I'll be covering light-weight model architectures, model compression techniques, and other optimizations in ML libraries; mostly from the perspective of targetting efficient deployment of DNNs.

In today's day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborative filtering has been tackled using Matrix Factorization which is linear in nature. We extend the work of [11] on using variational autoencoders (VAEs) for collaborative filtering with implicit feedback by proposing a hybrid, multi-modal approach. Our approach combines movie embeddings (learned from a sibling VAE network) with user ratings from the Movielens 20M dataset and applies it to the task of movie recommendation. We empirically show how the VAE network is empowered by incorporating movie embeddings. We also visualize movie and user embeddings by clustering their latent representations obtained from a VAE.

Today, Artificial Intelligence (AI) and Machine Learning (ML) are two popular terms that tech companies cannot stop talking about. Everyone from Google and Microsoft, to Apple, Samsung and Amazon are going big on AI. Besides smartphones, smart speakers, voice assistants, apps, connected cars, security surveillance, healthcare and customer support are other areas where AI is used. Machine Learning (and deep learning) has been going on for years, and now with the data that exists, tech companies are putting it to the best use. On device machine learning combined with artificial intelligence can help in anticipating things in advance. But while you may not know, AI and ML can be very helpful in customer service too.

For over 50 years, Disneyland and its sister parks have been a showcase for increasingly technically proficient versions of its "animatronic" characters. First pneumatic and hydraulic, and more recently fully electronic, these figures create a feeling of life and emotion inside rides and attractions, in shows and, increasingly, in interactive ways throughout the parks. The machines they're creating are becoming more active and mobile in order to better represent the wildly physical nature of the characters they portray within the expanding Disney universe. And a recent addition to the pantheon could change the way that characters move throughout the parks and influence how we think about mobile robots at large. I wrote recently about the new tack Disney was taking with self-contained characters that felt more flexible, interactive and, well, alive than "static," pre-programmed animatronics.

Don't fall behind when it comes to applying machine learning in your facility. Employing analytics with the mass of data collected in your facility can help you cut costs across the board. There are five steps key to getting the most out of machine learning, according to Ash Awad, Chief Market Officer at McKinstry, a design, build, operate and maintain firm. Follow this blueprint to optimize your building through data analytics. Related: Machine Learning 101: Is Predictive Analytics Possible in Your Facility?

Google's Tilt Brush is one of the best VR painting apps for the Oculus Rift and HTC Vive. Since its release in 2016, artists have drawn magnificent ships, jaw-dropping mountain ranges and imaginative fight scenes in immersive 3D. Most of the app's brushes, however, mimic the real world with flat, ribbon-like strokes. For years, you've had to move around and paint, or'color in' every surface of a 3D object like a cube or cone. It was pretty time consuming.

To put it mildly, news media has been on the sidelines in AI development. As a consequence, in the age of AI-powered personalized interfaces, the news organizations don't anymore get to define what's real news, or, even more importantly, what's truthful or trustworthy. Today, social media platforms, search engines and content aggregators control user flows to the media content and affect directly what kind of news content is created. There's a history: News media hasn't been quick or innovative enough to become a change maker in the digital world. Historically, news used to be the signal that attracted and guided people (and advertisers) in its own right. The internet and the exponential explosion of available information online changed that for good.

To put it mildly, news media has been on the sidelines in AI development. As a consequence, in the age of AI-powered personalized interfaces, the news organizations don't anymore get to define what's real news, or, even more importantly, what's truthful or trustworthy. Today, social media platforms, search engines and content aggregators control user flows to the media content and affect directly what kind of news content is created. There's a history: News media hasn't been quick or innovative enough to become a change maker in the digital world. Historically, news used to be the signal that attracted and guided people (and advertisers) in its own right. The internet and the exponential explosion of available information online changed that for good.

American visual computing company Nvidia, along with researchers from Aalto University and MIT, have come up with an artificial intelligence (AI) program that removes defects from images such as grain, text and watermarks, without studying how clean photos look like. "Our AI program shows significant benefits that can be reaped by removing the need for potentially strenuous collection of clean data (images)," the team said. The AI program is expected to find use in the medical field to eliminate extensive post-processing that is deployed to remove noise from MRI (magnetic resonance imaging) images. "There are several real-world situations where obtaining clean training data are difficult: Low-light photography (for example, astronomical imaging) and MRI," the team said. Previously, a neural network could cut out noise from images only after being trained on example pairs of unclean and clean images.

NVIDIA teamed up with researchers from Finland's Aalto University and MIT to teach an old AI a new trick. Their neural network can now fix grainy or pixelated images in your photo library just by looking at them. AIs have been able to do similar work for a while, but typically it required both a so-called noisy image (grainy, pixelated) and a noise-free one in order for the AI to learn how to make up the difference and clean up the photo. This new method, which is being presented at the International Conference on Machine Learning in Stockholm this week (assuming my invitation was lost in the mail?), no longer requires a noise-free image for the AI to remove artifacts, noise, grain, and automatically enhance your photos. Using deep-learning work, the AI can look at those so-called noisy images and make them clear even without looking at a clean image first.