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.

About a month into Japan's professional baseball season, the Fukuoka SoftBank Hawks, the 2014 and 2015 national champions, are doing no worse. Many say the team's strong lineup is underpinned by the cash-rich SoftBank Group, a big telecom and technology group. The reality is quite the reverse. Unlike their rivals, the Hawks are a stand-alone club, though one with the financial leeway to allocate profits to areas where the front office sees fit, such as player development and information technology. With the help of its tech-savvy parent, the club may be about to rewrite "Moneyball," the 2003 bestseller about how a Major League Baseball team in the U.S. used statistical analysis to beat high-spending opponents.

The last few years have seen an explosion of interest in Machine Learning (ML) technology and potential applications. Machine Learning is the unsung hero that powers many applications, systems, sensors, devices, and products. Today, Machine Learning is so pervasive that we can often assume its presence in most of the applications and systems without having to specifically call it out. In simple terms, machine learning is a computer's ability to learn from data, and it is one of the most useful tools we have to develop intelligent systems and applications. Machine learning is used widely today for all kinds of tasks, from churn prediction in large companies, to web search, to medical diagnostics, to robotics.

In my last post on the subject of AI, values,and socially beneficial simulation, I looked at one way of approaching the problem of AI values, society, and institutions through simulation and modeling. It occurred to me while writing it that there was also another way that might be interesting to think about, and it stems from a rejected piece I wrote last year on the subject of the computer as a "moral mirror." Though that piece was rejected for a reason (it wasn't that well-written), it has come into my mind again over the last week as the Internet has collectively reflected on my Slate article about AI values. I have some commonalities and differences with Stuart Russell's view of AI value alignment. I've looked at them here, here, here, and here.

Bossa Nova Robotics, a company specializing in building robotic technology for retailers, has raised 14 million to expedite the rollout of its robots in stores. Founded out of Pittsburgh, with offices in San Francisco as well, Bossa Nova has been developing its robot technology over the past few years, setting out to serve retailers through automation and analytics. Its machines analyze stock on shelves and collect data to optimize inventory, with fully autonomous robots unleashed in stores among shoppers. The company says it is now testing its "retail robots" with "five of the world's leading retail chains," though it wouldn't divulge any names. What we're effectively talking about here is automating a task that would ordinarily be carried out by humans, with a view toward improving efficiency and productivity and cutting costs.

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.

Distributional (or distribution-valued) data are a new type of data arising from several sources and are considered as realizations of distributional variables. A new set of fuzzy c-means algorithms for data described by distributional variables is proposed. The algorithms use the $L2$ Wasserstein distance between distributions as dissimilarity measures. Beside the extension of the fuzzy c-means algorithm for distributional data, and considering a decomposition of the squared $L2$ Wasserstein distance, we propose a set of algorithms using different automatic way to compute the weights associated with the variables as well as with their components, globally or cluster-wise. The relevance weights are computed in the clustering process introducing product-to-one constraints. The relevance weights induce adaptive distances expressing the importance of each variable or of each component in the clustering process, acting also as a variable selection method in clustering. We have tested the proposed algorithms on artificial and real-world data. Results confirm that the proposed methods are able to better take into account the cluster structure of the data with respect to the standard fuzzy c-means, with non-adaptive distances.

A "robot revolution" will transform the global economy over the next 20 years, cutting the costs of doing business but exacerbating social inequality, as machines take over everything from caring for the elderly to flipping burgers, according to a new study. As well as robots performing manual jobs, such as hoovering the living room or assembling machine parts, the development of artificial intelligence means computers are increasingly able to "think", performing analytical tasks once seen as requiring human judgment. In a 300-page report, revealed exclusively to the Guardian, analysts from investment bank Bank of America Merrill Lynch draw on the latest research to outline the impact of what they regard as a fourth industrial revolution, after steam, mass production and electronics. "We are facing a paradigm shift which will change the way we live and work," the authors say. "The pace of disruptive technological innovation has gone from linear to parabolic in recent years. Penetration of robots and artificial intelligence has hit every industry sector, and has become an integral part of our daily lives."

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning. Its victory is a stunning achievement and another milestone in the inexorable march of AI research.

Although the game between AlphaGo AI and legendary chess master Lee Sedol ended over a month ago, the event's influence is opening doors for companies seeking to explain the significance of AI technology. Fu Sheng, CEO Cheetah Mobile who predicted Lee would win the game, is one of them. At the company's global media conference held Tuesday, Fu announced that Cheetah Mobile, the Beijing-based startup best known for its utility apps, is going to open a robotics division with a 50 million USD investment designated for artificial intelligence development. Fu says he is going to lead the team himself. "AI technology is maturing and there's no doubt that human labor is going to be replaced by robots", he said, adding that deep learning and AI will offer a chance to bypass competitors and disrupt the industry landscape.