Filmmaker Oscar Sharp pulled together a cast and crew for a day of filming. He had actors, including Thomas Middleditch from HBO's "Silicon Valley." He had a set, props and special effects. Most importantly, he had a script written by an artificial-intelligence neural network. And the finished product, "Sunspring," is exactly as weird as you'd expect.

Filmmaker Oscar Sharp pulled together a cast and crew for a day of filming. He had actors, including Thomas Middleditch from HBO's "Silicon Valley." He had a set, props and special effects. Most importantly, he had a script written by an artificial-intelligence neural network. And the finished product, "Sunpring," is exactly as weird as you'd expect.

In this paper, we describe a case study in a big metropolis, in which from data collected by digital sensors, we tried to understand mobility patterns of persons using buses and how this can generate knowledge to suggest interventions that are applied incrementally into the transportation network in use. We have first estimated an Origin-Destination matrix of buses users from datasets about the ticket validation and GPS positioning of buses. Then we represent the supply of buses with their routes through bus stops as a complex network, which allowed us to understand the bottlenecks of the current scenario and, in particular, applying community discovery techniques, to identify clusters that the service supply infrastructure has. Finally, from the superimposing of the flow of people represented in the OriginDestination matrix in the supply network, we exemplify how micro-interventions can be prospected by means of an example of the introduction of express routes.

The security threat landscape continues to evolve not just in scale, but, more importantly, in sophistication. Despite a range of advancements in the industry to safeguard against increasingly bold and intricate threats, organizations have struggled to keep pace with the technologies and techniques employed by those responsible for such attacks. As companies continue to increase their digital footprints, "identify and diagnose" capabilities are not enough to remediate against a growing fundamental business challenge for organizations of all shapes and sizes. We spoke with Avivah Litan, vice president and distinguished analyst at Gartner, about the development of advanced security analytics and important considerations for organizations looking to implement machine learning to defend against an array of internal and external security threats. Q: How are analytics and machine learning changing the current security landscape?

In 10 years, we'll see big changes in how people live their lives and how companies operate, thanks to the innovation that's now being kindled around the world. So says Alec Ross, former innovation adviser to Hillary Clinton during her term as U.S. Secretary of State, who lays out the thesis in his new book, The Industries of the Future. In Part 1 of our interview, Ross explains how the technology underlying the cryptocurrency Bitcoin, blockchain technology, will greatly reduce the friction in financial and other transactions. Here he discusses the impact of cognitive robots and the outlook for U.S. companies trying to compete on a global scale. You talked about robotics a lot in your book and you focused for the most part on physical robots.

In Panama, indigenous tribes are turning to a modern tool to help protect their homes: drones. Vast rainforests, which once covered more than half of Panama's land surface, are shrinking – eaten away by development, both official and unofficial. Forest land is becoming mines, hydroelectric projects, farmland, cattle habitat, and the site of illegal logging. In response, seven indigenous tribes, whose members live in autonomous zones known as comarcas, have begun sending up drones to keep an eye on their forests. Three members from each tribe received a month of training on how to use the drones, Reuters reports.

Months after Susan Tully and friends bought a pair of professional video game teams for an estimated 1 million, her four-man "Call of Duty" squad finished its season in 11th out of 12 places. There, exposure and sponsor interest would dissolve. The distress Tully felt as she spent an April afternoon in a small, dark Burbank video studio watching her team attempt to avoid demotion was not the emotion she banked on when she put her money into the burgeoning industry. But upheaval is becoming something of a routine for the investors fueling pro video gaming's rapid rise. China's richest man, Russia's richest man, the U.S.'s fourth-richest man and a string of American multimillionaires all have ties to teams now.

A lot can change in the space of six years: At the 2010 FIFA World Cup in Germany, match results were predicted by an octopus named Paul. As Euro 2016 prepares to kick off Friday in France, scientists are using advanced neural networks to try to figure out which team will win this summer's big soccer tournament. As fans from across the continent begin their journeys toward France this week, predictions among them will be based on passion, patriotism and hope rather than algorithms, artificial intelligence or machine learning. But that's not stopping companies like Microsoft, Yahoo and Blue Yonder from trying to leverage their technology to predict this year's winner. At the World Cup in 2010, Paul the Octopus became a celebrity by accurately predicting the results of every single game involving host country Germany, which went on to win the tournament.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

How much should you charge someone to live in your house? Or how much would you pay to live in someone else's house? Would you pay more or less for a planned vacation or for a spur-of-the-moment getaway? And the struggle to do so, my colleagues and I discovered, was preventing potential rentals from getting listed on our site--Airbnb, the company that matches available rooms, apartments, and houses with people who want to book them. In focus groups, we watched people go through the process of listing their properties on our site--and get stumped when they came to the price field. Many would take a look at what their neighbors were charging and pick a comparable price; this involved opening a lot of tabs in their browsers and figuring out which listings were similar to theirs.