During Build 2016 this year, Microsoft pitched a world of connected devices and sensors all speaking through cloud-backed machine learning models to developers. Microsoft is betting on a future that involves developers utilizing sensors that link to a vast pool of intelligent data to return immediate real-world benefits. Practicing what the company preaches, Microsoft's Solution Architect and Technical Manager of Microsoft Research, Kenji Takeda talks about how Cortana Intelligence suite is being used to secure safe drinking water for thousands of villages in rural Africa and Asia. A team consisting of Dr. Robert Hope of the REACH initiative, and machine learning experts David Clifton, an associate professor, and graduate student Farah Colchester have come together to try and secure safe and healthy water sources for 5 million poor people in Asia and Africa. The REACH program utilizes a series of mobile sensors connected to cloud computing to monitor water wells to secure a safe supply of water in more rural areas.

IMAGE: Researchers collect information for the design of PAWS in a protected area for a trial patrol. A century ago, more than 60,000 tigers roamed the wild. Today, the worldwide estimate has dwindled to around 3,200. Poaching is one of the main drivers of this precipitous drop. Whether killed for skins, medicine or trophy hunting, humans have pushed tigers to near-extinction.

REMEMBER the basic income grant South African labour unions, churches and NGOs campaigned for back in the 1990s and early "noughties", but on which Trevor Manuel's Treasury frowned on as a fiscal nonstarter? Silicon Valley A-lister Sam Altman thinks the US will have to adopt something like it within the next generation or two -- and he's not alone. Altman is founder and president of Y Combinator, the seed-stage tech investor that helped launch Airbnb and Dropbox. As co-chairman of OpenAI, he is working with Elon Musk to see that artificial intelligence, as it approaches and perhaps surpasses the human variety, benefits mankind. He believes that while technology will generate vast new wealth, it will in the process destroy much traditional employment without replacing it.

American drones fired more ammunition last year than manned warplanes for the first time, according to new data analyzed by Reuters. The news comes three years after U.S. President Barack Obama said that a drawdown of U.S. military forces after 2014 would "reduce the need for unmanned strikes." The data shows just how much American forces have come to rely on the unmanned vehicles to carry out missions in the Middle East and abroad, even while human rights organizations and some foreign governments have raised concerns over what they call an unnecessary amount of civilian casualties. "In recent months it's definitely flowed more," Lieutenant Colonel Michael Navicky, who commands the Air Force's 62nd Expeditionary Reconnaissance Squadron, said. "We've seen increased weapons deployment in the past few months, and the demand is insatiable."

In 2025, in accordance with Moore's Law, we'll see an acceleration in the rate of change as we move closer to a world of true abundance. Here are eight areas where we'll see extraordinary transformation in the next decade: In 2025, 1,000 should buy you a computer able to calculate at 10 16 cycles per second (10,000 trillion cycles per second), the equivalent processing speed of the human brain. The Internet of Everything describes the networked connections between devices, people, processes and data. By 2025, the IoE will exceed 100 billion connected devices, each with a dozen or more sensors collecting data. This will lead to a trillion-sensor economy driving a data revolution beyond our imagination. Cisco's recent report estimates the IoE will generate 19 trillion of newly created value. With a trillion sensors gathering data everywhere (autonomous cars, satellite systems, drones, wearables, cameras), you'll be able to know anything you want, anytime, anywhere, and query that data for answers and insights. SpaceX, Google (Project Loon), Qualcomm and Virgin (OneWeb) are planning to provide global connectivity to every human on Earth at speeds exceeding one megabit per second. We will grow from three to eight billion connected humans, adding five billion new consumers into the global economy. They represent tens of trillions of new dollars flowing into the global economy. And they are not coming online like we did 20 years ago with a 9600 modem on AOL. Existing healthcare institutions will be crushed as new business models with better and more efficient care emerge. Thousands of startups, as well as today's data giants (Google, Apple, Microsoft, SAP, IBM, etc.) will all enter this lucrative 3.8 trillion healthcare industry with new business models that dematerialize, demonetize and democratize today's bureaucratic and inefficient system. Biometric sensing (wearables) and AI will make each of us the CEOs of our own health. Large-scale genomic sequencing and machine learning will allow us to understand the root cause of cancer, heart disease and neurodegenerative disease and what to do about it. Robotic surgeons can carry out an autonomous surgical procedure perfectly (every time) for pennies on the dollar. Each of us will be able to regrow a heart, liver, lung or kidney when we need it, instead of waiting for the donor to die. Billions of dollars invested by Facebook (Oculus), Google (Magic Leap), Microsoft (Hololens), Sony, Qualcomm, HTC and others will lead to a new generation of displays and user interfaces.

