatGPT for local news publishers. In the book, he has included dozens of possible use cases for local newsrooms, a step-by-step breakdown of the anatomy of a ChatGPT prompt, and beginner, intermediate, and advanced prompts for local publishers. He sees many advantages: not only does AI free up time for journalists to report or engage with community members, it “could also help make local news content more accessible to
people with disabilities, people who speak English as a second language, and people who might not usually come across local news stories in their current format”.
AI FOR COST EFFICIENCIES
Granted, this is an area where things get a little uncomfortable when speaking about AI and the impact on media, but a number of publishers have, understandably perhaps, started touting the cost-saving potential of the new technologies.
Gannett has made investments in AI and machine learning tools to simplify routine tasks, such as selecting and cropping images quickly, personalising content and gathering datasets to inform readers on where to watch various sporting events. Doug Horne, CFO at Gannett, said during the company’s earnings call earlier this year that this is to achieve an annual savings of at least $220 million this year. AI will be used to “create efficiencies”.
The Arena Group recently announced partnerships with AI firms like Jasper and OpenAI “to give our reporters and editors the ability to quickly and efficiently search and pull content from our rich archives for news stories,” said Ross Levinsohn, CEO of The Arena Group. Levinsohn noted “productivity improvements” since testing the use of generative AI. When The Wall Street Journal reported in February that The Arena Group was using AI to produce articles, shares jumped 18%. “We do not intend for this to replace our talented writers and editors. But we do believe this initiative will make our great teams even more productive.
These are just a few of the examples of how we are proactively adjusting our cost structure to ensure that we hit our 2023 adjusted EBITDA target,” Levinsohn said. Similarly, earlier this year the German media group Axel Springer said journalists are at risk of being replaced by artificial intelligence systems like ChatGPT. According to The Guardian,
the announcement was made as the publisher sought to boost revenue at German newspapers Bild and Die Welt and transition to becoming a “purely digital media company”. It said job cuts lay ahead, because automation and AI were increasingly making many of the jobs that supported the production of their journalism redundant.
“Artificial intelligence has the potential to make independent journalism better than it ever was – or simply replace it,” CEO Mathias Doepfner said in an internal letter to employees. Axel Springer did not specify how many of its staff could be cut, but promised that no cuts would be made to the number of, “reporters, authors, or specialist editors”.
USING AI FOR REVENUE OPPORTUNITIES
BuzzFeed’s Peretti said AI-powered content will move “from an R&D stage to part of our core business” in the company’s earnings call on March 13. BuzzFeed president Marcela Martin outlined how BuzzFeed’s AI-powered Infinity Quizzes are the beginning of the company’s adoption of AI “to enhance the audience experience and open up new avenues for monetisation”. A custom quiz matching a reader with a house plant was sponsored by Scotts Miracle-Gro. But on the content creation side, AI tools were recently integrated into BuzzFeed’s content management system, meaning more writers now have access to produce AI-generated content, indicating that the scalability is really just starting now.
Peretti also said AI technology can help automate, improve and streamline the process of making animated content.
AI AND PROMOTING DIVERSITY IN NEWSROOMS
The AIJO project was a collaborative project run in 2020 as part of the JournalismAI Collab of LSE Polis. Representatives from eight major news organisations across the world came together to explore a big challenge: How might we leverage the power of AI to understand, identify and mitigate newsroom biases? AIJO explored the topic by, among other things, doing an AI-driven analysis of the gender balance in our reporting. They focused primarily on images, as it was the language of expression everyone had in common. One basic conclusion is clear: AI won’t visualise a more diverse future for journalism unless we ask for it.
In 2020, the project reported: “The media cannot change who is in charge of countries, institutions and companies and we will continue to cover them regardless of race, gender or age. But we can decide how we report on the impact those leaders have on real people of all backgrounds and the way we represent diverse
communities.”
How then can emerging technologies have implications related to diversity and inclusion? While a lot of the responsibility to manage biases and harms falls on the model creators, users should also keep certain things in mind when exploring generative AI in and for journalism. In a Medium blog post, Agnes Stenbom & Delfina Arambillet propose that generative AI can indeed help promote diversity in journalism, but only if active measures are taken by both creators and users of these models. They offer three suggestions for newsrooms:
- Be mindful about the biases of the models. Avoid falling into stereotypes and take an active approach to prompting the models. For example, if you want to generate a picture for an article about the stock market, consider adding ‘female’ to the prompt (the models will be conservative
- Investigate the models. Leverage your newsroom’s investigation skills and seek further understanding of emerging models. What data were they trained on? How might they impact different communities of people? Report what you learn!
