Artificial intelligence sounds mysterious to many people, but it has been a part of our life, an expert said at the Third World Internet Conference in Wuzhen, East China's Zhejiang province, on Wednesday. The voice recognition function for mobile internet search used by Baidu, for instance, is an application of artificial intelligence, said Sun Ninghui, director of the Institute of Computing Technology, Chinese Academy of Sciences. The tech company's "Baidu Doctor", which can simulate dialogues between a patient and a doctor, and read large amount of literature and the patient' records, is another example of how artificial intelligence is applied, he added. Besides, artificial intelligence is also used in image recognition and teaching, according to Sun. More applications such as autonomous vehicles would soon enter our life, Sun said, referring to Baidu's driverless vehicle which recently underwent road tests.

Infosys has made an investment of R14.5 crore in a Denmark-based artificial intelligence start-up, Unsilo. Founded in 2012, this Danish company is focused on solutions in the area of advanced text analysis. Infosys has earmarked $500 million for its innovation fund which invests in start-ups across the globe. Of this, $250 million has been set aside for start-ups in India. "We will partner with Unsilo to bring their artificial intelligence and machine learning technology to our global clients. They join and expanding portfolio of innovative young companies from around the world that Infosys works with to help enterprises drive their digital transformation," said Ritika Suri, executive vice president & global head of corporate development & ventures at Infosys.

At the inaugural O'Reilly AI conference, 66 artificial intelligence practitioners and researchers from 39 organizations presented the current state-of-AI: From chatbots and deep learning to self-driving cars and emotion recognition to automating jobs and obstacles to AI progress to saving lives and new business opportunities. There is no better place to imbibe the most up-to-date tech zeitgeist than at an O'Reilly Media event as has been proven again and again ever since the company put together the first Web-related meeting (WWW Wizards Workshop in July 1993). The conference was organized by Ben Lorica and Roger Chen, with Peter Norvig and Tim O'Reilly acting as honorary program chairs. Here's a summary of what I heard there, embellished with a few references to recent AI news and commentary: In contrast to traditional software, explained Peter Norvig, Director of Research at Google, "what is produced [by machine learning] is not code but more or less a black box--you can peak in a little bit, we have some idea of what's going on, but not a complete idea." Tim O'Reilly recently wrote in "The great question of the 21st century: Whose black box do you trust?": Because many of the algorithms that shape our society are black boxes… because they are, in the world of deep learning, inscrutable even to their creators – [the] question of trust is key. Understanding how to evaluate algorithms without knowing the exact rules they follow is a key discipline in today's world.

This is the text version of a talk I gave on November 11, 2016, at the Direction conference in Sydney. When John Allsopp invited me here, I told him how excited I was discuss a topic that's been heavy on my mind: accountability in automated systems. But then John explained that in order for the economics to work, and for it to make sense to fly me to Australia, there needed to actually be an audience. Let's start with the most obvious answer--the military. This is the Predator, the forerunner of today's aerial drones. Those things under its wing are Hellfire missiles. These two weapons are the chocolate and peanut butter of robot warfare. In 2001, CIA agents got tired of looking at Osama Bin Laden through the camera of a surveillance drone, and figured out they could strap some missiles to the thing. And now we can't build these things fast enough. We're now several generations in to this technology, and soldiers now have smaller, portable UAVs they can throw like a paper airplane. You launch them in the field, and they buzz around and give you a safe way to do reconaissance. There are also portable UAVs with explosives in their nose, so you can fire them out of a tube and then direct them against a target--a group of soldiers, an orphanage, or a bunker–and make them perform a kamikaze attack. The Army has been developing unmanned vehicles that work on land, little tanks that roll around with a gun on top, with a wire attached for control, like the cheap remote-controlled toys you used to get at Christmas. Here you see a demo of a valiant robot dragging a wounded soldier to safety. The Russians have their own versions of these things, of course. I imagine it asking you who you are in a heavy Slavic accent before firing its many weapons into your fleeing body. Not all these robots are intended as weapons. The Army is trying to automate transportation, sometimes in weird-looking ways like this robotic dog monster.

Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.

