Excerpts from this post appeared on this Huffington Post piece. 

Software was eating the world. Then came AI.

Can you remember the last time you sat in on a panel discussion about technology without hearing the words “artificial intelligence” or “machine learning”? Reminiscent of big data in 2007, the ubiquity of AI in 2017 is profoundly impacting the world. Unlike big data, which predominantly focused on the enterprise sector, rapid advances in AI and machine learning are directly affecting end consumers in a much more tangible fashion. In only a few years, we went from smart phones and smart TVs to AI-powered shoes, strollers, luggage and bags, doors, trucks, burger flipping robots, and even more recently, Microsoft’s audacious attempt to push AI into single board computing devices the size of a red-pepper flake.

While the impact of machine learning is fairly clear for certain industries in terms of their strategic long term outlook, others will require more out-of-the-box thinking to reap the benefits. The focus of this post is to highlight the far-reaching implications of AI on one such domain: storytelling. Although historically storytelling has been an exclusively human process, it is no longer the case. Incorporating the power of artificial intelligence into the editorial workflow can massively enhance how stories are discovered, created, conveyed, and consumed. The creativity of AI is also evolving, establishing the technology as one of the most disruptive forces to impact the end-to-end cycle of storytelling. From creation to consumption, understanding machine intelligence’s impact on storytelling and embracing the change are critical to succeeding in the age of human-machine collaboration. Already, several news and media organizations are heavily experimenting with AI in various aspects of their business, leveraging the technology to gain a competitive edge. To better understand how these organizations are capitalizing on the immense power of machine learning, we will take a closer look at the impact of artificial intelligence in various aspects of storytelling.

Detection: Storytelling, news generation, and other forms of content creation are triggered by observations. These observations can emerge from immediate triggers or long-term trends. Utilizing massive computing power, software solutions are now capable of rapidly identifying short term anomalies, as well as long term patterns in large amounts of heterogeneous and multi-modal data that are invisible to the human eye. Just as Twitter is being used to predict riots up to an hour faster than the police, or how we at Tilofy analyze data to forecast future trends, machine intelligence presents myriad opportunities for storytelling. Using machine learning to identify patterns is not just faster, cheaper, and more scalable. It also removes subjectivity. The human brain is programmed to fear the unknown, implanting an inherent bias in accepting things that “seem” right to the observer. Machine intelligence, on the other hand, models out such human subjectivity in identifying what stories need to be told. Moreover, the sheer volume of data and information available around any topic creates a cognitive overhead for the human brain to identify, understand, and contextualize. Machines, however, are extremely efficient in dealing with such analytical complexities that surpass the capacity of the human mind. Case in point: The International Consortium of Investigative Journalists (ICIJ) sifted through 2.6TB of unstructured data embedded in 11.5 million documents to expose a network of tax havens being exploited by rich and powerful civic employees and politicians in what is known as Panama Papers. It would take several decades for humans to process, organize and analyze this data without the use of technology.

This example illustrates how creative use of artificial intelligence and machine learning can help save journalists massive amounts of time spent on mundane and repetitive tasks. By having machines share the workload, a storytellers’ time is freed up to do what they do best, produce high quality and comprehensive editorial content. Another interesting example of leveraging AI to simultaneously improve quality and quantity of news production was the Associated Press’ use of automation in their reporting process to increase the number of publications covering corporate earnings. Not only was the AP able to raise the number of publications by a factor of 12 and reduce human prone errors such as typos, they freed up journalists’ time by as much as 20%, leaving them more time to focus on their investigative efforts.

Creation: Pattern identification and analysis of large amounts of data are not just useful for detecting interesting topics. In fact, the role of machine automation becomes more evident when the idea for a story is already recognized. Machines are very adept at helping journalists augment their stories with more context. Several news organizations have already begun using automation for this purpose. The AP has utilized machine learning to predict race outcomes for several years, while startups such as Graphiq, are using billions of data points to auto-generate interactive visualizations. Content creation is one of the most exciting segments where technology can work hand in hand with human creativity to apply more data-driven, factual and interactive context to a story. For example, at Tilofy, we automatically generate insights and context behind all our machine generated trend forecasts.