- Contribute to developing the models. If identifying emails. shortcomings, evaluate whether you have the means to contribute to the field’s development by e.g. providing training data or development skills. Can you open source contributions, or in other ways pitch in to promote diversity and inclusion ideals in these developments? Like the AIJO project showed, AI can be used to understand, identify, and mitigate existing biases in our newsrooms. It can better journalism in other ways too: one project aims to use NLP (Natural Language Processing) tools to analyse a publication’s content and spot underreported topics to make their coverage more inclusive (BR and MDR are both German public broadcasters). Another, Modular Journalism, rethinks the core journalistic product, atomising the article to create innovative storytelling formats that directly address the information needs of a variety of users — particularly those who that have been badly served by traditional media. All of this excites Mattia Peretti, who manages JournalismAI at Polis, a research and training project at Polis, the international journalism think tank of the London School of Economics. “These are opportunities to use technology to transform our journalism into something that is more diverse, inclusive, and responsible,” he writes in a piece for the Global Investigative Journalism Network. “Because while it saves journalists some time and makes their work more efficient, it frees up time to use the skills that truly make us humans, like empathy and creativity.”
NEW JOBS AND POSITIONS AROUND AI
Everybody is talking about the artificial intelligence behind ChatGPT. But less widely discussed is a jobs market mushrooming around the technology, with some newly created roles paying upwards of $335,000 a year. For many of these, a computer engineering degree is optional.people who spend their day coaxing the AI to produce better results and help companies train their workforce to harness the tools.
As the technology proliferates, many companies are finding they need someone to add rigour to their results. “It’s like an AI whisperer,” says Albert Phelps, a prompt engineer at Mudano, part of consultancy firm Accenture in the UK. “You’ll often find prompt engineers come from a history, philosophy, or English language background, because it’s wordplay. You’re trying to distill the essence or meaning of something into a limited number of words.”
Companies like Anthropic, a Googlebacked startup, are advertising salaries up to $335,000 for a “Prompt Engineer and Librarian” in San Francisco. Automated document reviewer Klarity, also in California, is offering as much as $230,000 for a machine learning engineer who can “prompt and understand how to produce the best output” from AI tools. In an earlier section in this chapter we also covered how the Financial Times had appointed its first ever AI editor Madhumita Murgia. Though that’s an entirely different type of role, focused on leading coverage of AI as technologies progress and find application in more and more fields, it’s an indicator of the type of new roles that may emerge in other news organisations.
LIMITATIONS AND DISADVANTAGES OF GENERATIVE AI
First and foremost, there is no concept of truth. Truth is not the goal. OpenAI is open enough to say that there is no source of truth in ChatGPT. Their limitations section covers it: “ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers. Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth; (2) training the model to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.”
And ask OpenAI’s playground itself on whether the machine can tell the difference between truth and fiction: “GPT-3 and ChatGPT do not have a ground truth reference, so they cannot tell the difference between truth and fiction. However, GPT-3 and ChatGPT are trained on a large corpus of text, which can help them make informed guesses about the plausibility of a given statement.”
ChatGPT is proof that finding “truth” is a lot trickier than having enough data and the right algorithm. Despite its abilities, ChatGPT is unlikely to ever come close to human capabilities: its technical design, and the design of similar tools, is missing fundamental things like common sense and symbolic reasoning. Scholars who are authorities in this area describe it as being like a parrot; they say its responses to prompts resemble “pastiche” or “glorified cut and paste”. As Jenna Burrell writes in a piece for Poynter, “when you think of ChatGPT, don’t think of Shakespeare, think of autocomplete. Viewed in this light, ChatGPT doesn’t know anything at all.”
Time limit
Equally importantly, there is a datecut- off. If you push ChatGPT hard enough about current developments, it will repeat one line back to you — that its training cut-off date is 2021. Try asking ChatGPT, “Did Kari Lake win in Arizona” and you’ll see what I mean. Since that election happened in 2022, ChatGPT does not “know” about it. This means that, while it could be helpful in synthesising information, making edits and informing reporting, FT’s Murgia, speaking to Reuters Institute, believes generative AI as we see it today is missing some key skills that will prevent it from taking on a more significant role in journalism. “Based on where it is today, it’s not original. It’s not breaking anything new. It’s based on existing information. And it doesn’t have that analytic capability or the voice,” she says.
