On the heels of several new acquisitions and product announcements in recent months, Motorola Solutions is announcing a new radio equipped with a voice assistant, which the company says is the first of its kind. The public safety radio is called APX NEXT, building upon the company's prior APX two-way radios, and the virtual assistant that controls it has been dubbed ViQi (pronounced "Vicky"). The company's news release on Thursday said the radio is FirstNet-ready, built with LTE connectivity, and is the first APX radio to feature a touchscreen, designed for field use including with rain or gloves. Motorola Solutions Chief Technology Officer Mahesh Saptharishi said that besides being able to control the radio, the virtual assistant responds to commands like "ViQi, run a license plate," and can also look up driver's license information and vehicle identification numbers. He said other functions will come with future updates.

In the battle of the buds, our "taste test" was striking. A friend and I both compared listening to the Foo Fighters "Learn to Fly" on Amazon's new Echo Buds and the product it aims to emulate, what Apple calls the best-selling headphone "in the world," the AirPods. The Amazon product "sounded tinnier," said Jan Schreiber, a Laguna Beach, California-based photographer. The AirPods had richer bass and a fuller sound. We switched to other songs, and the verdict didn't change.

According to an old fable, if you sat a monkey at a keyboard and gave it enough time, it would eventually type out the entire works of Shakespeare. The idea behind the thought experiment is a simple one, but it is an outdated way of thinking about artificial intelligence (AI). For example, consider the predictive text function on a phone -- unlike the hypothetical monkey, it does not randomly generate suggested words, but detects patterns in the way we write to select the likeliest options. "We've gone from predicting the next letters in a word to predicting the next word to being able to autogenerate whole sentences, and sometimes whole articles," Adelaide-based AI researcher, Dr John Flackett, said. Last year, Google launched Smart Compose for Gmail users which anticipates potential endings to sentences as you type them out, with the company promising it would "save you time".

Can't get your dog or that tiger at the zoo to smile for your Instagram? A new artificially intelligent program developed by researchers from Nvidia can take the expression from one animal and put it on the photo of another animal. Called GANimal -- after generative adversarial networks, a type of A.I. -- the software allows users to upload an image of one animal to re-create the pet's expression and pose on another animal. GAN programs are designed to convert one image to look like another, but are typically focused on more narrow tasks like turning horses to zebras. GANimal, however, applies several different changes to the image, adjusting the expression, the position of the animal's head, and in many cases, even the background, from the inspiration image onto the source image.

Notebooks are the data scientist best friend and can also be a nightmare to work with. For someone accustomed to work with modern integrated develop environments(IDEs), working with notebooks feels like going back decades. Furthermore, modern notebook environments is mostly constrained to Python programs and lack first-class support for other programming languages. Polynote was born out of the necessity to accelerate data science experimentation at Netflix. Over the years, Netflix has built a world-class machine learning platform mostly based on JVM languages like Scala.

Matrix completion is often applied to data with entries missing not at random (MNAR). For example, consider a recommendation system where users tend to only reveal ratings for items they like. In this case, a matrix completion method that relies on entries being revealed at uniformly sampled row and column indices can yield overly optimistic predictions of unseen user ratings. Recently, various papers have shown that we can reduce this bias in MNAR matrix completion if we know the probabilities of different matrix entries being missing. These probabilities are typically modeled using logistic regression or naive Bayes, which make strong assumptions and lack guarantees on the accuracy of the estimated probabilities. In this paper, we suggest a simple approach to estimating these probabilities that avoids these shortcomings. Our approach follows from the observation that missingness patterns in real data often exhibit low nuclear norm structure. We can then estimate the missingness probabilities by feeding the (always fully-observed) binary matrix specifying which entries are revealed or missing to an existing nuclear-norm-constrained matrix completion algorithm by Davenport et al. [2014]. Thus, we tackle MNAR matrix completion by solving a different matrix completion problem first that recovers missingness probabilities. We establish finite-sample error bounds for how accurate these probability estimates are and how well these estimates debias standard matrix completion losses for the original matrix to be completed. Our experiments show that the proposed debiasing strategy can improve a variety of existing matrix completion algorithms, and achieves downstream matrix completion accuracy at least as good as logistic regression and naive Bayes debiasing baselines that require additional auxiliary information.

Apple said that Deep Fusion "uses advanced machine learning to do pixel-by-pixel processing of photos, optimizing for texture, details and noise in …

We can say that we understand neural networks then and only then if you will come to me and say that the best model ever for some task has a 100 layers, and I will answer "No! 101 layers model is the best!". By this philosophical paper, we express our opinion on the neural network's understanding issue. We propose understanding requirements and based on them, describes the state when we can say that we understand the neural networks. The problem of "understanding" is very hotly debated in the machine learning community in the last time. We hope that our article will draw even more attention to this problem.

The Personalization team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music and podcasts better than anyone else so that we can make great recommendations to every individual person and keep the world listening. Everyday, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search as well as original playlists such as Discover Weekly and Daily Mix. Were a team of technologists, product insight experts, designers, and product managers in Boston, New York, Stockholm, and London.

While many scifi authors write serial novels to make a living specializes the Chinese-American in short stories & novellas and publishes them infrequently. In the past 28 years, he's released 17 short stories and novellas (gq.com). The frugality seems to support the quality, Chiang collected a lot prestigious awards and his novella "Story of Your Live" was turned into the movie "Arrival". Recently Chiang published his second book: "Exhalation" ( amazon). This book is again a collection of short stories like the first book "Stories of Your Life and Others" from 2002.