Before the invention of the computer, most experimental psychologists thought the brain was an unknowable black box. You could analyze a subject's behavior--ring bell, dog salivates--but thoughts, memories, emotions? That stuff was obscure and inscrutable, beyond the reach of science. So these behaviorists, as they called themselves, confined their work to the study of stimulus and response, feedback and reinforcement, bells and saliva. They gave up trying to understand the inner workings of the mind. They ruled their field for four decades. Then, in the mid-1950s, a group of rebellious psychologists, linguists, information theorists, and early artificial-intelligence researchers came up with a different conception of the mind.

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.

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.

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

If the founders of a new face recognition app get their way, anonymity in public could soon be a thing of the past. FindFace, launched two months ago and currently taking Russia by storm, allows users to photograph people in a crowd and work out their identities, with 70% reliability. It works by comparing photographs to profile pictures on Vkontakte, a social network popular in Russia and the former Soviet Union, with more than 200 million accounts. In future, the designers imagine a world where people walking past you on the street could find your social network profile by sneaking a photograph of you, and shops, advertisers and the police could pick your face out of crowds and track you down via social networks. In the short time since the launch, Findface has amassed 500,000 users and processed nearly 3m searches, according to its founders, 26-year-old Artem Kukharenko, and 29-year-old Alexander Kabakov.

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.

For 30 years the games industry worked in a certain way. People rented offices and set up studios to create games; they employed staff to work in-house, then got those projects funded and distributed by publishers. If you wanted to opt out of that setup, you worked alone, or in a small team, as an indie developer – you operated in a totally separate stratosphere; the system neatly self-segregated. Meanwhile, in the background, the business worked to the seven-year cycles dictated by the lifespan of the major consoles. It was a machine of discreet components. But that machine is rusting and falling apart.