Sourcing limitations
AI opacity means sourcing is a deadend. ChatGPT does not cite all itssources when it generates text. This is how large language models work. For journalists, sources, and sourcing always matter. OpenAI has not posted a list of all its training sources for ChatGPT. There are some guesses floating around. So why trust a talking tool that is opaque?
When working with tools like ChatGPT, keep in mind that it is smart, but not that smart. It is a machine that has no intentions — it does not want to help or mislead you; it has no concept of what is real and no morals. It is just what it says on the tin – it generates text based on a lot of information it has been trained on. Potentials of misinformation,
disinformation and AI misuse Because of this, you need to fact-check absolutely everything it generates. It goes beyond what you would need to verify if the text was written by a human: ChatGPT will almost always answer your question and if no real information is available, it may make one up. Fact-check maths, names and places and always make sure that everything, well, exists. Data produced is well-written, convincing, and often entirely incorrect. Producing disinformation like this has suddenly become vastly cheaper than it was just a few months ago, and with no marginal cost to produce this content, there is likely to be a massive spike in its production. As Josh Schwartz, CTO of
Chartbeat tells NiemanLab, “Trustworthy journalism that can find a way to drown out the noise has never been more important, or more challenging.” But wildly cheap-to-produce disinformation isn’t the only threat the media faces, Schwartz adds. Wildly cheap correct information has the potential of being just as disruptive, rivalling the content produced by many SEO-focused sites. “Will there be any place at all for human-written SEOfriendly content?” he asks. “It seems likely that the market for it will be massively smaller, as it struggles for position in search results against a broad set of algorithmically generated content.”
Another potential risk of relying on large language models to write news articles is the potential for the AI to insert fake quotes. Since the AI is not bound by the same ethical standards as a human journalist, it may include quotes from sources that do not actually exist, or even attribute fake quotes to real people. This could lead to false or misleading reporting, which could damage the credibility of the news organisation. It will be important for journalists and newsrooms to carefully fact check any articles
written with the help of AI, to ensure the accuracy and integrity of their reporting.
While generative image models like OpenAI’s DALL-E 2 and Stability AI’s Stable Diffusion have allowed users to generate realistic images based solely on text prompts, they
are likely to result in instances of photo manipulation, inaccurate photo illustrations, and misleading stock photos.
Propaganda and disinformation are also possible issues. Cory Bergman is a co-founder of Factal, and he tells NiemanLab: “Imagine using ChatGPT to spam human-like comments and social media posts that support an agenda. Or a war. Thinking of running for office? Time to mobilise the AI content army.”
Ethical concerns around plagiarism
We already know that large language models, such as GPT-3, have the potential to disrupt newsrooms by enabling journalists to use AI to write their stories. However, this raises ethical questions around authorship and plagiarism. If a journalist relies too heavily on AI to write their stories, who can be credited as the author?
Additionally, there is the potential for the AI to inadvertently plagiarise other sources, raising questions about journalistic integrity and accuracy. Since the AI can write in a variety of styles and lengths, its content is virtually indistinguishable from an average human writer. OpenAI is currently working on creating a digital watermark embedded in ChatGPT responses, but that may not solve the issue entirely.
Appropriating the open web
Further, there are substantive ethical questions about trawling the open web and using the creative expression of humanity (even if all the text was uncopyrighted) this way for generative machines. It’s one thing for the collective commons to be open to humanity. It’s another thing for it to be used for massive computational pattern-matching models to get machines to blindly mimic humanity. And it comes as no surprise, for example, when Getty Images sued Stability AI (image generative AI) for copyright infringement in London. In this interview published by the Tech Policy Press on an indigenous perspective on generative AI, Michael Running Wolf (North Cheyenne man, computer science PhD student, and former Amazon engineer) says this: “It’s a lie to say that it only costs electricity to generate the art. That’s a lie. These Stable Diffusion could not do this if they didn’t have the ability to scan the intellectual property of the internet. And that is worth something.”
