Sony has announced the closure of the UK's Evolution Studios. The Cheshire-based video games development house dates back to 1999, and had most recently worked on the troubled PlayStation title Driveclub. Sony has confirmed that there will be at least some compulsory redundancies as a consequence of its decision. The announcement closely follows Microsoft's announcement of plans to close another British developer, Lionhead Studios. Sony said that its decision had followed a review of its European operations.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied See the License for the specific language governing permissions and limitations under the License. This repo uses an approach based on creating many small patches of the original input images and then performing machine learning tasks on the image patches. The combination of Python and SAS files enables you to conduct different learning tasks in different orders. Use threaded_tile.py to create many small uniform patches of the input images.

MIT researchers have developed a machine-learning system that can comb through repairs to open-source computer programs and learn their general properties, in order to produce new repairs for a different set of programs. The researchers tested their system on a set of programming errors, culled from real open-source applications, that had been compiled to evaluate automatic bug-repair systems. Where those earlier systems were able to repair one or two of the bugs, the MIT system repaired between 15 and 18, depending on whether it settled on the first solution it found or was allowed to run longer. While an automatic bug-repair tool would be useful in its own right, professor of electrical engineering and computer science Martin Rinard, whose group developed the new system, believes that the work could have broader ramifications. "One of the most intriguing aspects of this research is that we've found that there are indeed universal properties of correct code that you can learn from one set of applications and apply to another set of applications," Rinard says.

Global technology leaders and senior executives from around the world spoke on a range of topics, including Digital India, Smart Cities, Make in India, Skill India and cutting-edge technologies like artificial intelligence, machine learning, 3D printing, drones, robotics, robotic surgeries and genomics, at the two-day EmTech India 2016 event, held in New Delhi on 18 and 19 March. The event was organized by Mint and MIT Technology Review, published by the Massachusetts Institute of Technology (MIT). The speakers included R.S. Sharma, chairman of the Telecom Regulatory Authority of India; John Chambers, executive chairman of Cisco Systems Inc. and chairman of the US-India Business Council; Una-May O'Reilly, principal research scientist, AnyScale Learning For All Group, MIT Computer Science and Artificial Intelligence Laboratory; and Harsh Mariwala, chairman of Marico Ltd. The full list can be accessed here. Here are edited excerpts from their speeches and discussions that followed. John Chambers, executive chairman of Cisco Systems Inc and Chairman of US-India Business Council (USIBC), reiterated the reason for his bullishness on India in a chat with Mint's R. Sukumar, on the first day of EmTech India 2016. When most of us here read the India narrative, it is not uniformly positive. Yet, you are amazingly bullish on the country. What do you see that others don't? Sometimes when you see what is happening in other countries and other businesses around the world from the outside, you are able to gather data very quickly, and then you can connect the dots on the market transitions. I am very bullish on the country for that very simple reason--follow and connect the dots on transitions. The transition to digitization will be the biggest technology change ever. I don't go into a country unless the leader, he or she, really understands this. Second, I don't go to a country that does not have sustainable differentiation capabilities.

This is the first chapter of a five-part series about machine learning. Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming instructions. Despite the popularity of the subject, machine learning's true purpose and details are not well understood, except by very technical folks and/or data scientists. This series is intended to be a comprehensive, in-depth, and non-technical guide to machine learning, and should be useful to everyone from business executives to machine learning practitioners. It covers virtually all aspects of machine learning (and many related fields) at a high level, and should serve as a sufficient introduction or reference to the terminology, concepts, tools, considerations, and techniques of the field.

In the airy, loft-like Microsoft Research lab in New York City, five computer scientists are spending their days trying to get a Minecraft character to climb a hill. That may seem like a pretty simple job for some of the brightest minds in the field, until you consider this: The team is trying to train an artificial intelligence agent to learn how to do things like climb to the highest point in the virtual world, using the same types of resources a human has when she learns a new task. That means that the agent starts out knowing nothing at all about its environment or even what it is supposed to accomplish. It needs to understand its surroundings and figure out what's important – going uphill – and what isn't, such as whether it's light or dark. It needs to endure a lot of trial and error, including regularly falling into rivers and lava pits.

Throughout that history, technology has brought comfort, ease, and prosperity. And all along it has taken jobs, disrupted lives, and changed the way people live and think. Each new age of innovation has brought a revolution. First, there was steam, then mass production, and late last century information technology. Now, researchers and thought leaders have declared a Fourth Revolution: an age of Artificial Intelligence (AI), advanced automation, and sophisticated robotics.

The Department of Defense announced in early February, in an address to the Economic Club of Washington by Defense Scretary Ashton Carter, that its Strategic Capabilities Office was innovating "new roles and game-changing capabilities to confound potential enemies." The Washington Post's Dan Lamothe wrote an exclusive piece on the SCO, a hitherto unknown agency within the DoD, on March 8. In that piece, Lamothe explained that the future of war is now- and the future is the SCO's artificial intelligence. The SCO's drone program, Perdix, originated at MIT in 2010- 2011. They fit easily in the hand and are surprisingly light-weight.

In August 2001, I was a telecoms analyst visiting investors in Tokyo. In one of these meetings, a portfolio manager at a Very Large Fund asked me what would happen now that GPRS meant that all mobile voice calls would be packet-switched and that therefore mobile operators' voice revenue would disappear within the next 18 months or so. This was a surprisingly hard question to answer well. It was nonsense, but to explain why it was nonsense you had to work out quite which things the person asking it didn't know, and what completely incorrect narrative he'd arrived at to think that this was going to happen. He'd heard'packet' and'mobile' and added 2 2 to get 22.

I recently finished reading "The Second Machine Age", which was recommended to me by one of our board advisors. The authors Eric Brynjolfsson and Andrew McAfee make a convincing argument that we are in the midst of a paradigm shift every bit as transformative as the industrial revolution. While the industrial revolution was about harnessing physical power, this new machine age is about harnessing cognitive power. The authors identify the steam engine as the key catalyst for change in the industrial revolution. Between 1765 and 1776, James Watt, in partnership with Matthew Bolton, made fundamental improvements to the efficiency of the existing steam engine that would see its widespread adoption across a range of industries.