Disney is taking scary clown makeup to the next level. It's using a new projection system to transform the appearance of actors during live performances, tracking facial expressions and "painting" them with light, rather than physical makeup. Called Makeup Lamps, the system was developed by a team at Disney Research, and it could potentially change the way stage makeup is used in future theater productions. Makeup Lamps tracks an actor's movements without using the facial markers common in motion capture, then it displays any color or texture the actor wants by adjusting the lighting. It can make someone appear older by creating "wrinkles" on their face, for example, or it can paint their face in creepy clown makeup, à la Heath Ledger in The Dark Knight.

The digital media industry has long faced a unique data challenge. Radio, television and film productions generate massive unstructured data sets, which can measure up to hundreds of terabytes total for a single large-scale film project. The amount of data generated by these digital media productions is growing rapidly. In fact, IDC has projected that the world's total amount of data will grow to 44 zettabytes (or a billion terabytes) by 2020, and a whopping eighty percent of that growth will be from unstructured content much of it created by the digital media industry. Managing and storing all this digital content can be both complicated and costly.

Recently I watched a 15-second Burger King commercial, which was designed to trigger my voice-activated Google devices. In the ad, a Burger King employee, standing behind a counter at the restaurant, stared into my screen and told me that he didn't have enough time to explain all the "fresh ingredients in the Whopper sandwich." He glanced to the side, suggesting that he was about to let me in on a little secret. Then the camera zoomed in, and in a clear, crisp voice he said, "O.K. Google. What is the Whopper burger?" The video cut to black just as my phone, watch, and Google Home responded to the trigger, reading the first few lines of a Wikipedia entry about the Whopper in an unsynchronized mess of sound.

We may not give it a lot of thought, but artificial intelligence is already part of our daily lives in a number of ways. When it works well, it makes our life slightly easier – from trying to anticipate our needs (e.g. Amazon's suggested products or Netflix's recommended shows) to preventing us from being victims of fraud (e.g. As a result, our experiences as consumers and humans becomes more seamless and pleasant. It doesn't take long to get used to this, to the point that some of us are now starting to expect a certain level of personalisation prompted by artificial intelligence systems in our interactions with brands in the B2C space.

Artificial intelligence will begin to play a more vital role in areas such as virtual reality and gaming, agreed several speakers Sunday at the National Association of Broadcasters Show's Future of Cinema Summit. "We have been talking about using AI in gaming, but it's more a simulated intelligence. If the player does something that's not in the list [of actions, it won't properly respond]," said Kevin Bolen, immersive and interactive audio designer at Skywalker Sound. "I think the next generation is an artificial character that responds and interacts appropriately based on what the player does. This will give us a chance to create interesting experiences."

Spotify, the most popular music streaming service, might be getting ready to jump into the hardware game -- if a few job postings are to be believed. The company recently posted a handful of openings that make clear references to designing and selling hardware direct to Spotify users. A posting for a senior hardware product manager says that the eventual hire would work on an initiative to "deliver hardware directly from Spotify to existing and new customers." It also indicates that the hardware would be "a category defining product akin to Pebble Watch, Amazon Echo, and Snap Spectacles." Spotify indicates that this would be a "fully-connected" hardware device; the senior product manager would define both the internet-connected hardware requirements as well as its software.

Two years ago, Google introduced Jump, a VR platform that uses cloud-based software and smart stitching algorithms to make 360-video creation easier than ever before. It also partnered with GoPro to make the Odyssey, a 16-camera rig that was the first-ever camera to have the Jump software built right in. Now, Google is ready for the next generation of Jump, and for that, it's partnered with a new company and made a brand new camera. The device is called the Yi Halo, it'll retail for $16,999 and Google is touting it as the "next generation Jump camera." The Yi Halo was built in collaboration with Yi Technology, a Xiaomi-backed GoPro competitor mostly known for its action cameras.

ContentWise, the personalization, discovery, analytics and metadata expert, today announced that Cablevisión Argentina (CVA) has successfully deployed the ContentWise personalization system in Cablevisión Flow, its new suite of multiscreen television services as part of its drive to provide new, next-generation services to its customers. ContentWise has been selected as part of a new, best-of-breed video platform, which includes the Minerva 10 multiscreen TV platform by Minerva Networks. The Contentwise Personalization system anticipates user s actions and facilitates discovery by sorting content based on user s taste as well as showing trending titles popular with other subscribers with similar viewing habits. ContentWise next-generation solution enables CVA to utilize: Personalized content discovery with context-aware algorithmic and social content recommendations; Automatic micro-genres, dynamically adapted to CVA Spanish offering; Assisted content curation tools, including business rules providing total control to editorial and marketing teams. ContentWise is the TV personalization software that gives broadcast, Pay TV and OTT operators total control over the curation and automation of the Personalized TV experience, providing a UX engine API that controls each user interface element across screens and apps.

Brash Games is no more. The video game news site that claimed to be a source for unbiased game reviews has folded its website and its domain is now for sale. Just less than 24 hours ago, Brash Games was still putting up reviews for "Star Wars: Galactic Battlegrounds Saga" and "Flinthook" on its website, but now when gamers try to access the site they end up loading a page that says the domain name for Brash Games (brashgames.co.uk) is up for grabs. The domain listing, which is courtesy of GetDotted.com, The shutdown comes after the website came under fire when its shady business and unethical practices got exposed.

I will use {ordinal} clm() (and other cool R packages such as {text2vec} as well) here to develop a hybrid content-based, collaborative filtering, and (obivously) model-based approach to solve the recommendation problem on the MovieLens 100K dataset in R. All R code used in this project can be obtained from the respective GitHub repository; the chunks of code present in the body of the post illustrate the essential steps only. The MovieLens 100K dataset can be obtained from the GroupLens research laboratory of the Department of Computer Science and Engineering at the University of Minnesota. The first part of the study introduces the new approach and refers to the feature engineering steps that are performed by the OrdinalRecommenders_1.R script (found on GitHub). The second part, to be published soon, relies on the R code in OrdinalRecommenders_3.R and presents the model training, cross-validation, and analyses steps. The OrdinalRecommenders_2.R script encompasses some tireless for-looping in R (a bad habbit indeed) across the dataset only in order to place the information from the dataset in the format needed for the modeling phase.