It all boils down to where AI gets its stuff from. Even the best generative AI tools are only as good as their training, and they are trained with data from today’s messy, inequitable, factually challenged world, so bias and inaccuracy are inevitable. Because their models are black boxes, it is impossible to know how much bad information
finds its way into any of them. But considering that more than 80% of the weighted total of training data for GPT- 3 comes from pages on the open web —
including, for example, crawls of outbound links from Reddit posts, it is highly likely that information generated is problematic at best, and dangerous at worst. Since the tool has been trained on what humans, past and present, have written about groups like women or minorities, information generated could also be biased, even though it has reportedly been trained not to give sexist or racist answers.
So are journalists becoming redundant? AI-based tools will never replace human journalists. As we know, CNET recently made the mistake of overestimating AI’s ability, yielding not only a series of articles rife with factual errors but a broader reckoning for the company and perhaps the industry at large. If AI replaced humans, which it would do ineptly, it would merely flood the internet with even more unreliable (but plausible sounding) junk. The fear that journalists will become redundant is also fuelled by journalism itself, as Burrell points out. Headlines like this one from The New York Times: “Meet GPT-3. It Has Learned to Code (and Blog and Argue) fuel this idea. Even articles meaning
to serve as correctives fall into anthropomorphism, like a piece in Salon that said “AI chatbots can write, but can’t think.” According to Burrell, the claim that ChatGPT can “write”
is an exaggeration, an inflation of the tool’s capabilities that contribute to further misunderstandings and overstatements. Memo’s Kim believes that a company that uses AI to analyse new articles, believes that it’s the evergreen and informational content that will be most at risk. “The power of the editorial publishers will endure. So much of the product that premium publishers offer is their masthead and authority. The same words living elsewhere simply don’t carry the same weight or prominence,” Kim told Euronews.
However, much like previous technological advancements, tools like GPT could be part of a broader shift and redelegation of how journalism is done and change how reporters do their jobs — freeing them up to spend more time interviewing sources and digging up information and less time transcribing interviews and writing daily stories on deadline. AI isn’t taking over a journalist’s role but making their job easier. They should be seen as TOOLS rather than pieces themselves. “It’s probably better to think of these tools as internal newsroom tools, making suggestions to reporters and editors rather than generating text that will be directly published,” says Nicholas Diakopoulos, associate professor of communication studies and computer science at Northwestern University, in a piece for NiemanLab. “Research is blazing ahead to make future versions of the technology better able to output organisations could also invest more in R&D to fine-tune and further adapt the models to be better aligned to journalistic needs.”
The fear of bots replacing journos is perhaps unfounded. However, interfacing with an artificial intelligence will become a specialised skill. Just as “Social Media Editor” was a wholly unique job title, new roles may well be invented and defined by a new class of journalists who will leverage this technology in ways we can’t predict. Publishers are in the driver’s seat ChatGPT is just a tool – albeit a brand new, powerful tool with huge scope, but a tool nonetheless. It does not change the guiding principles of journalism – a fundamentally human activity. Publishers have to work on certain aspects:
1.Transparency
AI written articles could have a byline which makes it unequivocally clear that it was written by a robot, not a reporter. Transparency is critical internally as well as externally, and key for trust. In the case of the CNET story, the Verge reports that there seems to be a lack of transparency around the actual purpose of the content too. According to the Verge, the business model of CNETs relatively new owners Red Ventures, is about creating content designed to get high rankings in search, and then monetise the traffic.
Will AI labels/transparency work? Doesn’t transparency demand that news organisations using machines to write stories disclose that to users? This finding only brings up a tension between the risk of labeling and the assumptions of the reader. It may only further drive publishers to be resistant to label AI-generated news text as such.
Binary disclosure, then, won’t cut it, according to Subramaniam Vincent, Director of Journalism and Media Ethics at Santa Clara University’s Markkula Center for Applied Ethics.
“The real question for news readers and publishers is not plain disclosure in a binary sense: Written by human vs. written by generative AI,” he writes. “Transparency has to be along AIliteracy lines among the public, beyond simplistic labels. We may need a deep transparency of models, training data corpora, and model limitations to the reader, in plain de-complexed language.”