We introduce an Embarrassingly Parallel Search (EPS) method for solving constraint problems in parallel, and we show that this method matches or even outperforms state-of-the-art algorithms on a number of problems using various computing infrastructures. EPS is a simple method in which a master decomposes the problem into many disjoint subproblems which are then solved independently by workers. Our approach has three advantages: it is an efficient method; it involves almost no communication or synchronization between workers; and its implementation is made easy because the master and the workers rely on an underlying constraint solver, but does not require to modify it. This paper describes the method, and its applications to various constraint problems (satisfaction, enumeration, optimization). We show that our method can be adapted to different underlying solvers (Gecode, Choco2, OR-tools) on different computing infrastructures (multi-core, data centers, cloud computing). The experiments cover unsatisfiable, enumeration and optimization problems, but do not cover first solution search because it makes the results hard to analyze. The same variability can be observed for optimization problems, but at a lesser extent because the optimality proof is required. EPS offers good average performance, and matches or outperforms other available parallel implementations of Gecode as well as some solvers portfolios. Moreover, we perform an in-depth analysis of the various factors that make this approach efficient as well as the anomalies that can occur. Last, we show that the decomposition is a key component for efficiency and load balancing.

SYNTHETIC BIOLOGY: China Used Crispr to Fight Cancer in a Real, Live Human Megan Molteni Wired "The FDA, for better and for worse, is a historically cautious gatekeeper, unconcerned with international spitting contests. Clinical trials cost millions, and last for years. But George Church, Harvard University geneticist and co-founder of Editas Medicine believes it's a necessary step to ensure new technologies like Crispr-based gene therapies really work. Even when they hold you up from making history." VIRTUAL REALITY: I Hung out With My Past Self in Virtual Reality Ben Popper The Verge "I met the crew from AltspaceVR inside a virtual space station. We chatted briefly, moving around the room...Then I moved off to the side, and watched as my avatar reappeared at our starting location...I watched as beta-Ben repeated the last five minutes of my life."

Japan Creates Hyperrealistic Dinobots, There May Soon Be A'Jurassic' Theme Park'Jurassic Park' could well be a reality soon, judging from the intricate and hyperrealistic dinosaur robots created by Japan's ON-ART Corp. The company recently unveiled its latest project with man-controlled robotic replicas of raptors, an allosaurus and a tyrannosaurus rex. The life-sized dinosaurs were modeled from skeletons of dinosaurs and created from carbon fiber materials. With the grand reveal, CEO of ON-ART Corp. Kazuya Kanemaru shares that he wants to create a'DINO-A-PARK', similar to the Steven Spielberg's'Jurassic Park', a franchise that remains wildly popular. See the robotic dinosaurs in action below.

A product from Japan created quite the stir at Consumer Electronics Show in Las Vegas and CEATEC JAPAN in Tokyo this year. The "harmony" of clothing analysis, artificial intelligence (AI), and robotics blend together to produce a "fully automatic clothes folding machine." Japan technological alliance "seven dreamers laboratories' is the developer. The product details have been released in various places, so I won't get into that, but as the name says, "It's a robot that folds clothes. No further explanation is needed." The company announced a partnership with Panasonic (TSE:6752) and Daiwa House (TSE:1925) last year, and together established the joint venture Seven Dreamers Laundroid with plans to begin sales by reservation for their first machine "Laundroid 1" in March of 2017. The developer, Seven Dreamers, announced on November 14th the securement of 6 billion yen (around $60 million US) in funds from SBI Investment, in addition to Panasonic and Daiwa House. The shareholding ratios and payment date remain undisclosed. The concept began in 2005, and with the realization of "folding" from 2013, Laundroid was born. I heard from Seven Dreamers CEO Shin Sakane about the road it took to get here. I came today with the idea of asking straight out, "What happened to make robots fold the laundry?" Well, to be straight, "It's now possible to recognize clothes using artificial intelligence," is maybe the simplest answer I can give. Let's go through the process. How did the idea first come to you? Before that, first permit me to talk a little about what criteria the Seven Dreamers esteem. For us, there are three criterion for "Things that have not been realized yet but could change our lives, and also enrich them." The technological hurdles are high and our policy is to clear them. You've made something that sets high hurdles. Since first coming up with the idea, I was thinking about different markets to satisfy all the criteria. Looking around we see many products targeted at men. Starting now and into the future, 'women', 'the elderly', and'children' are the keywords that will become important. After thinking, the idea that maybe the answer lies within the home came to me and, while I don't usually talk with my wife about work, I casually mentioned it to her. What do you wish you had? She came back just as fast, "Of course, it has to be a machine that folds the laundry.

Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at Cloud Expo, Ed Featherston, a director and senior enterprise architect at Collaborative Consulting, discussed the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine. Speaker Bio Ed Featherston is a director/senior enterprise architect at Collaborative Consulting. He brings 35 years of technology experience in designing, building, and implementing large complex solutions. He has significant expertise in systems integration, Internet/intranet, and cloud technologies, Ed has delivered projects in various industries, including financial services, pharmacy, government and retail.