A drone takes a practice flight in Virginia with medical supplies -- part of a project to evaluate the flying machines for use in humanitarian crises. A drone takes a practice flight in Virginia with medical supplies -- part of a project to evaluate the flying machines for use in humanitarian crises. She was a mother in rural Ghana. She only wanted four children. That's a story that Faustina Fynn-Nyame told at the Women Deliver conference this week in Copenhagen, Denmark.

ARM has acquired Apical, a U.K. designer of embedded computer vision technology, and plans to incorporate that technology into future ARM microprocessor and system-on-chip designs, it said Wednesday. The move will open up new opportunities for designers of autonomous vehicles and security systems, among other connected things, according to ARM CEO Simon Segars. Computer vision is in its early stages, and Apical is at the forefront of embedding such technology, he said. Apical's technologies is already used in 1.5 billion smartphones, according to ARM, although many of those phones may be using nothing more sophisticated than a display brightness control Apical calls Assertive Display. That technology also turned up in Samsung Electronics' new laptop, the ATIV Book 9. Assertive Camera is another of Apical's developments: It's a range of software packages and silicon-based image signal processors for reducing image noise, managing color and shooting high dynamic range images.

US-based conglomerate GE has signed an agreement with the Indian Institute of Technology, Madras (IIT Madras), to set up an Industrial Internet Centre of Excellence. The Centre is being designed to develop applications that will help companies save costs. The first of these will be the Digital Twin of an aluminium smelter. According to senior company officials, GE would invest around Rs 3 crore in the first six months and could commit around Rs 30 crore over five years depending upon the outcome. Aluminium smelters are refineries for extracting the metal from aluminium oxide, separating it from oxygen through a chemical reaction.

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1) opacity as intentional corporate or state secrecy, (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented), and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These are just some examples of mechanisms of classification that the personal and trace data we generate is subject to every day in network-connected, advanced capitalist societies. These mechanisms of classification all frequently rely on computational algorithms, and lately on machine learning algorithms to do this work. Opacity seems to be at the very heart of new concerns about'algorithms' among legal scholars and social scientists. The algorithms in question operate on data. Using this data as input, they produce an output; specifically, a classification (i.e. They are opaque in the sense that if one is a recipient of the output of the algorithm (the classification decision), rarely does one have any concrete sense of how or why a particular classification has been arrived at from inputs.

Industrial revolutions are momentous events. By most reckonings, there have been only three. The first was triggered in the 1700s by the commercial steam engine and the mechanical loom. The harnessing of electricity and mass production sparked the second, around the start of the 20th century. The computer set the third in motion after World War II (see "The Man Who Made the Computer Age Possible," by Jeffrey E. Garten). It might seem too soon to proclaim that the fourth industrial revolution, spurred by interconnected digital technology, has begun. But Henning Kagermann, the head of the German National Academy of Science and Engineering (Acatech), did exactly that in 2011, when he used the term Industrie 4.0 to describe a proposed government-sponsored industrial initiative. When you look closely at the rapid pace of digitization in industry today, the name doesn't seem hyperbolic at all. It is a signal of sweeping change that is rapidly transforming many companies and may catch others by surprise.