2.Trust
Trust is the currency of journalism. Any deployment of new tech tools must in no way leave room for people to question the integrity of a publication. In mid-2022, AI news headlines were tested with people. Foretelling how the public at large may treat generative AI news writing was some research that came out about six months before OpenAI released ChatGPT. In their work studying how people perceived news generated by AI, a group of researchers (Chiara Longoni, Audrey Fradkin, Luca Cian, and Gordon Pennycook) pointed out that if it were disclosed that a headline was AI-generated (a form of labeling), readers attached an additional credibility deficit to the news. If
journalists start using generative AI for news writing, then, the so-called trust deficit may get another wheel to accelerate on.”
One way to address this trust deficit, according to Jennifer Brandel, CEO of Hearken, is to care. She asks that journalists orient their interactions to ensure the people they engage with, report on and report for, get the signal that the journalists and the newsroom really care. Brandel lays down three questions she believes journalists should ask themselves more in 2023:
- Are you aware of the power you wield in a situation, based on what identities you carry with you or are perceived to have?
- Do you consciously work to ensure the people you’re interviewing feel truly heard and understood? Or are you rushing in to grab a quote and leaving them in the dust because: deadlines?
- Did the people you reported on feel like you got it right in the end? How do you know? “It’s easy to forget that every interaction that journalists and other newsroom
staff have with the public — be that sources, readers, viewers, members, subscribers, commenters, etc. — is an opportunity to influence people to feel something,” she says. That’s where AI can never replace the human touch. In the latest edition of his newsletter, The Rebooting, Brain Morrissey too reckons that AI will displace a lot of work that’s mundane communication and will end up putting a premium on the human connection. “We’re hard wired to trust humans more than machines. There’s a reason we tend
to get far more frustrated trapped in a loop with automated customer service. Publishers that have both human connections and the ability to convene a community will be far more valuable.” Professor Charlie Beckett, of the Polis/ LSE JournalismAI research project, speaking to Reuters Institute, also advises caution and would discourage journalists from using new tools without human supervision: “AI is not about the total automation of content production from start to finish: it is about augmentation to give professionals and creatives the tools to work faster, freeing them up to spend more time on what humans do best,” he says. “Human journalism is also full of flaws and we mitigate the risks
through editing. The same applies to AI. Make sure you understand the tools you are using and the risks. Don’t expect too much of the tech.” So what should media organisations keep in mind about AI? Mattia Peretti writes in LSE blogs…
- The Need for Strategy
We talk about “implementing AI” as if it was one single process. But the reality is that depending on one’s use case and the type of technology (AI is an umbrella term with many subfields), that process may be completely different from the one one has to go through for a different use case. Using a machine learning model to filter the comments you receive from your readers, automatically tagging potentially harmful ones, for example, requires a completely different process than if you want to optimise your paywall to maximise your chances to turn a sporadic reader into a paying subscriber. It is key to come up with a strategy.
2.
Tools Buy or Build?
“Using AI” means using some technological tools that can help one’s work as a journalist. So the question is, where does one get those tools?
The debate in the industry lies mostly around the dichotomy buy vs build: Does one build AI tools in-house to serve your specific purpose or does one instead buy existing tools and adapt them to one’s needs?
Famously, two international news agencies that have been at the forefront of AI innovation have taken different approaches: Reuters builds most of its AI tools in-house, while the AP buys tools by working with startups and vendors in the open market.
Organisations have to consider what they are trying to do, evaluate the costs of both options, consider carefully what skills they already have in the team or what they can easily acquire, and chart a path that works for them. If they do decide to build, it’s likely that there are others in the industry who went down that route already and that they can learn from. And if they decide to buy existing tools, there are many available out there. The buy vs build dichotomy is a false one.
There’s a third route, which is to partner with startups or academic labs that may have the skills one needs, have done a lot of useful research already, and could be eager to partner with media to apply their theoretical learnings to a practical use case.
- Collaboration is Essential
When it comes to AI, collaboration is key. Someone in another news organisation may be facing the same challenges and may be trying to build the exact same thing. JournalismAI, for instance, has helped news organisations from across the world collaborate on AI projects. One team, with members from Bloomberg News, CLIP, DataCritica, and La Nación, went about using artificial intelligence to identify visual indicators within satellite imagery to chase a story, hoping their tool could be used to identify illegal airstrips built in a remote area or the expansion of deforestation in a jungle or even being able to prove if a public road was indeed being built. Another, with Abraji (Brazil) and Data Crítica (Mexico) as participating organisations, sought to train a language model to detect hate speech – in Spanish and Portuguese – directed primarily at journalists and environmental activists. A third, with participating oganisations Sky News (UK), Infobae (Argentina), Il Sole 24 Ore (Italy), The Guardian (UK), focused on tracking influencers, to help journalists to investigate influencers on a greater scale using AI techniques and developing a replicable methodology. This project focused on creating a system to flag Instagram users who were promoting brands or services without complying with the rules of disclosing they have — potentially — a commercial relationship.
