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.

Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research \& development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice.

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict open types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets. Our data and model can be downloaded from: http://nlp.cs.washington.edu/entity_type

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.

This Saturday, the Robot Film Festival is taking place in Portland, Ore. This is the 8th year of the festival, and after bouncing around between San Francisco, Pittsburgh, and Los Angeles, the festival has (at least temporarily) settled on the greatest city on Earth (and coincidentally my hometown), Portland. The theme this year is "Vintage Arcade Revival," celebrating the culture and technology of the 1980s. Or at least, celebrating what passed for culture and technology back then. I'll be giving a short introductory talk to help kick off the film festival, about the progress we've seen in social home robots from the 1980s to today.

Jefferson Graham shows the many options for finding audio podcasts to listen to, from apps to connected speakers, TV and the car, on #TalkingTech. In July, we've covered everything from how to get the beat deals on Amazon Prime Day to tips on creating and publishing an e-book and we previewed the new website with Netflix's Phil Rosenthall on Talking Tech, USA TODAY's daily, seven-days-a-week podcast. You can listen to Talking Tech on Stitcher, Apple or Google Podcasts or wherever you enjoy online audio. Talking Tech offers the latest tech news updates, gadget reviews, opinion on tech trends and interviews with insiders. Here are the July Talking Tech episodes.