Everyone knows about the giant skills gap that is haunting the IT sector worldwide. According to IoT company PTC, it is estimated that in the next ten years more than two million IT and communication jobs will be unfulfilled. To address this, several universities have come up with degrees that address the different skills needed in the IoT market, including user interfaces, networks, artificial intelligence, networking, and others. CBR lists ten courses being taught in the UK institutions. Offering both a full time or part time (12 and 24 months respectively) course, University of London's Royal Holloway has built a degree based on computer science, technology and engineering.

Imagine a human being born on the cusp of a robotic revolution. At first, when the machines take over the economy, Bill is stoked. He wakes up late every morning, smokes some 22nd century weed and then watches TV. His degree in accounting has become worthless, because there is nothing he could do that couldn't be done better by a machine. Bill plugs his brain into a virtual world, becomes obsessed with killing dragons and winning digital gold, and dies shriveled and alone as a level 900 paladin.

China continues to accelerate its bid to grab the initiative in 21st-century robotics and artificial intelligence development as the next emerging global powerhouse. Small wonder that the world's leading manufacturing giant has already raced past the rest of the world as a major user of robots. According to the China Robot Industry Alliance, the country is already a flourishing hub for consumer robotics and is poised for a radical transition from its human-based workforce to an automated Artificial Intelligence or AI-based alternative. Its recent unveiling of the incredibly adept and efficient personal robot BIG-i is a pertinent example. Dubbed "butler," this humanoid is primarily a service robot with the programmed ability to aid homeowners in the performance of a wide variety of household errands.

The firm says it will grow its team to more than 100 researchers and engineers by the end of the year, and has already moved several staff from its new Autonomous Driving Unit (ADU) to Sunnyvale and hired a Tesla Autopilot software engineer. Liang Heng, a PhD graduate in Electrical Engineering from Stanford, joined Tesla last year after CEO Elon Musk requested hardcore software engineers join the firm's efforts to make its Autopilot system fully autonomous. He has left Tesla after just 5 months to join Baidu's team as a software architect. Before joining Tesla, Heng worked at Google on Street View. The person in charge of Baidu's ADU, Jing Wang, also just happens to be a former Google employee.

A slowdown in tech start-up funding has at least one big beneficiary: Walt Disney Co.'s video games division. The entertainment giant is having an easier time finding partners with whom it can develop mobile games, one of its top executives said last week. Why? Up-and-coming companies are losing access to the cash needed to launch games on their own. "As the venture money has dried up and exits have slowed down and valuations have come down, larger game developers that have one or two hits [but not a big stable of them] are now open to work with us in co-development," said Chris Heatherly, senior vice president and general manager at Disney Mobile Games. The comments came during a discussion last week at the L.A. Games Conference with Michael Metzger of investment bank Houlihan Lokey.

Tech giants are active players at the cognitive technologies scene. Alphabet and Facebook proved themselves active acquirers and investors in cognitive tech. Supercomputers, robots and drones are among themes approached by Internet giants. Microsoft, however, seems to be following a differed path: it integrates cognitive technologies into traditional products and makes them smarter. Bill Gates coined the concept of'digital nervous system (DNS)... that ... [provides] a well-integrated flow of information to the right part of the organization at the right time'.

"Chappie," the highest-grossing movie in America last weekend, is, to put it mildly, not a great film; the critics have given it a twenty-nine on Rotten Tomatoes, and it is nowhere near as original as "District 9," an earlier effort by the director, Neill Blomkamp. "Chappie" does not have the philosophical depth of "The Matrix" or the remade "Battlestar Galactica" series. Nor does it have the visual panache of "Interstellar" or "2001." From its opening scene, the film comes across as little more than a warmed-over "RoboCop" remake, relocated to Johannesburg. There's an evil company man, droids that menace the population, and a whole lot of blood, shooting, and broken glass.

As artificial intelligence grows increasingly sophisticated, it also grows increasingly alien.Deep learning algorithms and other A.I. technologies are creating systems capable of solving problems in ways that humans might never consider. But it's important that such systems understand humans as well, lest they inadvertently harm their creators. Accordingly, some researchers have argued that we need to help A.I. grasp human values--and, perhaps, the value of humans--from the start, making our needs a central part of their own development. To better understand some of the thinking around these issues, I spoke with Stuart Russell, a professor of computer science at the University of California, Berkeley. Russell has been involved with A.I. research for decades and is the co-author of one of the field's standard textbooks.

Where exactly is all the water on Earth's surface? Stand-alone satellite images have their limitations, but using artificial intelligence to examine them can now glean precise levels of water around the world and how they are changing week by week. Palo Alto startup Orbital Insight uses freely available images taken by the U.S. Geological Survey's Landsat 7 and 8 satellites, much like the images you see on Google Maps. The startup feeds the images into a neural network, which pinpoints the exact location and area of surface water. While it's not a new feat to track the Earth's water levels, Orbital Insight is approaching the task with a specially trained neural network that labels water pixel by pixel.

We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms. This penalty term is an effective regularizer for RNNs including LSTMs and IRNNs, improving performance on character-level language modeling and phoneme recognition, and outperforming weight noise and dropout. We achieve competitive performance (18.6\% PER) on the TIMIT phoneme recognition task for RNNs evaluated without beam search or an RNN transducer. With this penalty term, IRNN can achieve similar performance to LSTM on language modeling, although adding the penalty term to the LSTM results in superior performance. Our penalty term also prevents the exponential growth of IRNN's activations outside of their training horizon, allowing them to generalize to much longer sequences.