INITIATIVES THAT HELP JOURNALISTS BUILD SKILLS ON AI
Journalists can report on AI much better if they know how to use it — and they will be much more responsible in using it if they understand the risks and potential
consequences. There are more and more journalists doing fantastic work on algorithmic accountability, and initiatives like the AI Accountability Network of the Pulitzer
Center are allowing more reporters to build their skills for this critical purpose.
This Network seeks to address the knowledge imbalance on artificial intelligence that exists in the journalism industry, especially at the local level, and to build the capacity of jour- and underreported topic with skill, nuance, and impact. JournalismAI — a research and training project at Polis, the international journalism think tank of the London School of Economics — also helps news organisations use artificial intelligence (AI) more responsibly. This can be done best once journalists and media organisations understand how AI works via direct experience.
Teams like the AI and Automation Lab of Bavarian Radio in Germany are positioning themselves as leaders in this field because they have been smart enough to recognise this,
creating a team, a dedicated lab, that is responsible for both using AI for product development and for algorithmic accountability reporting.
HOW AI IN NEWSROOMS CAN HELP SUPPORT JOURNALISM IN THE GLOBAL SOUTH
News outlet La Silla Vacia, an independent Colombian news outlet that covers politics and power in the country, has experimented with how to make fact checking more efficient. They have taken their first steps in bringing artificial intelligence into fact checking efforts thanks to a small Artificial Intelligence (AI) Impact Fund grant by IMS. “These tools have already helped us to streamline a capital part of the fact checking process: we identify much more quickly the statements from political leaders that can be reviewed and eventually fact checked,” says Santiago Chavarriaga from La Silla Vacia, in a report by International Media Support (IMS). The grant allowed La Silla Vacia to access and incorporate the bespoke solution Chequeabot, an AI bot developed by Chequeado, an Argentinian fact checking outlet. They have been using Chequeabot to be
more efficient and accurate in their daily production for the “Detector de Mentiras” (Factchecking area). Chequeabot has three uses. First, it helps to detect false claims in
politicians’ speeches or tweets. It also records the speeches in order to facilitate the detection of false claims. Lastly, it detects false claims in preselected
Twitter accounts.
The bot allows La Silla Vacia to have a daily customised queue of factcheckable tweets by Colombian politicians. It also includes a tool that automatically selects fact-checkable sentences from a long text or a video (including transcribing). The bot is manually linked to a database of previously factchecked material, allowing the factcheckers to quickly know if a claim has already been checked and reviewed.
“We see the access to the bot as only the first step on the path to be able to factcheck in real time, or almost, an ability that can make the fact checking work much more powerful, both by providing information that can counter mis- and disinformation at the beginning of their lives and by being able to strike the iron when it is hot, to give the public a fact check when a debate is roaring,” says Chavarriaga.
La Silla Vacia found the AI technology especially useful when reporting on the presidential election in Colombia in May 2022. “Among other things, we used the new tools to factcheck the claims of a dozen presidential candidates in three separate TV debates, being able to publish all the factchecking within four hours of the
end of each debate.”
To mention a few examples, La Silla Vacia was able to verify a claim that a new opinion poll had shown that a presidential candidate had taken the lead in Colombia’s Caribbean coast, a statement that was being hotly contested by followers and several candidates. The media factchecked the claim in under four hours from the moment the claim was originally stated. The AI technology also helped La Silla Vacia verify a claim of a presidential candidate who had stated that income from mining royalties was not being put to any use. Although verifying this required more work, they were able to factcheck the claim in only one day. Chavarriaga sees the implementation of the Chequeabot as a sign that AI can help promote good journalism if used in the right way. “Our experience proves that technology can be an important ally of quality journalism even in a middle-income country and for a small, independent media outlet like ours,” he says.
AI Journalism Lessons from a 150-Year-Old Argentinian Newspaper
The 150-year-old Argentinian newspaper La Nacion has produced a diverse range of stories assisted by AI technologies and has created an AI lab.
