Facebook disclosed last month it had found ads linked to fake accounts -- likely run from Russia -- that sought to influence the U.S. election. Facebook said the ads focused on divisive political issues such as immigration and gun rights in an apparent attempt to sow discord among the U.S. population.

Despite recent attempts to tease the robotics projects incubating at its Google X skunkworks, industry observers say that Google has done more to stifle than advance innovation in robotics. On December 4th, 2013, John Markoff, a technology reporter for The New York Times, broke the story that Google had acquired seven robotic companies and that Andy Rubin, of Android fame, would be heading the group. Schaft, a Japanese start-up developing a humanoid robot; Industrial Perception, a Silicon Valley start-up that developed a computer vision system for loading and unloading trucks; Meka Robotics, a robot developer for academia; Redwood Robotics, a start-up intended to compete with the Baxter robot (and others) entering the small and medium-sized shop and factory marketplace; Bot & Dolly, a maker of robotic camera systems used for special effects such as in the movie "Gravity;" Autofuss, a design and marketing firm and a partner in Bot & Dolly; and Holomni, a maker of powered caster modules for omnidirectional vehicles. On December 14th, 2013, Markoff followed up with the news that Google had added to its new stable of robotic companies by acquiring Boston Dynamics, a 20-year old developer of mobile and off-road robotics and human simulation technology mostly for DARPA and the Department of Defense. Thus some of the leading startups in the industry and the whole 80 talent pool from Boston Dynamics became part of Google. "The company's initial market will be in manufacturing, e.g., electronics assembly which is mostly done by hand.

Artificial intelligence: everybody is talking about it, and the as-of-yet unrealized possibilities of the technology are fueling a renaissance in the hardware and software industry. Hardware and software companies -- including Intel, NVidia, Google, IBM, Microsoft, Facebook, Qualcomm, ARM and many others -- are racing to build the next AI hardware platform or fighting to maintain their lead. AI, and deep learning (a sub-field of neural networks) in particular is an inherently non-Von Neumann process, and the prospect of having a processor more closely tailored to the specific needs of neural networks is appealing. But, I like to think before acting, especially before diving into a potentially very expensive hardware project. Should the AI industry build a specialized deep learning chip, and, if so, what should it look like?

Two days after K.S. Sunil Kumar received a promotion, Human Resources phoned him up and asked him to resign. This happened in April, just as Kumar was beginning his ninth year at Tech Mahindra, one of the giants in India's IT services industry. He worked in engineering services, where he designed components and tools for aerospace firms in North America and Europe. They'd send over specs--the materials available to construct a hinge, and the kind of load it had to bear, and the cost at which it had to be manufactured--and he mocked up options with the help of software. He was a foot soldier in the army of Indian engineers to whom work is outsourced from the West, so that it can be finished at a fraction of the expense. Sometimes he left his base, Tech Mahindra's Bangalore campus, to serve stints at clients' offices abroad: in Montreal, Belfast, or Stockholm.

Are we doing enough to get ready for the emergence of artificial intelligence as a major driving force for transformation in our economy? Forget the apocalyptic concerns of people like Elon Musk and Stephen Hawking for a moment and consider the very real and practical ways AI is already changing the way we do things, from chatbots for customer service through to driverless vehicles in industries like mining and agriculture. This stuff is already here and it's only going to accelerate as we start to understand and apply AI, machine learning and automation technologies across our industries. A new report from the UK government which looks into growing the AI industry is taking its potential as an economic driver very seriously. Are we doing the same here in Australia?

In the 1970s and the 1980s, the tech scene witnessed the so-called "AI winter". It was a period that came after a phase of hype that was followed by disappointments that resulted in the loss of interest in AI and the cutting of the funds. Although AI today impacts our lives way more than it used to do back then, there are striking similarities between that period and today. AI is there to stay, but we might hit a stagnation point soon. It is difficult to pin down when exactly AI was born.

Probabilistic methods for classifying text form a rich tradition in machine learning and natural language processing. For many important problems, however, class prediction is uninteresting because the class is known, and instead the focus shifts to estimating latent quantities related to the text, such as affect or ideology. We focus on one such problem of interest, estimating the ideological positions of 55 Irish legislators in the 1991 D\'ail confidence vote. To solve the D\'ail scaling problem and others like it, we develop a text modeling framework that allows actors to take latent positions on a "gray" spectrum between "black" and "white" polar opposites. We are able to validate results from this model by measuring the influences exhibited by individual words, and we are able to quantify the uncertainty in the scaling estimates by using a sentence-level block bootstrap. Applying our method to the D\'ail debate, we are able to scale the legislators between extreme pro-government and pro-opposition in a way that reveals nuances in their speeches not captured by their votes or party affiliations.

In late August, an astronaut on board the International Space Station remotely operated a humanoid robot to inspect and repair a solar farm on Mars--or at least a simulated Mars environment, which in this case is a room with rust-colored floors, walls, and curtains at the German Aerospace Center, or DLR, in Oberpfaffenhofen, near Munich. European Space Agency astronaut Paolo Nespoli commanded the humanoid, called Rollin' Justin, as the robot performed a series of navigation, maintenance, and repair tasks. Instead of relying on direct teleoperation, Nespoli used a tablet computer to issue high-level commands to the robot. In one task, he used the tablet to position the robot and have it take pictures from different angles. Another command instructed Justin to grasp a cable and connect it to a data port.

In a Silicon Valley startup, Fetch Robotics, about 50 employees, and 125 robots work cohesively. According to CEO Melonee Wise, the human employees see the robots as pets, even calling them "pups". This is becoming the new normal, with many companies employing robots to supplement their workforce as opposed to completely replacing it. Despite the fear that robots are predicted to take over most jobs, the future might, in fact, be more about working alongside robots rather than robots running the workforce completely. At Fetch Robotics, Wise states that "no one has ever lost a job because of our robots."

Governments around the world are beginning to understand the potential and importance of artificial intelligence (AI). International acceptance has spread with the implementation of AI regulations, like what has been created in Germany. For others, like the U.S., incorporating AI begins with recommendations on how to move forward with the tech. In the United Arab Emirates (UAE), however, the government took AI adoption one step further by installing a minister in-charge of AI. The UAE's first State Minister for Artificial Intelligence is 27-year-old Omar Bin Sultan Al Olama.