From simple chatbots to generative content creation tools, artificial intelligence has become a force to be reckoned with for digital media companies. But while interest has heated up lately, artificial intelligence (AI) has already been employed by media companies for about a decade. Among other things, AI has long enabled journalists to analyze large amounts of data, extract insights, and identify patterns and trends. It also offers ways to engage audiences through personalized experiences and recommendations.
However, while it is not a new phenomenon, AI has come a long way since 2014 when some of the first media companies started using it for journalistic purposes. In this two-part series, we explore the evolution of AI use in the media, what’s different now, the opportunities that it offers publishers, as well as challenges including the misuse and abuse of AI, errors and accuracy, and concerns about job losses for human journalists.
AI + journalism
The news industry has witnessed three phases of AI innovation over the past decade: automation, augmentation, and generation, according to Francesco Marconi, computational journalist and the co-founder of real-time information company AppliedXL. “During the first wave, the focus was on automating data-driven news stories, such as financial reports, sports results, and economic indicators, using natural language generation techniques,” Marconi said.
In that first wave, AI offered a way to free journalists from menial or repetitive tasks so they could engage in more complex, high-impact, and investigative journalism. It was touted as a way to save organizations time and money by automating the production of certain types of news stories based on data.
For example, The Washington Post used its in-house automated technology Heliograf to cover weekly high school football games in Washington, D.C. and The Associated Press used AI to automate quarterly earnings reports – generating more than 3,000 stories per quarter compared to just 300 previously.
During the second wave, the emphasis shifted to augmenting reporting through machine learning and natural language processing to analyze large datasets and uncover trends, Marconi explained. AI was a tool for journalists to use to analyze large amounts of data, text, video and images, to help them identify trends, patterns or outliers.
During this period, digital media companies also began experimenting with chatbots to help journalists tell stories differently. Chatbots simulated human conversations by responding to specific questions with pre-programmed answers. Early examples included Harvard Business Review’s Slackbot, and AccuWeather’s chatbot for Facebook Messenger that took questions from users and provided weather-related answers.
The third and current wave is driven by the AI generative movement, powered by large language models, which are capable of generating narrative text at scale.
OpenAI’s ChatGPT-3 was trained on massive amounts of text (large language models), which it scraped from the internet. This allowed it to learn the patterns and structures of language to “make educated guesses based on the words you’ve typed previously.” ChatGPT reached 100 million users in January, two months after its launch. Not wanting to be left behind, Google released its own AI chatbot called Bard Facebook announced its AI large language model called LlaMA, and search engine DuckDuckGo has announced DuckAssist.
In recent days, digital media companies have been experimenting with and examining how they can use ChatGPT and generative AI. Last month, Jim Mullen, chief executive officer at Reach PLC, told the Financial Times his company had set up a working group to examine how the tool might be used to assist journalists to write short news stories or compile coverage of local weather and traffic.
For media companies that have used AI for years, this new crop of AI has been interesting to watch. While ethical and quality uses are well-established, some of the current applications are still a work in progress.
One early adopter, Bloomberg Media, has used AI for more than a decade. Their deployment uses natural language processing (NLP) and machine learning to extract information from documents, including detecting people, companies and organizations found in the text of their stories. In addition, they also extract insights to help reporters find news about companies or sectors they cover.
“Like most consumers, I’ve been enchanted by the latest crop of AI tools. Tools like ChatGPT and Midjourney are showing us how much creativity these tools can unlock,” said Julia Beizer, Chief Digital Officer at Bloomberg Media.
“But alongside the enchantment, I’ve felt a fair bit of unease. These are early, very public experiments and we are seeing in real time how easy it is for unproven tech to get the facts wrong. This should be a reminder to all of us – particularly those of us in the journalism business – how important the work we do is,” she said.
The difference in today’s AI
For digital media companies, AI is different now for a number of reasons. Newer generative AI models have broader capabilities because of increased computational power, which means models can process larger amounts of data faster. There have been improvements in NLP, which has allowed AI to generate more nuanced language.
And, in recent years, AI tools have become more accessible for digital content companies. The current crop requires far less tech savvy and investment because they’re open-source.
“Initially, AI in the newsroom was limited to big newsrooms with significant resources,” explained Marconi. “However, with the widespread availability of AI infrastructure, such as open-source models and APIs, the technology is now accessible to every newsroom. This broad accessibility will also bring major challenges as the news industry rushes to adopt new standards and workflows.”
Bloomberg’s Beizer believes that the big difference right now is creativity. “The best technology feels like magic. It fades to the background so all you’re left with is the experience. ChatGPT is a simple interface over one of the most powerful pieces of technology we’ve seen,” she said.
So what’s new?
Given the leap forward in ease of use, AI may create new opportunities for those willing to adapt and innovate in this rapidly changing landscape.
“The economics of the media business are changing dramatically. AI has substantially reduced the cost of production” such as including editing, story writing, and creating multiple versions. “But newsgathering and fact-checking still necessitate specialized knowledge and infrastructure that technology companies lack,” Marconi said.
He added that media companies have an opportunity to become a major player in the AI space. That’s because they possess valuable assets for AI development: the text and image data for training models and ethical principles for creating trustworthy systems.
Advances in technology have disrupted the media industry for many years. According to Beizer, generative AI is only the latest chapter. “What I believe has changed this time around is that we are now mature digital companies with digital businesses to protect and grow. This time around, I have no doubt that our industry will rise to the occasion of thoughtfully exploring this disruption from all its angles – and setting the right strategies for our businesses and quality content to flourish,” she said.
There are valid concerns about whether generative AI will be used to flood the market with content in the future. However, reputable media outlets will find that there are responsible applications of AI that will actually allow them to double down on the quality of their journalism and provide better content recommendations and experiences.
For Beizer, the biggest opportunity at the moment is “focusing our companies on what matters most: our customers… In this moment, we should lean even harder into building direct relationships with those users, getting to know what they want and delivering that value. We have the biggest opportunities in the kind of differentiated value that a chatbot cannot provide – analysis, novel points of view, human connection, nuanced reporting and writing. We should deprioritize anything that isn’t that. The future of our business depends on it,” she said.
However, as AI becomes more sophisticated, there are other promising applications for journalists. Imagine an AI-based tool that could help with large projects that newsrooms could never have enough staff to do, or one that checked facts, style, and grammar.
Marconi sees the evolution of AI in recent years as the foundation for the next phase of AI in journalism, which he believes will come to fruition over the next decade. He calls it artificial journalistic intelligence, where machines are able to automate news gathering and production, resulting in improved accuracy, efficiency, and impartiality, he said.
“Nonetheless, humans will continue to play a significant role. Creating editorial algorithms requires human supervision. As a result, in this new era, transparency and auditing of algorithms will become critical functions of newsrooms in the future,” he said.
In part 2, we look at challenges and concerns around generative AI, including the misuse and abuse of AI, errors and accuracy, legal and ethical issues, and concerns about job losses for human journalists.