This post contains my answers from a Quora session I did on machine learning and artificial intelligence. Each section contains a link to the original Quora question, the overall session can be found here. Think carefully about what you actually want to achieve with it. Most fall into the latter camp, but it seems everyone fancies themselves as containing a bit of the former (particularly if they think they're going to solve AI). To do the former well, in the international community, requires really good foundations (particularly in mathematics) followed by a PhD with a supervisor who has experience of how that community works. Doing the second well is much easier from the perspective of learning machine learning. A data generator would often be a scientist or company that is working in a particular application and wants answers. They need access to machine learning researchers or statisticians to give advice on how to answer those questions. They should try and collaborate with experts in data analytics and data science, but they should be careful, there is a lot of hype around the term'big data' at the moment. It's a difficult area to navigate. Data generators typically need an interface to consume machine learning (or statistics) effectively, if this interface is poorly chosen a lot of wasted resource can result (things get very expensive very quickly for a lot of data generators!). A data consumer is where the largest demand is right at the moment, and should probably be the starting point for someone who wants to move in the right direction. An MSc in Data Science would be a good starting point. You can also use this experience to see if you want to transit into a machine learning generator (that's basically what happened to me). What are you passionate about? That is the route in to any subject. Is it a particular approach to learning or a particular application?

This paper investigates the problem of cross-lingual transfer parsing, aiming at inducing dependency parsers for low-resource languages while using only training data from a resource-rich language (e.g., English). Existing model transfer approaches typically don't include lexical features, which are not transferable across languages. In this paper, we bridge the lexical feature gap by using distributed feature representations and their composition. We provide two algorithms for inducing cross-lingual distributed representations of words, which map vocabularies from two different languages into a common vector space. Consequently, both lexical features and non-lexical features can be used in our model for cross-lingual transfer. Furthermore, our framework is flexible enough to incorporate additional useful features such as cross-lingual word clusters. Our combined contributions achieve an average relative error reduction of 10.9% in labeled attachment score as compared with the delexicalized parser, trained on English universal treebank and transferred to three other languages. It also significantly outperforms state-of-the-art delexicalized models augmented with projected cluster features on identical data. Finally, we demonstrate that our models can be further boosted with minimal supervision (e.g., 100 annotated sentences) from target languages, which is of great significance for practical usage.

As a reminder, all participants' lines will be in the listen-only mode. There will be an opportunity for you to ask questions after the presentation concludes. I would now like to hand the conference over to Mr. Aravind Viswanathan. Thank you and over to you, sir. We will begin the call with business highlights and overview by Abid, the Chief Executive Officer and Member of the Board, followed by the financial overview by our CFO, Jatin Dalal. Afterwards, the operator will open the bridge for Q&A with our management team. Before Abid starts, let me draw your attention to the fact that during this call, we may make certain forward-looking statements within the meaning of Private Securities Litigation Reform Act 1995. These statements are based on management's current expectations and are associated with uncertainties and risks, which may cause the actual results to differ materially from those expected. The uncertainties and risk factors are being explained in our detailed filings with the SEC. Wipro does not undertake any obligation to update the forward-looking statements to reflect events and circumstances after the date of filing thereof. The conference call will be archived and the transcript will be available on our website. Ladies and gentlemen, let me now hand it over to Mr. Abid. Today is the first opportunity for me to interact with all of you since I've taken over as the Chief Executive Officer of Wipro, and it's a special moment for me. While I will speak about the performance of our full quarter and the full fiscal year, I thought I will take this opportunity to begin by speaking about our ambition, our strategy and how we are going to execute this strategy. Since I got announced within two days, I was able to define and announce my structure and I had already preselected my leadership team which I announced on 6th of January, effective February 1. Over the past 80 days after I have taken over as CEO, I've had the opportunity to go around the globe and meet about 70 of our top 100 clients. And both with my leadership team and with the customers, I've had the opportunity to validate the strategy that we have been working on and this gives me a high level of confidence on the relevance of our overall strategy. Our ambition is to double our revenues to 15 billion by fiscal 2020 with a 23% operating margin.

Omoju Miller, a self-described futurist (someone who studies the future's possibilities), enjoys picturing tomorrow. As a Nigerian woman who settled in the Bay Area, she's already torn down historical barriers to work as a software engineer in Silicon Valley, a white man's world. But in envisioning a new society, Miller isn't thinking only of contemporary struggles; she's pondering what humanity will need next. Take one of her projects: Hiphopathy, where she's using machine learning to parse rappers' metaphorical language, in the hopes of teaching a computer to think conceptually, developing, in the process, a form of artificial intelligence. Recently, NationSwell spoke with Miller about true visionaries that inspire her and the lessons we can all take away from their avant-garde thinking.

This time last year Steve Wozniak was sounding a cautionary note about the future of Artificial Intelligence (AI), warning that computers would one day take over from humans and joking that we might even end up as their pets. In a recent interview with Australia's ABC TV's Lateline the engineering genius appeared more sanguine about the future of self-aware, super-intelligent Artificial Intelligence and much more concerned with the real world killer robots that are all but with us: Lethal Autonomous Weapon Systems (LAWS). The Apple co-founder maintains that human-level Artificial Intelligence won't happen for "a very long time": It might take 200 years before they are really fully able to operate all of their needs in the world, until then they're going to need human beings … I'm not really worried at all. It's very scary to make autonomous weapons that are just following some programmed set of instructions … even when you're driving a car there is no one set of rules … if a lane is closed off you have to do something against the rules … I don't think it's a good idea at all. I don't think we can really stop it.