Two years ago, a lawyer in Indiana sent me a check for seventy-eight thousand dollars. The money was from my uncle Walt, who had died six months earlier. I hadn't been expecting any money from Walt, still less counting on it. So I thought I should earmark my inheritance for something special, to honor Walt's memory. It happened that my longtime girlfriend, a native Californian, had promised to join me on a big vacation. She'd been feeling grateful to me for understanding why she had to return full time to Santa Cruz and look after her mother, who was ninety-four and losing her short-term memory. She'd said to me, impulsively, "I will take a trip with you anywhere in the world you've always wanted to go." To this I'd replied, for reasons I'm at a loss to reconstruct, "Antarctica?" Her eyes widened in a way that I should have paid closer attention to. But a promise was a promise. Hoping to make Antarctica more palatable to my temperate Californian, I decided to spend Walt's money on the most deluxe of bookings--a three-week Lindblad National Geographic expedition to Antarctica, South Georgia island, and the Falklands. I paid a deposit, and the Californian and I proceeded to joke, uneasily, when the topic arose, about the nasty cold weather and the heaving South Polar seas to which she'd consented to subject herself. I kept reassuring her that as soon as she saw a penguin she'd be happy she'd made the trip. But when it came time to pay the balance, she asked if we might postpone by a year. Her mother's situation was unstable, and she was loath to put herself so irretrievably far from home. By this point, I, too, had developed a vague aversion to the trip, an inability to recall why I'd proposed Antarctica in the first place. The idea of "seeing it before it melts" was dismal and self-cancelling: why not just wait for it to melt and cross itself off the list of travel destinations? I was also put off by the seventh continent's status as a trophy, too remote and expensive for the common tourist to set foot on. It was true that there were extraordinary birds to be seen, not just penguins but oddities like the snowy sheathbill and the world's southernmost-breeding songbird, the South Georgia pipit. But the number of Antarctic species is fairly small, and I'd already reconciled myself to never seeing every bird species in the world. The best reason I could think of for going to Antarctica was that it was absolutely not the kind of thing the Californian and I did; we'd learned that our ideal getaway lasts three days.

This article, co-authored by Harvey Lewis, research director at Deloitte UK, unveils the possibilities presented by artificial intelligence. In the 2015 film, "Ex Machina", the character Nathan Bateman, an archetypal eccentric billionaire, suggests that "one day the AIs are going to look back on us the same way we look at fossil skeletons on the plains of Africa. An upright ape living in dust with crude language and tools, all set for extinction." Given the surge of interest in artificial intelligence (AI) in recent years, fueled by big data and ever more sophisticated algorithms and hardware, it should come as no surprise that famous entrepreneurs and even eminent scientists in the real world are asking whether computers could one day threaten the survival of humankind. Governments and businesses around the world are continuing to invest billions of pounds in the technology.

In a world of smart machines that can drive cars, beat humans at chess, advise on medical diagnoses and perform a host of other tasks, imagine the next likely step as a smart machine as a people manager. As business investment in smart machines grows, "robobosses" will increasingly make workplace decisions that previously could only have been made by human managers. By 2018, more than three million workers globally will be supervised by robobosses, according to Frances Karamouzis, vice president and distinguished analyst at Gartner. These smart machine managers will look at data derived from worker performance in new ways because of their ability to dispassionately discover previously undetected relationships and correlations, test their hypotheses, and then elevate them to production status. "Supervisor duties are increasingly shifting toward monitoring worker accomplishments through measurements of performance that are directly tied to output and customer evaluation," explained Ms. Karamouzis.

It's probably years away from being used in West Virginia, but computer-based artificial intelligence appears to be helping law enforcement officers find wildlife poachers. Researchers are combining AI with something called "game theory" to plan patrols based on the topography, game movements and past instances of illegal activity in a given area. "In most parks, ranger patrols are poorly planned, reactive rather than pro-active, and habitual," said Fei Fang, a Ph.D. candidate in the computer science department at the University of Southern California. In other words, the horse is usually out of the barn by the time the law arrives -- and if the law does happen to arrive before law breakers do their thing, officers execute their patrols so predictably the bad guys often are able to avoid them. The National Science Foundation and the Army Research Office are supporting research into using AI to put patrols on the ground before poachers have a chance to poach.

Conversations about basic income, a government-funded salary given to every citizen, used to take place in the dingy offices of extremist left-wing politicians, or in the campus dorm rooms of idealistic students determined to fix the problems of the previous generation. The conversation was about social responsibility. It wasn't an economic case, it was a moral one. A sharp uptake in technology designed to automate jobs and replace human workers is bringing new voices to this old debate. Today's society could be disastrously affected by artificial intelligence and growing automation, and scientists and technologists are looking for ways to stop that damage before it happens.