From Google to Microsoft to Apple and possibly Samsung, AI is almost the buzzword in the smartphone space as well. But as these systems get more powerful and begin to do more, there is a need to make sure that they are not used by authoritarian regimes and then target certain populations. At the ongoing South by South West (SXSW) event in Texas, USA, Microsoft Research's Kate Crawford explained her point on view on the AI scene and how it makes for an ideal excuse to teach these humongous data systems the wrong ideas without any accountability. The Guardian reported the researcher's take on artificial intelligence, hinting at the rise of ultra-nationalism, right-wing authoritarianism and fascism, thanks to the widespread use of these systems. However, it is not the use of these systems, but the way in which human biases are encoded into them that leads to their misuse when they fall into the wrong hands.

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Around 1,500 people visit an industrial waste treatment facility in central Japan each year to see up close how the operator can recycle more than 99 percent of the solid garbage it receives from a variety of manufacturers and municipalities. Nakadai Co., which covers the Kanto region, accepts 60 tons of waste each day, which it recycles and resells to about 50 customers. The waste includes wooden materials, plastics, cardboard boxes, personal computers, auto parts and fluorescent lamps. Most industrial waste treatment companies specialize in handling a single type of waste for disposal. But Nakadai, founded as a scrap iron processor in Tokyo in 1937, has tried to diversify its sources of income by obtaining most of the nearly 20 types of licenses required for waste disposal since the late 1990s.

An award-winning brewpub built with recycled materials as part of a "zero waste" mission. There is new life in the mountains of Tokushima Prefecture, in the neighboring towns of Kamiyama and Kamikatsu, even as depopulation afflicts most rural areas with rot. In Kamiyama, young people work remotely for tech companies or as artists in cooperative spaces. In Kamikatsu, the elderly test drones as part of their work harvesting leaves and flowers for use as garnishes in restaurants as far away as Europe. Prime Minister Shinzo Abe has stressed the need to revitalize rural areas as the country struggles with demographic decline.

Talespin is a young Delhi-based startup that wants to place chatbots inside stores. The idea, according to Talespin's product head Tanay Dixit, is to help bring some of the benefits of the online shopping experience - such as personalised recommendations - offline as well, at a low cost. The idea was actually born when Dixit tried to go shopping for a t-shirt at Shopper's Stop, and after being shown a lot of different ones, he finally found something he liked, only to learn it wasn't available in his size. He realised that if there was a system to browse through the store's entire catalogue like you would an e-commerce marketplace, then he would have been able to get a better idea more quickly, and it could even have allowed him to place an order for the out- of-stock item, to be delivered to him. And so that's exactly what he set out to build.

Pride has quickly given way to consternation in some parts of Israel's tech community, following yesterday's news that Intel will buy hometown hero Mobileye for $15.3 billion. In short, there accusations that Mobileye sold out to Silicon Valley. Instead, he argues in a blog post that the Intel purchase will increase global attention on Israel's machine learning sector and, more importantly, inspire new entrepreneurs:

Intel has announced that it plans to acquire the autonomous car hardware firm Mobileye for a cool $15 billion. It's a weighty sum, but the purchase gives Intel cutting-edge machine-learning technology that lies at the center of the nascent autonomous car industry. The buyout of Mobileye, one of our 50 Smartest Companies of 2016, is the second largest ever made by Intel. It's also by far the largest autonomous car acquisition to date, and another sign that the self-driving vehicle sector looks set to explode in the coming years. Many car manufacturers already use Mobileye's hardware to power their autonomous systems, giving the company a position of power throughout the industry.

The self-driving car space is getting increasingly more cutthroat. The sheer number of lawsuits filed recently are a testament to that. Tesla, for example, is suing its former Autopilot director Sterling Anderson. The lawsuit claims Anderson stole data for a competing venture, Aurora Innovations, that hasn't even come out of stealth mode yet. "In their zeal to play catch-up, traditional automakers have created a get-rich-quick environment. Small teams of programmers with little more than demoware have been bought for as much as a billion dollars. Cruise Automation, a 40-person firm, was purchased by General Motors in July 2016 for nearly $1 billion. In August 2016, Uber acquired Otto, another self-driving startup that had been founded only seven months earlier, in a deal worth more than $680 million."

This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical predictive variance, which is a lower bound on the mean square-error (MSE). This approach, however, does not take into account that the statistical model is learned from the data. We show that this omission leads to a systematic underestimation of the prediction errors. Starting from a generalization of the Cram\'er-Rao bound, we derive a more accurate MSE bound which provides a measure of uncertainty for prediction of Gaussian processes. The improved bound is easily computed and we illustrate it using synthetic and real data examples. of uncertainty for prediction of Gaussian processes and illustrate it using synthetic and real data examples.

Network analysis has been a very active area of research with applications to social sciences, biology and marketing, to name a few. A fundamental problem in network data analysis is community detection, or clustering: Given a collection of nodes and a similarity matrix among them, interpreted as the adjacency matrix of a (weighted) network, one wants to partition the nodes into clusters, or communities, of high similarity. For undirected networks, a popular model for community-structured networks is the stochastic block model (SBM) [1] and its variants [2, 3], which have been extensively investigated in recent years both in terms of theoretical properties and efficient fitting algorithms. See, for instance [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18] for a sample of the work. On the other hand, a natural structure is often present in many real networks, that of being bipartite, where nodes are divided into two sets, or sides, and only connections between nodes of different sides are allowed.