In today's so-called smart home, you can dim the lights, order more toothpaste or tell the kids to go to bed simply by talking to a small Wifi-connected speaker, such as Amazon's Echo or Google's Home. This voice-first market – combining voice with artificial intelligence (AI) – barely existed in 2014. This year, Voice Labs, a consultancy, expects 24.5 million appliances to be shipped. Other big tech firms have their own plans: Apple is taking its Siri voice assistant beyond its mobile devices to PCs, cars, and the home; Baidu last month bought Raven, billed as China's answer to Amazon's Alexa intelligent personal assistant; and Samsung Electronics plans to incorporate Viv, its newly acquired virtual assistant, into its phones and home appliances. But not everyone thinks the future of communicating with the Internet of Things needs to be vocal.

On a November, 2015 morning in Bentonville, Arkansas, first responders discovered a corpse floating in a hot tub. The home's resident, James Andrew Bates, told authorities he'd found the body of Victor Collins dead that morning. He'd gone to bed at 1 AM, while Collins and another friend stayed up drinking. This past December, The Information reported that authorities had subpoenaed Amazon over the case. The police were considering Bates a suspect in what they suspected was a murder after signs of a struggle were found at the scene.

AI services like Apple's Siri and others operate by sending your queries to faraway data centers, which send back responses. The reason they rely on cloud-based computing is that today's electronics don't come with enough computing power to run the processing-heavy algorithms needed for machine learning. The typical CPUs most smartphones use could never handle a system like Siri on the device. But Dr. Chris Eliasmith, a theoretical neuroscientist and co-CEO of Canadian AI startup Applied Brain Research, is confident that a new type of chip is about to change that. "Many have suggested Moore's law is ending and that means we won't get'more compute' cheaper using the same methods," Eliasmith says.

Over the weekend, I put a question to the Google Home speaker I'd brought back from the United States. "Is Obama planning a coup?" I'd asked this after reading an article that suggested a relatively new feature that gives answers - or Snippets as the search company call them - to queries, rather than just links, had been producing some troubling results. The piece said a search asking which US presidents were in the Ku Klux Klan had listed several as members of the KKK, despite there being no evidence for that. It also featured a search for "Proposition 63", a gun control measure, that had produced a Snippet describing it as "a deceptive ballot initiative that will criminalise millions of law abiding Californians". And then there was "Is Obama planning a coup?" which had resulted in a Snippets box describing "Western Center for Journalism's exclusive video".

The robotics market is undergoing a major transformation. Robots are growing beyond the industrial sector and beginning to assume the roles of personal assistants, surgical assistants, delivery vehicles, and autonomous vehicles, among many others. Until now, industrial robots have accounted for more than 50% of overall robotics market revenue. But 2016 marked one of the critical turning points of this shift in the robotics market, as Tractica estimates that industrial robots is set to drop to 41% of total robotics revenue, with the remaining 59% coming from non-industrial robots. Robotics is the interdisciplinary branch of engineering and science.

Amazon.com's Alexa artificial-intelligence software could generate $10 billion worth of business a year for the company, stemming from device sales and voice orders from Amazon's site. Analysts with RBC Capital Markets tried to gauge Alexa's financial impact. It's a daunting endeavor given that it is the "very early days" for the software, the analysts wrote in a note distributed Friday. Nevertheless, their projections show that by 2020 the sale of products that incorporate Alexa -- the Echo line of devices, for example -- could reach $5 billion a year. Increased shopping on Amazon's site via voice commands through Alexa could mean another $5 billion in the pocket for the e-commerce giant.

Phillip De Leon has been named associate dean of research and doctoral studies for the New Mexico State University College of Engineering. LAS CRUCES -- Through the use of New Mexico State University's High Performance Computing system, a supercomputer known as Joker, Phillip De Leon, associate dean for research in the College of Engineering, not only conducted research but students in his graduate electrical engineering course also used the system. In the Pattern Recognition and Machine Learning course, which is a data science class De Leon taught in the fall, graduate students used Joker on projects that included developing machine learning codes and evaluating the models with standard datasets. "These projects including identifying a song much like the Shazam app, recognizing handwritten digits like ZIP codes, classifying email as ham or spam, analyzing Twitter feeds, etc.," De Leon said. "Being able to use this system allowed the students to experiment and tune their codes much faster since everything ran much faster. It also allowed for big datasets to be used in training and evaluation."

In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i.e., number of seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the prevalence of the task of recommending items under capacity constraints in a variety of settings, to the best of our knowledge, none of the known recommender methods is designed to respect capacity constraints. To close this gap, we extend three state-of-the art latent factor recommendation approaches: probabilistic matrix factorization (PMF), geographical matrix factorization (GeoMF), and bayesian personalized ranking (BPR), to optimize for both recommendation accuracy and expected item usage that respects the capacity constraints. We introduce the useful concepts of user propensity to listen and item capacity. Our experimental results in real-world datasets, both for the domain of item recommendation and POI recommendation, highlight the benefit of our method for the setting of recommendation under capacity constraints.

Just when you thought Amazon's virtual assistant knew enough already, WebMD – the hypochondriac's favorite website - has teamed up with the retail giant to give Alexa medical diagnosis capabilities. The integration will allow Amazon Echo, Echo Dot and Fire TV users to ask Alexa basic health queries, such as "Alexa, ask WebMD what are the symptoms of a heart attack", or "Alexa, ask WebMD how to treat a sore throat." Amazon's Alexa virtual assistant now answers your basic health-related questions To make use of the feature, users simply need to add the skill to Alexa via the Alexa app store. Then tap inside of the search box at the top where it says "Search Skills." Type in WebMD and search, then choose to install.

Digital transformation is clearly essential to companies' future success, as businesses that manage the digital transformation process well are already seeing higher growth, profit, and employee engagement rates than their peers, according to the Leaders 2020 Research Brief. Digital transformation, however, is not merely about the implementation of new technology but, just as important, about the usage of this technology. As we go about our private lives, we are increasingly relying on the Internet of Things (IoT) to connect us to myriad applications, services, service providers, and vendors. We are connected to the IoT through everything from our smartphones, wearable fitness devices, and personal virtual assistants, to connected cars, connected homes, GPS locators, and a wide variety of sensors. All of this happens in an increasingly seamless fashion, and our usage of these technologies is affecting virtually every aspect of our daily routines and tasks. From a corporate perspective, the massive amounts of data generated – through these devices and the billions of enterprise devices and sensors that are already connected (15 billion devices in 2015 based on an Intel infographic) – is creating the potential for an entirely new level of integration and usage of data into our business decisions.