Microsoft wants the world to know it's not falling behind in the race for artificial intelligence. The technology giant--like Google goog, Facebook fb, and IBM ibm --has been pushing into the trendy field of artificial intelligence, catch-all for various technologies that help computers recognize patterns from massive amounts of data. Microsoft CEO Satya Nadella emphasized A.I. on Monday at the company's annual IT conference in Atlanta by describing how his company is using the technology and how it plans to do so a lot more in future. "We are not pursuing A.I. to beat humans at games," said Nadella, taking a subtle hit at competitors like Google and IBM, whose A.I. technologies got some attention for beating humans at the ancient Chinese board game Go and on the game show Jeopardy. Microsoft's overarching goal is to "democratize A.I.," which Nadella explained has something to do with analyzing the mountains of data produced by consumers and businesses and then presenting the findings to people who have far less free time than they used to have.

The retail industry is inundated with buzzwords that describe the best way to engage with consumers -- such as omnichannel marketing, customer journey, and a 360-degree view -- to provide a more personalized and optimized experience. All of these buzzwords point to a common problem most retail marketers are aching to solve: How can they use customer data to create timely insight into what customers are doing in-store and/or online and convert that into strategies and actions that increase sales? It's an age old question with new age solutions, and the retail industry is now leveraging artificial intelligence (A.I.) to usher in a unique way of thinking about this problem. In fact, a new chart shows there are 45 A.I. companies focused on retail across a range of verticals, including recommendations, merchandising, search, conversational commerce, and especially multichannel marketing. Using omnichannel retailing, the ability to connect with a customer both online and offline, is ideal because omnichannel customers have a 30 percent higher lifetime value than single channel customers.

Microsoft's servers are now powered by optimized custom chips that joined together to translate the entirety of Wikipedia in literally less than a blink of an eye. In a demonstration at Microsoft's Ignite conference on Orlando, Microsoft tapped what it called its "global hyperscale" cloud to translate 3 billion words across 5 million articles in less than a tenth of a second. Microsoft helped custom-design the programmable logic components, or Field Programmable Gate Arrays (FPGAs), that it has added to each of its computing nodes. The company recognizes that smarter, more computationally intensive technologies will require more computing power on the back end, whether those technologies revolve around Microsoft's own Cortana digital assistant--which can now intelligently reschedule your workout to meet your fitness goals--or something that can recognize a distracted drivers, as the automobile manufacturer Volvo is researching. Microsoft's Cortana now includes health-specific information.

Microsoft CEO Satya Nadella took the stage at his company's massive Ignite conference to lay out his vision for how deep learning and artificial intelligence will transform the company. "AI is at the intersection of our ambitions," Nadella said, noting how it will allow us "to reason over large amounts of data and convert that into intelligence." He likened AI to the arrival of books and the web and joked that we will soon create so much data that "we are getting to a point where we don't even know what to name things." "In this information explosion, what remains scarce is human intention and time -- our ability to make sense out of all of this information," he said. At Microsoft, this transformation is currently happening in a few different areas, with agents like Cortana, applications like SwiftKey and Office 365, and developer tools and platforms like the Cortana Intelligence Suite and the Azure cloud computing platform.

Now that you're done lusting over jet black iPhones, it's time to turn your attention toward Google. You know, the guys who make Android and Chrome. And what a bonanza the company reportedly has in store for Oct. 4. SEE ALSO: Google's smart speaker will be cheaper than the Amazon Echo, report says Traditionally, Google uses its I/O developer conference in the spring to talk developer-y things like frameworks, announce new versions of Android or skydive in to show off new moonshots like Google Glass. Then, in the fall, the company unveils new Nexus phones and sometimes new Nexus tablets, as well as Chromebooks either under its Pixel brand or from its hardware partners. At I/O this year, Google did announce a good mix of new hardware and software.

If getting up to dim or turn off the lights is a chore too far, you will soon be able to do it from the comfort of your seat. Flat-packed furniture giant Ikea has announced it is to release a range of smart light bulbs that can be controlled wirelessly using a remote control. The move promises to take smart lighting, which until now has largely confined to expensive high-end products, into the mainstream. A number of manufacturers already sell lighting systems that link to a central hub and the internet and can be controlled through a smartphone app. Apple is reportedly developing its own device similar to the Amazon Echo, which is said to have left the research and development lab and is now being tested as a prototype.

With the rise of online dating, it has never been easier for the younger generation to find love. But it seems the new technology is actually having the opposite effect. Those aged between 16 and 21 have become so absorbed in the internet they are having less sex than earlier generations, a major study has found. And one in three are abstaining from sex altogether. The findings reveal a different side to the rising popularity of dating apps and changing attitudes to sex.

Virtual reality may be generating most of the buzz today, but another major tech shift looms much closer on the horizon: machine learning. The technology has already made inroads with the public through platforms such as Amazon's Echo and Google's Deep Dream Generator. But its influence will extend beyond voice-controlled speakers and AI-enhanced art, effecting a sea of change for businesses of all sizes. It will be a few years before we witness machine learning's breakthrough moment, but it's coming -- and it will change everything. Humans could be incredibly effective given endless timelines, budget, and energy.

This fall, customers cruising the aisles of Lowe's home improvement stores in the San Francisco Bay Area may see a new type of employee taking inventory and assisting shoppers. You won't find a nametag on this worker, but you won't confuse it with other employees, either. The new kid in town is the LoweBot, an autonomous retail service robot that scans and audits store inventory on the floor. It uses voice recognition to identify products for customers and lead them to the right shelf -- in multiple languages. The retailer is deploying LoweBots at 11 of its Bay Area stores over a seven-month period using NAVii robots made by Fellow Robots, following a successful two-year pilot program of a first-generation robot called OSHbot that was tested at one of Lowe's Orchard Supply Hardware stores.

With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to address in this context include the sheer volume of data that may consist of categorical samples, the typically streaming format of acquisition, and the possibly missing entries. To cope with these challenges, the present paper develops a novel categorical subspace learning approach to unravel the latent structure for three prominent categorical (bilinear) models, namely, Probit, Tobit, and Logit. The deterministic Probit and Tobit models treat data as quantized values of an analog-valued process lying in a low-dimensional subspace, while the probabilistic Logit model relies on low dimensionality of the data log-likelihood ratios. Leveraging the low intrinsic dimensionality of the sought models, a rank regularized maximum-likelihood estimator is devised, which is then solved recursively via alternating majorization-minimization to sketch high-dimensional categorical data `on the fly.' The resultant procedure alternates between sketching the new incomplete datum and refining the latent subspace, leading to lightweight first-order algorithms with highly parallelizable tasks per iteration. As an extra degree of freedom, the quantization thresholds are also learned jointly along with the subspace to enhance the predictive power of the sought models. Performance of the subspace iterates is analyzed for both infinite and finite data streams, where for the former asymptotic convergence to the stationary point set of the batch estimator is established, while for the latter sublinear regret bounds are derived for the empirical cost. Simulated tests with both synthetic and real-world datasets corroborate the merits of the novel schemes for real-time movie recommendation and chess-game classification.