With drone photography, "we can track all of the trash in a creek, river, or stream, examine how it's distributed, and then apply machine–learning …

This module provides a brief introduction to the course and to the outline of topics we'll cover. This module provides a broad overview of how algorithmic approaches are being used throughout journalism including in areas like content production and computational story discovery. We'll talk about which tasks algorithms are good at, which tasks people are still better at, and about how to blend algorithms and people together in productive workflows. We'll also talk about what it means to develop computational thinking skills and how to apply them in news production. In Module two we'll get into a lot more detail on automated content production, including how it works and is used by news organizations, as well as covering its benefits and limitations so you know when it might be appropriate to deploy.

The increasing presence of artificial intelligence and automated technology is changing journalism. While the term artificial intelligence dates back to the 1950s, and has since acquired several meanings, there is a general consensus around the nature of AI as the theory and development of computer systems able to perform tasks normally requiring human intelligence. Since many of the AI tools journalists are now using come from other disciplines--computer science, statistics, and engineering, for example--they tend to be general purpose. Now that journalists are using AI in the newsroom, what must they know about these technologies, and what must technologists know about journalistic standards when building them? On June 13, 2017, the Tow Center for Digital Journalism and the Brown Institute for Media Innovation convened a policy exchange forum of technologists and journalists to consider how artificial intelligence is impacting newsrooms and how it can be better adapted to the field of journalism.

This report focuses on a specific part of news automation: the automated generation of news texts based on structured data. This is not about crystal ball gazing. News automation is already making itself felt in the daily life of newsrooms, and the examples presented in this report show how automation can aid journalism as well as the implications, and the ethics involved. Media outlets face ever-growing commercial pressure to extract higher margins from dwindling resources and that is a key driver for news automation. Right now, one of the main goals of automated content is to save journalistic effort, especially on repetitive tasks, while increasing output volume.

Will the use of artificial intelligence (AI), algorithms, and smart machines be the end of journalism as we know it--or its savior? In Newsmakers, Francesco Marconi, who has led the development of the Associated Press and Wall Street Journal's use of AI in journalism, offers a new perspective on the potential of these technologies. He explains how reporters, editors, and newsrooms of all sizes can take advantage of the possibilities they provide to develop new ways of telling stories and connecting with readers. Marconi analyzes the challenges and opportunities of AI through case studies ranging from financial publications using algorithms to write earnings reports to investigative reporters analyzing large data sets to outlets determining the distribution of news on social media. Newsmakers contends that AI can augment--not automate--the industry, allowing journalists to break more news more quickly while simultaneously freeing up their time for deeper analysis.

From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated. An expert in computer science and media explains the present and future of a world in which news is created by algorithm. Amid the push for self-driving cars and the roboticization of industrial economies, automation has proven one of the biggest news stories of our time. Yet the wide-scale automation of the news itself has largely escaped attention. In this lively exposé of that rapidly shifting terrain, Nicholas Diakopoulos focuses on the people who tell the stories--increasingly with the help of computer algorithms that are fundamentally changing the creation, dissemination, and reception of the news. Diakopoulos reveals how machine learning and data mining have transformed investigative journalism.

Businesses are expected to spend as much as $97.9 billion annually on AI projects by 2023, up from just $37.5 billion this year, IDC forecasts. But there's still some debate about what qualifies as true AI. When China's state-run Xinhua News Agency last year announced that it had created the "world's first" AI news anchor, some in the industry balked. The digital anchor was built to move and speak as a normal anchor would, but its underlying technology leaned closer to machine learning than true artificial intelligence. Perhaps the simplest definition of what constitutes AI came from one of the founders of the discipline, Stanford University Professor John McCarthy, who died in 2011.

My grandmother was an outstanding cook. So when I recently came across her old cook book I tried to read through some of the recipes, hoping I could recreate some of the dishes I enjoyed as a kid. However this turned out harder than expected since the book was printed around 1911 in a typeface called fraktur. For example the letter "A" looks like a "U" in fraktur and every time I see a "Z" in fraktur I read a "3" (see Figure 2). So the idea emerged to develop a pipeline that will create a live translation of the fraktur letters into a modern typeface.

Artificial Intelligence is here to stay. AI systems are set to provide a $14 trillion boost to the global economy by 2035.

Analytics is a game-changer in detecting procurement fraud. It is by far the most efficient way of doing so.