Amazon's Echo, Plus and Dot speakers will finally be available in Japan starting next week. To prepare for the devices' arrival in the island nation, the e-retail giant taught the voice assistant how to understand and respond in the Japanese language. Alexa SVP Tom Taylor said the company designed an all-new experience "from the ground up for Japanese customers, including a new Japanese voice, local knowledge and over 250 skills from Japanese developers." Users in the country can issue typical Alexa voice commands in their native tongue. They can, say, listen to news from Japanese media companies like NHK, TBS TV, TV TOKYO, Asahi Newspaper and Mainichi Newspaper.

How often has any piece of consumer technology had as eventful a year as 2017 has been for Amazon's Alexa voice service and Echo hardware? Consider the evidence: A year ago, Amazon boasted that there were 4,000 Alexa skills–tasks the service can perform, from setting a Nest thermostat to playing Jeopardy–up from 135 the previous January. Today, the count stands at 25,000. Alexa can now make phone and video calls, distinguish between the voices of multiple household members, and display information on screens, none of which it was able to do when the year began. In August, Amazon and Microsoft even announced that Alexa and Cortana would be able to talk to each other, a first-of-its-kind arrangement in the voice-assistant market.

Recommender systems problems witness a growing interest for finding better learning algorithms for personalized information. Matrix factorization that estimates the user liking for an item by taking an inner product on the latent features of users and item have been widely studied owing to its better accuracy and scalability. However, it is possible that the mapping between the latent features learned from these and the original features contains rather complex nonlinear hierarchical information, that classical linear matrix factorization can not capture. In this paper, we aim to propose a novel multilayer non-linear approach to a variant of nonnegative matrix factorization (NMF) to learn such factors from the incomplete ratings matrix. Firstly, we construct a user-item matrix with explicit ratings, secondly we learn latent factors for representations of users and items from the designed nonlinear multi-layer approach. Further, the architecture is built with different nonlinearities using adaptive gradient optimizer to better learn the latent factors in this space. We show that by doing so, our model is able to learn low-dimensional representations that are better suited for recommender systems on several benchmark datasets.

If you have a Sony TV in your living room, it just got a little smarter. Sony is rolling out an update to many of its Android TV-powered 4K HDR sets (more on that in a moment) to enable Google Assistant. Talk to your remote and you'll get the same AI helper that you can likely find on your phone. Naturally, Assistant can do a little more than you're used to on your phone: you can ask about the weather, search for factoids or play videos, but you can also control your TV. Sony is particularly keen to tout the'seamless' smart home control -- you should have an easier time dimming the lights when it's movie time.

Suddenly, artificial intelligence (AI) is everywhere. For decades, the dream of creating machines that can think and learn like humans seemed like it would be perpetually out of reach, but now artificial intelligence is embedded in the phones we carry everywhere, the websites we use every day and, in some cases, even in the appliances we use around our homes. The market researchers at IDC have predicted that companies will spend $12.5 billion on cognitive and AI systems in 2017, 59.3% more than they spent last year. And by 2020, total AI revenues could top $46 billion. In many cases, AI has crept into our lives and our work without us realizing it.

R2-D2 may seem like a blast to hang out with, but in real-life, robots are rarely social butterflies. They're fantastic assembly line workers, bolting together cars and refrigerators, but most are all work and no play. The closest thing to robotic friends we have right now are the growing number of smart speakers, like the Amazon Echo, each with an voice assistant inside them. Smart speakers have carved out a few uses, like playing music, cracking jokes, telling us the weather, and controlling our smart home devices. Which is why I was excited to meet Jibo.

As IoT branches out over the next few years, transforming the way consumers live, work, and think, we will enter the age of ubiquitous computing. By 2025, there will be more than 100 billion connected devices – that's 14 for every person on the planet – generating a revenue of almost $10 trillion. The scale of data creation in this environment is too vast to imagine. But there's no escaping data, and finding new ways to analyze and harness all this information will be just as critical as it is challenging. Enter artificial intelligence: Our key to unlocking never-before-seen opportunities in data and spending less time driving these insights into action. During the mobile era, devices became a distraction.

German luxury car maker Daimler is set to combine a range of digital customer support experiments into a single voice-activated app that is available whether users are driving their cars, sitting at home or on the go. Chief digital officer Sabine Scheunert said on Tuesday that Daimler will soon introduce a digital assistant called'Ask Mercedes' that ties together previous trial projects on platforms such as Google and Facebook. Scheunert said'Ask Mercedes' will be available in several markets and languages immediately before being rolled out globally, without providing further details. 'Ask Mercedes is a new cognitive assistant available any time to support customers in exploring all the functions of their Mercedes-Benz vehicles,'the firm said at the Lisbon Web Summit. Mercedes is one of Daimler's leading car brands.

New York State's power grid is built to accommodate only a few hours of the highest short-term load of the year. More than 90% of the time, that oversized capacity sits idle--costing rate-payers $17 billion over the past decade. That number will nearly double to $30 billion over the next 10 years if changes aren't made. But New York technology companies are diving into this problem and others, and have some promising solutions. While other startup ecosystems boast the next best dating app, New York City startups are integrating business and technology to solve the world's most pressing energy problems.

Chatbots, voicebots, Alexa skills, Google actions, intelligent assistants -- these are different all, yet possess a single common denominator: they're conversational apps. Underlying every conversational app is the exchange of words between humans and machines. Today's technology is mature enough to create high-level conversation, using broadly available building blocks. The building blocks required to develop a modern conversational application. Let's describe these building blocks in more detail!