One of the great things about Netflix's global expansion is that it allows audiences to enjoy new movies and TV shows that have been produced in other countries. French subscribers, for example, saw their first local original -- Marseille -- debut around this time last year. Today, as part of a wider statement confirming the creation of 400 new European jobs, Netflix announced that it has greenlit its second French-language TV series, Osmosis. Judging by the show's synopsis, fans of Charlie Brooker's Black Mirror could be in for a treat. Netflix says its new original will be an 8-episode drama set in Paris in the near future. It's centered around a new dating app called "Osmosis," which is capable of reading its users' thoughts and feelings -- or brain data -- to "find a perfect match with 100 percent accuracy."

Microsoft's showcasing the power of universal apps, machine learning, and the Microsoft ecosystem with Story Remix, an intriguing new app that helps you create personalized videos quickly and easily. Story Remix intelligently pulls from your collection of videos and images to create highlight videos of events on the fly, or you can start a new album of your own. So far, so blah--Google Photos already does that. But Story Remix shakes things up with powerful editing tools and integration with Microsoft's Remix 3D service to add "mixed reality" digital images and animations to your projects. It looks remarkably simple and surprisingly powerful.

As consumers get more comfortable with AI and understand its ability to improve their lives, companies are making investments to improve key supporting technology, such as translation, algorithms and discovery. In IBB Consulting's work with media companies, we've identified three key areas where opportunity exists to integrate AI and machine learning to improve the customer experience, boost revenues, increase productivity and more. From chatbots and content creation to new levels of personalization, the following areas are opportunities to integrate AI into everything from consumer-facing properties to internal functions. AI-driven chatbots can already interact with people on the web or on mobile to help find information, answer questions and sell services. We're early in the game when it comes to how they will be deployed, with the goal being to ultimately get to a place where communicating with a chatbot feels no different than chatting with a real person.

With the Fall Creators Update coming in September, Microsoft has unveiled a new app that can transform you photos and videos into a cinematic spectacle, complete with CG effects, titles and a soundtrack. Called Story Remix, it's a Universal Windows App that uses deep learning and Microsoft's Graph, letting you pull media in from colleagues, friends and others. It's a powerful, yet simple app that uses image analysis and AI to to clip discovery, effects integration, and even the entire edit, complete with music. It's like a fancier, smarter version of Apple Clips, Google's Photo Assistant or the Magisto app (remember that?), but geared towards Windows 10 content. As a nod to the latest Creators Update, it can also pull in 3D objects from Paint 3D and other programs, or let you draw on your videos and photos using Window Ink.

It lives inside of Allo, Google's ML-driven chat app. Starting today, when you pull up the list of stickers you can use to respond to someone, there's a simple little option: "Turn a selfie into stickers." Tap, and it prompts you to take a selfie. Then, Google's image-recognition algorithms analyze your face, mapping each of your features to those in a kit illustrated by Lamar Abrams, a storyboard artist, writer, and designer for the critically acclaimed Cartoon Network series Steven Universe. There are, of course, literally hundreds of eyes and noses and face shapes and hairstyles and glasses available. All told, Google thinks there are 563 quadrillion faces that the tool could generate.

Taste in movies is idiosyncratic, and not linked to the demographic traits that film studios target, a study has found. It also shows that moviegoers' ratings don't always match those of film critics. The survey of more more than 3,000 people found that the best predictor of a non-critics' response to a film was the aggregated evaluations of other non-critics, such as those on the Internet Movie Database (IMDb). The results of a study revealed that there were generally low levels of correlation in movie preferences among non-film critics - in other words, their movie tastes were highly individualistic. 'What we find enjoyable in movies is strikingly subjective - so much so that the industry's targeting of film goers by broad demographic categories seems off the mark,' says Dr Pascal Wallisch, a clinical assistant professor in New York University's Department of Psychology and the senior author of the study.

Thousands of analysts, journalists and developers came to the Washington State Conference Center in Seattle today to see what Microsoft had to unveil at its three-hour-long Build conference. As it turns out, there wasn't a lot of interesting news for non-developers. In other words, if you had played a drinking game with the trigger words being "Azure," "Microsoft Graph" and "Visual Studio," you would have needed two kegs of liquor. To be fair though, Build is an event for developers. Still, there were updates around new Cortana skills, artificial intelligence for the workplace and a PowerPoint translator tool that may have useful applications for consumers.

I put together an in-depth tutorial to explain Transforms (Data Augmentation), the Dataset class, and the DataLoader class in Pytorch. I also show a ton of use cases for different transforms applied on Grayscale and Color images, along with Segmentation datasets where the same transform should be applied to both the input and target images. I show how to do Affine transforms (rotation, translation, shear, zoom), some awesome Image-based transforms (saturation, brightness, contrast, gamma, grayscale). These transforms can be applied with pre-determined settings or randomly sampled from a range of values. I also show some cool utility transforms like type casting, converting to tensors, and going from CHW to HWC.

Digital video consumption has surged, igniting new monetization opportunities for these modern distribution outlets and content makers, a point I explored in a previous essay. The challenge I didn't touch on, however, is how to optimize these monetization events in the most efficient manner. Machine learning is at the root of the answer. Machine learning is when computers learn and analyze new data without being programmed. It's when software can recognize patterns and draw conclusions.

As the basic toolkit becomes more rounded, that's where the applications will be - using pretrained models, maybe with a little fine tuning. Some of these functions should be included directly in the OS (such as speech recognition). I hope AI tools and pretrained models will become more and more integrated and accessible, because now they just sit in separate repositories and libraries, needing a considerable amount of effort to make them work together. One model does age detection, another does sex detection, another action recognition, and so on, but we need them all in one place. As I see it, AI is slowly converging towards differentiable programming, flexible network structure (torch-style frameworks), graph based neural nets and hierarchically composing higher order skills from lower order ones, anyway.