The following post was first published in an earlier edition of Marketing Insider: Cross-Channel. Creating original branded content solves many problems for marketers, but also presents challenges -- among them distribution and realizing ROI from what can be a costly investment. Time Inc., CBS and Telepictures are among hundreds of publishers working with IRIS.TV, which recently introduced a product to manage the distribution of branded content. Its video personalization solution uses artificial intelligence and machine learning technology so publishers can automate the programming of their video libraries for the individual based on that person's preferences and behavior. We spoke with Rohan Castelino, director of business development and marketing with IRIS.TV, about how this works.

Now that you have an awesome, king-of-the-universe bot developed, packaged, and ready to roll, it's time to put it in front of consumers. It's time to make it discoverable. Making your bot discoverable is no different than making your mobile app or your website discoverable. It's a process that needs to be approached methodically. Let's take an imaginary "Sports News Delivery Bot" as an example and see how three key prerequisites may help you reach your potential consumers.

It's 2025 (give or take), and the long-awaited Big One has hit the San Francisco Bay Area. In the frenetic aftermath, teams of specialized rescue workers begin tearing through piles of wreckage--searching for signs of life, administering care, and calling for backup. As Stanford University's leading AI scientist Fei-Fei Li imagines it, they're robots with the smarts to "see" through their immediate surroundings and respond to humans in need, saving the maximum number of lives they can. The enabling technology behind this scenario is one Li has thought about and researched deeply--and it's not too far off, she argues, if computers can master what is arguably humankind's most complicated cognitive ability: vision. Current research, led by Li and the Stanford Artificial Intelligence Laboratory she directs, has already gotten us partially there, thanks to a database of more than 15 million digital images built in 2009.

Today, even though similar approaches are in use, they are usually just one part of complex recommendation approaches that can include large collections of algorithms and data sources. The data set was again provided year that the summer school on Recommender Systems by Moviepilot and was co-organized by TU Berlin. By 2007, the Netflix Prize had The second track focused on recommendation of scientific attracted thousands of participating teams, and the papers. The challenge attracted 30 participating Netflix Prize concluded. At the by Simon Fraser University and Yelp who also 2010 ACM RecSys conference, the seed for what provided the data. CAMRa attracted a moderate the 2014 challenge did not focus on classical recommendation, number of participants, but contributed to establishing but rather on prediction of user engagement, the RecSys Challenge series.

Can the language used in movie reviews hold the tea leaves revealing the winners of the Academy Awards? That's the hypothesis of Luminoso Technologies, an artificial-intelligence startup that specializes in natural-language processing, which has already declared the likely best-picture winner of the 2017 Academy Awards before the nominations are even out: Pablo Larraín's biopic "Jackie," starring Natalie Portman as Jacqueline Bouvier Kennedy. Here's the methodology: The company analyzed user movie reviews for 2013-15 in IMDb, focusing on the 50 most popular movies of each year, to see if there was a correlation behind the concepts that appeared in their language and the eventual Oscar nominees that year. Luminoso's software found certain specific concepts -- such as "cinematography," "masterpiece," "stunning," "visuals" and "experience" -- were highly correlated with films that received nominations. Concepts like "narrative" had less correlation with Oscar nods, and a few (like "CGI" and "horror") had negative correlation.

After studying the tribe, which was still living in the preliterate state it had been in since the Stone Age, Ekman believed he had found the blueprint for a set of universal human emotions and related expressions that crossed cultures and were present in all humans. A decade later he created the Facial Action Coding System, a comprehensive tool for objectively measuring facial movement. Ekman's work has been used by the FBI and police departments to identify the seeds of violent behavior in nonverbal expressions of sentiment. He has also developed the online Atlas of Emotions at the behest of the Dalai Lama. And today his research is being used to teach computer systems how to feel.

TEHRAN, Iran – Sustained gunfire rang out over central Tehran on Monday afternoon as anti-aircraft guns targeted what officials said was a drone flying over the Iranian capital. Many residents ran to rooftops and craned their necks to see what was happening. Others sought shelter as bursts of machine gun fire echoed through the streets. The semi-official Tasnim news agency quoted Tehran Governor Isa Farhadi as saying that the gunfire targeted a drone near restricted airspace in the capital. It wasn't clear who owned the drone, which he described as a quadcopter. That suggests it may have been operated by a local hobbyist or aerial photographer rather than a foreign government.

A true genius, Alan Turing was played brilliantly by Benedict Cumberbatch inThe Imitation Game -- the movie about his life and role in ending WWII -- which introduced him to a whole new generation of admirers. It was Turing who predicted machine learning would play a big role in modern computing in his article the "Turing Test," way back in 1950. Indeed, Turing was way ahead of his time, which was a major theme in the movie, but now the world has caught up. The major advancements in readily accessible computing power, the quantity of data available, and algorithms that truly make machine learning possible are driving our ability to process data, analyze it, and act on it in ways that would make Mr. Turing proud. These advances have completely changed the machine learning game: The fundamental concept remains the same, but now it's far more sophisticated, efficient, and easily deployable. Beyond the big headline-grabbing examples of how machine learning will impact our lives -- such as through driverless cars -- it has exciting potential to put an end to the bland and sometimes ineffective customer experiences that many retailers are delivering to their customers.

AMC is producing a six-part series on the history of science fiction in collaboration with one of its most prolific modern purveyors, James Cameron. Tentatively titled James Cameron's Story of Science Fiction, it will examine the history of science fiction from the early days of H. G. Wells and Jules Verne to the pop-culture juggernaut it is today. "When I was a kid, I basically read any book with a spaceship on the cover and I saw 2001: A Space Odyssey many, many times," Cameron said in a statement Each episode will tackle a "big question" that humankind has pondered over the ages, AMC said. The series will also feature Cameron and other notable sci-fi personalities debating "the merits, meanings and impacts of the films and novels that influenced them." Cameron added that he's interested in not just the history of the genre, but how it informed and interacted with real science and space exploration.

Lily Robotics, the defunct manufacturer of the world's first "selfie drone", is being sued over allegations that it faked product shots and misled consumers about the capability of its prototype devices. The lawsuit alleges that videos on Lily's website, presented as though they had been taken by the drone, were in fact shot by a mixture of GoPro cameras and DJI drones, a competitor model that costs up to four times as much and requires a skilled filmmaker to manually control the camera. The San Francisco District Attorney's (SFDA) office filed the case on Thursday, the day after Lily announced it was shutting down and refunding customers who had pre-ordered its drone at prices ranging from $499 to $899. Promotional videos detailed a number of groundbreaking capabilities of the Lily drone: the ability to take off from, and land on, a user's outstretched hand; a waterproof casing to enable water-based launches; and most impressively, autopilot mode that could allow the drone to follow the user at a set distance and automatically film them. In the videos, a drone operating under the autopilot setting was shown following people engaged in extreme sports such as snowboarding and white water kayaking.