La Nación’s experiments with AI began with an investigation into private renewable energy in Argentina. In 2016, Mauricio Macri, the Argentinian President at the time, launched a programme to open up the country’s clean energy resources to private and international investment. Florencia Coelho, new media researcher at La Nación, pitched a project to map the government programme’s progress four years after it was launched.
The La Nación data team used machine learning and computer vision and worked with a third-party lab specialising in geospatial analysis and AI. La Nación’s algorithm was trained to identify the shape of solar farms in Argentina. Computer vision is a process that trains computers to analyse and understand visuals. So 10,999 images were
used to train the algorithm before a total of 7 million images were processed and 2,780,400 square kilometres (1,074,000 square miles) of land analysed. The data suggested the government programme had not met its targets.
Analysing Music with AI
The media company’s own AI lab’s first project was an analysis of the lyrics of trap music that took seven months to complete. The team used machine
learning, natural language processing, Spotify’s API, and lyrics from genius.com to process 692 songs and dig intothe topics, trends, and messages of this increasingly popular music genre in Argentina. The AI that journalists used had to deal with some linguistic problems, including invented words used in trap songs. An interactive feature, including an extensive trap dictionary, an “egometer” measuring how many times an artist mentions him or herself, and other analysis of hallmarks of the genre allowed readers to explore the results. Much of what they learned, according to Florencia Coelho, New Media Researcher at La Nación, can be applied to other types of music or even to different
texts. “Today the topic was trap, but tomorrow we may use this for political discourse or for a different topic,” data analyst Gabriela Bouret echoes in a Reuters Institute blog post.
AI and Election Coverage In 2021,
The newsroom again used computer vision to detect errors in the telegrams returned from polling stations during parliamentary elections in Argentina. Working with a third-party company to build and train an algorithm to identify inconsistencies in the telegrams, which record details including the number of votes won by each party and how many election monitors were present, human volunteers were then asked to sift through flagged records. La Nación used its existing VozData platform, where readers have collaborated on data investigations and worked with transparency initiatives and universities. Human helpers refined the algorithm: it had to be adjusted to deal with telegrams that were wonky or that had been uploaded upsidedown.The results suggested that 95% of telegrams returned were filled out correctly, but 5% had some information missing.
Gender in the Newsroom
The team is now working on a Spanish- language version of the gender gap tracker, a tool originally devised to measure the ratio of female-to-male sources quoted in online news articles in Canadian media. Coelho and her colleague Delfina Arambillet began working on the project through the JournalismAI Collab project and they have brought the work into La Nación’s newsroom to better understand gender biases in reporting, including whether a source’s gender affects the topics on which they are likely to be quoted. The resulting tool will be useful for the newspaper’s business insights team to assess how article performance is affected by the gender or topics featured. In an extension of the gender tracker project, La Nación was also involved in an open source AI model developed to detect gender in faces to help analyse the ratio of male and female images
used by news outlets. By sharing around 50 Argentinian and Latinx portraits with the team training the AI model, which was originally trained on Asian faces, the AI’s ability to detect a more diverse range of faces in terms of skin tone and ethnicity will improve, making it more useful to a wider range of newsrooms.
PROTECTIONS FOR PUBLISHERS
In a detailed piece covering publisher’s concerns around generative AI published earlier this year, The New York Times’ media reporter Katie Robertson writes about the potential implications for search traffic. “Many sites get at least half their traffic from search engines. Fuller results generated by new chatbots could mean far fewer visitors,” the subheading to her article reads.
Robertson writes that new AI tools from Google and Microsoft give answers to search queries in full paragraphs rather than a list of links. As a result, many publishers worry that
far fewer people will click through to news sites as a result, shrinking traffic — and, by extension, revenue. Many publishers, she writes, “are pulling together task forces to weigh
options, making the topic a priority at industry conferences and, through a trade organisation, planning a push to be paid for the use of their content by
chatbots”.
News Corp’s chief executive, Robert Thomson, who for years has led a push to get tech companies to pay for news content, said in an interview that tech companies should pay to use publishers’ content to produce results from AI chatbots. The chatbots generate their results by synthesizing information from the internet. He added that News Corp, which owns The Wall Street Journal and The New York Post among other outlets, was in talks with “a couple of companies” about the use of its content, though he declined to specify which ones. “There is a recognition at their end that discussions are necessary,” he said.
