The business of communicating the happenings of society might seem to be something entirely human-centric. After all, what do machines care about what happens in the news? However, the journalism industry as a whole is highly dependent on technology. From ways of communicating instantly with news sources and content generated first-hand in image, video and text form, to the publication of content across an ever-increasing array of channels, technology is core to making the news and journalism industry buzz at the pace that news is generated. However, it's not just that news and journalism is a consumer of technology, but increasingly technology itself is changing the way that news and journalism are done. In particular, news organizations are increasingly leveraging AI to change the way news is generated, produced, published and shared. In the not-too-distant future, intelligent machines will generate news articles; perhaps exactly like the kind you are reading now.
Content Writing and Information Gathering with AI
Content and news organizations are making increasing use of AI systems to uncover data from multiple sources and automatically summarize them into articles or supporting research for those articles. Machine learning algorithms have been proven to be adept at finding patterns in textual data and uncovering the useful information that accurately summarizes the data inside. By using these advanced algorithms against enormous quantities of data from press releases, blog posts, comments, social media posts, images, video and all sorts of unstructured content, journalistic organizations can get quickly up to speed on fast-breaking news developments and generate content that accurately summarizes changing situations.
AI systems are also used to gather information for marketing and advertising operations. Machine learning systems can find patterns gleaned across various channels that indicate engagement rates with content and find hidden patterns that can suggest better ways to connect with readers and provide better results for advertisers and content monetization. Already readers are benefiting from this intelligent news delivery system. AI-enabled content personalization is guiding the reader with relevant content regarding their interests and suggests other articles to read. This keeps readers on news sites longer and gets them more engaged with the writing and content. As a result, it drives more eyeballs to advertisers and potential opportunities for conversion.
Besides simply aggregating information, some content organizations are putting into place AI systems that generate entire articles from scratch. Forbes has rolled out an AI-powered Content Management System called Bertie that suggests content and titles, The Washington Post released Heliograf that can generate entire articles from quantitative data, Bloomberg is using Cyborg for content creation and management, and other AI systems are being used or tested by The Guardian, Associated Press and Reuters. Many of these organizations are using artificial intelligence to generate shareholder reports, legal documents, press releases, general reports and articles. Artificial intelligence is a great resource to help cover things where reporters can't always get to for example local sports and local political elections.
Keeping an Eye on Fake News and Moderating User Generated Content
A big challenge in today's fast-paced democratized access to technology is separating real news with verifiable facts from fake news that intends to misdirect, misinform, confuse, or otherwise prevent the uninformed user from discerning reality from fiction. Fortunately, AI is providing tools to help content producers and publishers identify fake news and reduce their impact on their readership. (Disclosure: I am a principal analyst at Cognilytica and co-host of the AI Today podcast)
AI systems are capable of identifying patterns of real data sources and real news content from those that have been artificially generated. These machine-learning systems can serve as a first-pass editorial control that can verify news items against additional sources, automatically provide verification from third-party sources, and further help reinforce real news stories or debunk falsities. News aggregators can automatically put in truth-checking links and sources and score inbound news stories with their likelihood of being true. Many sources of fake news fit into patterns of misinformation campaigns that aim to skew public opinion or otherwise communicate a story that is not borne out by truth and facts.
Not only are the original stories potentially fake, but also the comments and user-generated content might be rife with falsities. Automated software bots are creating fake comments, helping to magnify fake stories through shares and social media endorsement and otherwise augment fake stories with a veneer of reality. The combination AI with Machine Learning are helping to weed out this fake grassroots sharing (frequently called "astroturfing") by muting or deleting comments, flagging user-generated content for moderation, and otherwise preventing the spread of artificial social proof that can make fake news run around the world without control.
Besides helping filter fake content, AI systems help moderate user-generated content and comments. Users are notorious for submitting content that pushes the boundaries of acceptability. It is usually a full-time job for sites to moderate this content to make sure inappropriate materials aren't shared with their audiences. Machine learning systems are proving to be valuable, ever-vigilant assistants that can check the text of posted content, the content of images to make sure that only appropriate and acceptable images are posted, and other user posts meet acceptable guidelines. While humans might still be in the loop to make sure that they are watching the watchers, these humans are empowered with AI capabilities to allow them to moderate much greater quantities of content at scale.
Improving Journalistic Processes
AI systems are also being used to enhance the still human-powered journalistic news processes and organizational workflows. News operations are complex organizations just like most enterprises. These organizations are using AI to help streamline their distributed processes for gathering information, contacting sources and backend operations like dealing with the advertisers and classifieds. Intelligent automation tools are being used to bridge gaps between various systems and help stitch together the various systems and processes needed to form stories, put reporters on-site and organize content in a way usable for readers, advertisers and others.
These systems are also using machine learning-powered natural language processing to help analyze paperwork such as expenses and receipts for news operations, as well as assist with the planning of news operations. In addition, these AI-based systems are being used to help keep an eye on thin journalism business margins. They can spot unusual expenses, operations that are running outside of acceptable business requirements and otherwise provide visibility to help make the journalism business more profitable and efficient.
Journalism continues to change with each wave of technology. Handwriting made way for mass printing. The telegraph sped-up news collection across large distances. The telephone and radio accelerated journalism even further. And in just the past 100 years, journalism moved from radio to television to cable to the internet, and in each iteration, the news business has changed. There is no doubt that in this latest iteration, AI will change journalism even further, and will continue to change the way that news is created, generated, managed, published and shared.
Ronald Schmelzer, columnist, is senior analyst and founder of the Artificial Intelligence-focused analyst and advisory firm Cognilytica, and is also the host of the AI Today podcast, SXSW Innovation Awards Judge, founder and operator of TechBreakfast demo format events, and an expert in AI, Machine Learning, Enterprise Architecture, venture capital, startup and entrepreneurial ecosystems, and more. Prior to founding Cognilytica, Ron founded and ran ZapThink, an industry analyst firm focused on Service-Oriented Architecture (SOA), Cloud Computing, Web Services, XML, & Enterprise Architecture, which was acquired by Dovel Technologies in August 2011.
Ron is a Parallel Entrepreneur, having started and sold a number of successful companies. The companies Ron has started and run have collectively employed hundreds of people, raised over $60M in Venture funding and exits in the millions. Ron was founder and chief organizer of TechBreakfast – the largest monthly morning tech meetup in the nation with over 50,000 members and 3000+ attendees at the monthly events across the US including Baltimore, DC, NY, Boston, Austin, Silicon Valley, Philadelphia, Raleigh and more.
He was also founder and CEO at Bizelo, a SaaS company focused on small business apps, and was Founder and CTO of ChannelWave, an enterprise software company which raised $60M+ in VC funding and subsequently acquired by Click Commerce, a publicly traded company. Ron founded and was CEO of VirtuMall and VirtuFlex from 1994-1998, and hired the CEO before it merged with ChannelWave.
Ron is a well-known expert in IT, Software-as-a-Service (SaaS), XML, Web Services, and Service-Oriented Architecture (SOA). He is well regarded as a startup marketing & sales adviser, and is currently mentor & investor in the TechStars seed stage investment program, where he has been involved since 2009. In addition, he is a judge of SXSW Interactive Awards and served on standards bodies such as RosettaNet, UDDI, and ebXML.
Ron is the lead author of XML And Web Services Unleashed (SAMS 2002) and co-author of Service-Orient or Be Doomed (Wiley 2006) with Jason Bloomberg. Ron received a B.S. degree in Computer Science and Engineering from Massachusetts Institute of Technology (MIT) and MBA from Johns Hopkins University.