AKITA – A northeastern city renowned for its cherry blossom is teaming up with a software developer to film the scene using drones, in a bid to boost tourism and assist in the upkeep of the trees. Senboku in Akita Prefecture, which boasts the cherry trees in its Kakunodate district, is one of the several areas nationwide designated as special zones where the use of drones for public purposes is allowed. This is intended to encourage economic revitalization. Infoteria Corp. will provide the technology and donate 1 million toward the care of the trees. Technicians flew a drone along a river bank in the city Wednesday, shooting vistas of early blossom.

A new center to study the implications of artificial intelligence and try to influence its ethical development has been established at the U.K.'s Cambridge University, the latest sign that concerns are rising about AI's impact on everything from loss of jobs to humanity's very existence. The Leverhulme Trust, a non-profit foundation that awards grants for academic research in the U.K., on Thursday announced a grant of 10 million ( 15 million) over ten years to the university to establish the Leverhulme Centre for the Future of Intelligence. The new facility will be directed by Professor Huw Price, the university's Bertrand Russell Professor of Philosophy. Others on the team include political scientists, lawyers, psychologists and technologists, said Prof. Gordon Marshall, the director of the Leverhulme Trust. The Trust sprang out of a company that now is part of Unilever.

Despite the hype, there is a lot of work to be done before we can build conversational software. These are some notes about what interesting conversational software would look like, and what techniques we'll need to build it. It may be obvious, but I feel we have to point out that the giddy excitement around bots stems from being happy that there is something new to build/invest in/write medium posts about, and not from exciting new technology. For VCs, new platforms mean new opportunities to bundle and unbundle services, and new battlegrounds for the big players (likely leading to acquisitions). So even without real technological breakthroughs, there is at least some money to be made investing in bot startups.

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.

Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings. This paradigm has had immeasurable practical success, but it is not the complete story for understanding and inferring the preferences of people. First, peoples' preferences and their observable manifestations as ratings evolve over time along general patterns of trajectories. Second, an individual person's preferences evolve over time through influence of their social connections. In this paper, we develop a unified process model for both types of dynamics within a state space approach, together with an efficient optimization scheme for estimation within that model. The model combines elements from recent developments in dynamic matrix factorization, opinion dynamics and social learning, and trust-based recommendation. The estimation builds upon recent advances in numerical nonlinear optimization. Empirical results on a large-scale data set from the Epinions website demonstrate consistent reduction in root mean squared error by consideration of the two types of dynamics.

It has already been cheerfully offering advice to customers hoping to buy a phone in Tokyo, but Pepper the'emotional' robot is now about to enrol in school. The expressive humanoid, which has been developed by Japanese corporation SoftBank Robotics, is designed to identify and react to human emotions. It is now due to attend classes at Shoshi High School in Waseda, in the Fukushima Prefecture of Japan – making it the first time a robot will'study' alongside human students. Pepper the robot has become the world's first humanoid to enroll into a high school. Pepper is intended to be used for customer service in banks, shops and for greeting people. However, SoftBank has said it – or he as they company seems to prefer – could become a companion in people's homes in the future too.

The robotics industry has been rapidly advancing and, with the creation of robots that are becoming more and more like humans, it doesn't show any signs of stopping. Now, researchers from China created a very realistic-looking robot called Jia Jia, the "robot goddess," which took three years to make. Jia Jia bears the appearance of a female adult and possesses many human facial features, such as blinking, as well as realistic facial expressions and hair that closely resembles human hair. Researchers created Jia Jia in an attempt to mimic a human in as many ways as possible. "Her" mouth is designed to correspond with the statements that she makes and her eyes casually scan her surroundings much like a real human.

Business documents written in foreign languages are no longer the problem they once were thanks to technologies like Google Translate, but what about contracts written in legalese? That's where LawGeex hopes to help with an AI-based online tool. LawGeex offers what it calls the world's first contract review platform based on artificial intelligence. The goal, it says, is to help businesses and individuals "get a fair deal" before signing an agreement. Toward that end, it combines machine-learning algorithms with crowdsourced data, text analytics, and the knowledge of expert lawyers to make in-depth contract reviews accessible to everyone.

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.

By 2030, India's population of people age 60 or older is projected to grow by 64 percent. Also by 2030, India's urban areas are expected to more than double their current population levels. Additionally, factors such as an increase in income levels, increases in health care insurance penetration, increases in private and public health care expenditure and rising consumer awareness will shape the future of the health care sector in India for the coming decades. Total spending on health care has increased at double-digit rates and accounted for 4 percent of GDP in 2013. However, government spending still remains low at 1.3 percent of GDP, making private expenditures as high as 2.7 percent of GDP in 2013.