The use of artificial intelligence in the creation of a story is not limited to machine learning approaches. Natural Language Processing (NLP) as well as Natural Language Generation (NLG) are being widely applied by editorial teams for a variety of use cases. From speech to text conversion and rapid summarization using NLP, to auto-generating a story based on the application of a predefined template to a large body of structured data (reports of earthquake, corporate earnings, sporting events, etc.) using NLG, AI has expanded its breadth of use cases and applications among a wide range of editorial teams. Tilofy’s trend forecasting platform uses similar techniques to auto-generate a trend report for each forecasted trend on the platform, alleviating the need for a large editorial team, as well as completely removing any bias in our trend reports. Other areas of artificial intelligence such as machine vision and image processing can also play an integral role in storytelling. Advances in machine vision and object recognition allow journalists to visually search massive image and video archives, even churning out auto-generated video compilations from textual data.

Distribution: With the emergence of social media, and its modern ubiquity, social platforms have become the primary source of content consumption among users. Media organizations are losing their grip on controlling how, when, where and who discovers their content. People are no longer obtaining their information exclusively from news organizations, while increasingly turning to technology backed social media platforms instead. Tech giants, such as Facebook, Instagram, and Twitter have their own internal ranking algorithms that determine what content populates a user’s feed. Similarly, search engines determine how content is discovered on the user’s search results page based on who is searching for what, when and where. Although the specific mechanics of these ranking algorithms are well kept secrets within these organizations, becoming aware of how machine learning is applied to content discovery, as well as actively partnering with technology companies can become an effective method to benefiting from artificial intelligence in content discovery as opposed to falling victim to it.

Moreover, automation can help storytellers identify a potential target audience for a story, or inversely figure out what stories a target audience would be potentially interested in learning about based on the analysis of their psychographic traits. The transparent nature and large volume of social media interactions, has allowed organization to go beyond traditional demographic characteristics in identifying like minded clusters of readers for each story. Several success stories have emerged showcasing how some internet companies, such as Buzzfeed have utilized data driven approaches to increase virality and audience engagement for their content. Another increasing use of automation among news organizations revolves around the idea of A/B testing article headlines to eventually converge on the optimal choice based on certain performance metrics (such as clickthrough rates). The Washington Post is currently using one such tool which allows editors to create multiple versions of a story (with different headlines, cover photos, snippets, etc.) to show the prevailing version more frequently to readers. Such A/B testing tools provide extremely effective training data for machine learning platforms to suggest edits/revisions to editors on the fly based on past performance of already published articles and historical data. In a similar sense, machine learning can also auto-suggest a proper length of an article or its title, image size/placement and various other factors based on the target device (tablet, phone, laptop, VR headsets) where the content is going to be consumed.

Lastly, despite advances discussed above, when it comes to accessing knowledge and information, issues of digital divide, low literacy, low internet penetration rate and poor connectivity still affect hundreds of millions of people living in rural and underdeveloped communities all around the world. This presents another great opportunity for technology to bridge the gap and bring the world closer. Microsoft use of AI in Skype’s real-time translator service has allowed people from the furthest corners of the world to connect, even without understanding each other’s native language using a cellphone or a landline. Similarly, Google’s widely popular translate service has opened a wealth of content originally created in one language to others. Due to its constant improvements in quality and number of languages covered, Google Translate might soon enhance or replace human-centric efforts like project Lingua by auto translating trending news at scale.

Whether we like it or not, artificial intelligence is here to stay. It is no longer possible to imagine a world without machine learning, as industries who refuse to adapt fall further behind. Like electricity or internet connectivity, the ubiquity of AI is becoming so widespread that it will soon be embedded in all aspects of daily life. Such a profound paradigm shift only emerges once in a generation, creating many opportunities for industries to evolve. Like all other professions, storytellers can leverage AI, by carefully and gracefully embracing the technology to enhance content creation and distribution. For storytellers to truly thrive in the modern world, they must continue to partner with engineers to better understand the mechanics of machine learning in an effort to form man-machine collaborations backed by powerful data driven intelligence and guided by human insight. While artificial intelligence is toying with writing science fiction screenplays, the ability for machines to add artful human nuance and the subtleties of culture to storytelling is still far away, making it imperative for storytellers to utilize the power of AI to augment their stories.