AMD's recent chips haven't rocked Intel's PC market dominance, but new chips based on the company's Zen architecture aim to change that next year. On Wednesday it shared initial details about the new FX and seventh-generation A-series chips, which are the brainchild of Jim Keller, a leading mobile chip designer at Apple until AMD hired him in 2012. The new AMD chips will battle Intel's highly anticipated Skylake line, which is designed to bring new wireless charging and data transfer features to laptops. Building on the launch of the fourth generation of its enterprise software suite earlier this year, SAP unveiled the cloud version of S/4Hana at its Sapphire conference on Wednesday. SAP expects most companies to opt for hybrid scenarios combining the on-premises version of the in-memory database platform with the new cloud version.

It turns out that a vital missing ingredient in the long-sought after goal of getting machines to think like humans--artificial intelligence--has been lots and lots of data. Last week, at the O'Reilly Strata Hadoop World Conference in New York, Salesforce.com's head of artificial intelligence, Beau Cronin, asserted that AI has gotten a shot in the arm from the big data movement. "Deep learning on its own, done in academia, doesn't have the [same] impact as when it is brought into Google, scaled and built into a new product," Cronin said. In the week since Cronin's talk, we saw a whole slew of companies--startups mostly--come out of stealth mode to offer new ways of analyzing big data, using machine learning, natural language recognition and other AI techniques that those researchers have been developing for decades. One such startup, Cognitive Scale, applies IBM Watson-like learning capabilities to draw insights from vast amount of what it calls "dark data," buried either in the Web--Yelp reviews, online photos, discussion forums--or on the company network, such as employee and payroll files, noted KM World.

Researchers at the University of Amsterdam are using neural networks to help a statistical machine translation systems learn what all human translators know--that the best translation of a word often depends on the context. Such tools are increasingly important as individuals and businesses seek to access information or buy products and services from other countries where different languages are spoken. Statistical machine translation work by breaking sentences into phrase fragments and selecting the most likely translation for each fragment--a process that doesn't always yield the best translation for the sentence as a whole in morphologically rich languages such as those where nouns are inflected for number, case and gender. To improve the word selection of such systems when translating into morphologically rich languages such as Russian, Bulgarian and German, the team used a neural network to analyze the words in context in the source language. Translating sentences into grammatically more complex languages is relatively easy for human translators because they understand the grammatical function of the word in a sentence.

Fresh off a nearly perfect run predicting the outcome of World Cup futbol matches, Microsoft's digital assistant, Cortana, has focused her abilities on picking the winners of NFL football games. Need help predicting whether or not the Seahawks will hold off the Green Bay Packers? The kicker, of course, is that you'll need access to Cortana, Microsoft's digital assistant. And for right now, that means a Windows Phone 8.1 phone, either one that's been upgraded via Microsoft's developer program or as part of the "Cyan" rollouts that Microsoft and its carriers are pushing to vanilla Windows Phone 8 Lumia phones. For one, asking Cortana about the outcome of a particular game is a bit like a magic spell: You'll need to ask Cortana "Who will win Team A or Team B?" and she will provide an answer.

For example, NASA and General Motors built the 300 pound Robonaut2 - or R2 - a robot that is capable of using the same tools as humans and now works alongside them in space onboard the International Space Station. R2 can use its hands to do work beyond the scope of prior humanoid machines and can easily work safely alongside people, a necessity both on Earth and in space, NASA stated. It is also stronger: able to lift, not just hold, a 20-pound weight (about four times heavier than what other dexterous robots can handle) both near and away from its body. Granted the robot takes up valuable space station space, but it doesn't have to be fed or go to the bathroom - major advantages in space. Other robots such as the Octoroach being developed by UC Berkeley researchers can crawl into all manner of super-secret surveillance or emergency recovery applications that the human body just could not.

While debate on military robots heated up this month thanks to UN talks about the development of lethal autonomous robots--and military robots are evolving quickly thanks to defense budgets--household robots remain far from ubiquitous. More than a half-century after the world's first industrial robot, Unimate, began work at a General Motors plant, most commercial robots still work in factories. The ones that are in households, such as the roughly 10 million robot vacuum cleaners led by iRobot's Roomba, have usually been limited to performing one task only, like sucking up dirt. Computers and robots can beat us at dedicated tasks like chess or painting cars, though humans still have a massive intelligence advantage in terms of general knowledge. That's a good thing if you fear a robot uprising.

For decades, scientists have fantasized about creating robots with brain-like intelligence. This year, researchers tempted by that dream made great progress on achieving what has been called the holy grail of computing. Today, a wide variety of efforts are aimed at creating intelligent computers that can progressively learn and make smarter decisions. Millions of dollars this year were poured in efforts to create "silicon brains," or neuromorphic chips that mimic brain-like functionality to make computers smarter. The new chips could give eyes and ears to smart robots, which will be able to drive, identify objects, or even point out rotten fruit.

When Christian Johnson began his summer 2012 internship at the information management branch of NASA's Langley Research Center in Hampton, Virginia, he little suspected that he'd soon be virtually tooling around the center via a vaguely humanoid robot on wheels. Once classes began in the fall, the 18-year-old had to finish up his senior year of high school in Buffalo, New York and needed to telecommute to continue his work as data analytics specialist at the research center. One of his co-workers had heard about a company called VGo Communications that makes a wheeled personal avatar, or what it calls a "productivity improvement solution," that lets people see and hear--and be seen and be heard--from far away. The co-worker wrote a proposal urging Langley's CIO to buy a VGo unit, and the CIO's office approved the purchase of one of the robotic avatars so that Johnson could use it to move virtually through the building and attend meetings--just one of the new ways robots are making their mark in business today. Industrial robots have been around since the early 1960s and have been used mainly in automotive plants.

Welcome to the Intelligent Assistive Technology and Systems Lab (IATSL), located in the Department of Occupational Science and Occupational Therapy at the University of Toronto. We are a multi-disciplinary group of researchers with backgrounds in engineering, computer science, occupational therapy, speech-language pathology, and gerontology. Our goal is to develop zero-effort technologies that are adaptive, flexible, and intelligent, to enable users to participate fully in their daily lives. We have an opening for an enthusiastic post-doctoral fellow to work on computer vision, signal processing, and video analysis algorithms for applications in sleep monitoring and medical diagnosis. Please read more about this position here at http://www.cs.toronto.edu/

Just over half-a-century ago, the brilliant mathematician Alan Turing, fresh from his secretive triumphs at Bletchley Park and the breaking of the Enigma Code, wrote a now famous essay speculating on the possibility of machine intelligence. Turing imagined what he called an'imitation game'. A judge communicating by some kind of remote message system with two players would have to guess which of them was human. In the now familiar version of the imitation game, one player would be human, the other a machine. A machine would be intelligent if it could con the judge.