Roger Lynch, the chief executive of Condé Nast, which owns titles like Vogue, Vanity Fair and Glamour, agreed that content creators should be compensated. He said one upside for publishers was that audiences might soon find it harder to know what information to trust on the web, so “they’ll have to go to trusted sources”.
The News Media Alliance, which represents 2,000 outlets around the world, including The New York Times, is working on principles that it says should guide the use and
development of A.I. systems, and regulation around them, to protect publishers. According to a draft, the principles say the use of publisher content for the development of A.I. should require “a negotiated agreement and explicit permission”.
The guidelines also call on tech companies to “provide sufficient value” for high-quality, trustworthy journalism content and brands, and state that any new laws or regulations that make exceptions to copyright law for A.I. must not weaken protections for publishers. “Without these protections, publishers — far too many of whom already struggle
to survive in the online ecosystem due to marketplace imbalances — face an existential crisis that threatens our communities’ access to reliable and trustworthy journalism,” the document states.
The WSJ has also reported on the issue. “We have valuable content that’s being used constantly to generate revenue for others off the backs of investments that we make, that requires real human work, and that has to be compensated,” said Danielle Coffey, executive vice president and general counsel of the News Media Alliance. Reddit has had talks with Microsoft about the use of its content in AI training, people familiar with the discussions said. Thomson, chief executive of The Wall Street Journal parent News Corp, said at a recent investor conference that he has “started discussions with a certain party who shall remain nameless”. “Clearly, they are using proprietary content — there should
be, obviously, some compensation for that,” Mr Thomson said.
THE FUTURE IS NOT ONE AI BUT MANY
You’ve all heard about ChatGPT, but several other competing technologies are soon to become ubiquitous. Here’s a ready guide:
Bing. Two months after ChatGPT’s debut, Microsoft, OpenAI’s primary investor and partner, added a similar chatbot, capable of having open-ended text conversations on virtually any topic, to its Bing internet search engine. There have already been a series of stories about the bot’s occasionally inaccurate and sometimes outright weird responses, that drew much media attention
away from the actual fact of its release and that it was quite a big deal for Microsoft.
Bard. Google’s chatbot, called Bard, was released in March to a limited number of users in the United States and Britain. Originally conceived as a creative tool designed to draft emails and poems, it can generate ideas, write blog posts and answer questions with facts or opinions.
Ernie. The search giant Baidu unveiled China’s first major rival to ChatGPT in March. However, according to The New York Times, the debut of Ernie, short for Enhanced Representation through Knowledge Integration, turned out to be a flop after a promised “live” demonstration of the bot was revealed to have been recorded.
Poe. Short for “Platform for Open Explorations this is a new AI chat app from the people who made Quora. The central metaphor of Poe is a chat app, like
WhatsApp or Messages. So rather than a place to chat with one bot, it’s a place to chat with many.
At the moment, Poe ships with three bots, each anthropomorphised into a personality. One is Sage — powered by OpenAI and either a version or a clone
of ChatGPT.
The second is named Claude, and it might be the most interesting. It’s powered by a company named Anthropic, which was founded by ex-OpenAI employees and is now a rival of their old bosses. (Google bought 10% of it for $300 million earlier this year.) The third bot in Poe is named Dragonfly. Like Sage, it’s made by OpenAI, the makers of ChatGPT. But it’s tuned differently; it responds to answers more quickly and in shorter forms.
FINAL THOUGHTS
We’ll level with you — it’s not been easy putting together this story on generative AI because every week brings about five new developments and interesting articles about publisher experiments and other assorted uses for the new tech. And that’s not even factoring in all the material now coming in, at the time of writing, about new higher iterations of the technology such as GPT-4 which The New York Times has called “exciting and scary”. As we mentioned earlier, our chapter on AI for this book last year was a very different read indeed! And who knows what may come around by next year. As we all contemplate the waves of change ahead, we leave you with this quote from Mattia Peretti: “I invite you to be balanced in approaching AI. Be skeptical, understand the limitations of the technology and its risks, and be careful and responsible once you decide to use it. But while you do all of that, allow yourself to get excited for the opportunities that it opens up.”
The Innovation in News Media World Report is published every year by INNOVATION Media Consulting, in association with FIPP. The report is co-edited by INNOVATION President, Juan Señor, and senior consultant, Jayant Sriram