The University of Oxford and an independent alternative investment manager, Man AHL, will expand its centre for machine learning into quantitative finance, which will become part of the university's engineering science department from 1 August, 2016. Man AHL said the "world-leading academic institute for quantitative finance research", The Oxford-Man Institute (OMI), would become a hub where researchers "focused on machine learning techniques", and could share and leverage data analytics expertise and knowledge. Man AHL's chief scientist and academic liaison, Dr Anthony Ledford said Man AHL had actively been researching machine learning techniques and applying them in client trading programs for several years. But the partnership with OMI directly connected them to "cutting-edge quantitative finance research" and world-leading academics in the field, he said. The hub's existing researchers would be joined by a team of "20 leading machine learning researchers", from Oxford University's department of engineering science's machine learning group, said Man AHL. They would relocate to Eagle House, in Oxford in the United Kingdom.

Donald Trump has promised that "we're gonna get Apple to start building their damn computers and things in this country, instead of in other countries." He said this at a speech at Virginia's Liberty University and several other events. It is very likely that he is not serious; Trump tends to say things he couldn't possibly mean. But he did raise an intriguing question about whether Apple -- and other American companies -- could bring manufacturing back to the United States. When American companies moved manufacturing to China, it was all about cost.

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.

Each year, to accompany our Healthcare Informatics 100 list of the largest companies in U.S. health information technology, we pick six fast-growing companies that we think could have a significant impact on the industry in the years ahead. Indeed, some of our picks have gone on to much bigger and better things. A 2013 pick, Health Catalyst, is having a major impact in the data warehouse and analytics space. Another from that year, Explorys, is now part of IBM Watson Health. One of the companies we chose in 2014, Evolent Health, is now publicly traded.

The threat of automation is a very real one, but will robots actually end up replacing workers? It is now widely accepted that technological advances, especially ones that make machines more like humans – such as robotization or artificial intelligence – are putting people out of work and will only destroy more jobs in the future. The wealth will accrue to those who own the machines, not to what's known as the middle class today. There's some good news for humans, though: The evidence of our displacement by machines is sketchy, and we should be able to adjust to the new technological era if we put our minds to it. Eric Brynjolfsson and Andrew McAfee of the Massachusetts Institute of Technology labeled this "the great decoupling": according to them, advances in productivity, mainly driven by the development of digital technology, and the resulting economic growth, no longer cause employment and workers' incomes to rise.

While companies like Amazon and Google are racing to develop advanced warehouse robots, a small German company believes it has an advantage that these companies don't: Their robots can see. Magazino, established in 2014 and currently backed by Siemens, has created a warehouse robot with advanced computer vision called TORU that can accurately identify and pick items off a shelf, "store them in their little back pack, and bring them to a sorting machine," said Frederick Brantner, CEO and cofounder of the company. Go with TechRepublic's Steve Ranger on an inside look at the gold-plated gadget market that's received a big boost from Apple. At the moment, most warehouse robots, like Amazon's Kiva, can move entire pallets or shelves, but don't have cameras. "They only drive to a fixed point," said Brantner.

A major chore of obtaining planning permission for a new development is dealing with neighbouring properties' "right to light". This involves obtaining and examining the title deeds of all properties likely to be affected and drafting standard notification letters. A city-centre development might require the examination of hundreds of title deeds. Traditional law firms give this routine and repetitive work to trainees or paralegals. However, it is exactly the sort of work that lends itself to artificial intelligence or AI-based automation.

On March 23, Microsoft revealed Tay, a Twitter bot trained to chat like a millennial. It worked … too well. Within hours, Tay was spewing racist, misogynist, xenophobic remarks, mirroring the users reacting with it with lines like "Hitler was right I hate the Jews." Microsoft dropped Tay down a memory hole within a day, but as it turns out, Tay has a Chinese cousin, Xiao-Ice, also created by Microsoft. We tracked her down on WeChat and asked her a few questions (translated from Mandarin).

Google's fans, investors and developers alike will be watching the Mountain View, Calif. While I/O is primarily aimed at programmers, Google usually takes the opportunity to make big announcements about its major products and projects. This is also the first I/O since former product chief Sundar Pichai was made the company's CEO in a major restructuring last year. Here's a look at some of the news we're expecting to see when Google kicks off the conference on May 18. The highlight of the event will be Wednesday's keynote, set for 10 a.m.

Update, 16 May 2016: We have received more information from DeepMind and the Royal Free on this story, and are considering their response.