A company owned by Google has been given access to the healthcare data of up to 1.6 million patients from three hospitals run by a major London NHS trust. DeepMind, the tech giant's London-based company most famous for its innovative use of artificial intelligence, is being provided with the patient information as part of an agreement with the Royal Free NHS trust, which runs the Barnet, Chase Farm and Royal Free hospitals. It includes information about people who are HIV-positive as well as details of drug overdoses, abortions and patient data from the past five years, according to a report by the New Scientist. DeepMind announced in February that it was developing a software in partnership with NHS hospitals to alert staff to patients at risk of deterioration and death through kidney failure. The technology, which is run through a smartphone app, has the support of Lord Darzi, a surgeon and former health minister who is director of the Institute of Global Health Innovation at Imperial College London.

Think of the gear you can't live without: The smartphone you constantly check. The camera that goes with you on every vacation. The TV that serves as a portal to binge-watching and -gaming. Each owes its influence to one model that changed the course of technology for good. Some of these, like Sony's Walkman, were the first of their kind. Others, such as the iPod, propelled an existing idea into the mainstream. Some were unsuccessful commercially, but influential nonetheless. And a few represent exciting but unproven new concepts (looking at you Oculus Rift). Rather than rank technologies--writing, electricity, and so on--we chose to rank gadgets, the devices by with consumers let the future creep into their present. The list--which is ordered by influence--was assembled and deliberated on at (extreme) length by TIME's technology and business editors, writers and reporters.

A Silicon Valley chip designer has launched a USB stick with a supercomputer onboard. Movidius, based in San Mateo, California, has essentially put a deep learning chip inside a USB drive. Deep learning involves "training" a computational model so it can decipher natural language. The "Fathom Neural Compute Stick" has been designed to connect to existing systems (running Linux) and increase the performance of deep learning tasks by 20-30 times. Movidius chips are also used to help drones to avoid obstacles and thermal cameras to spot people in a fire.

There is a lot of excitement about artificial intelligence (AI) and how to create computers capable of intelligent behavior. After years of steady but slow progress on making computers "smarter" at everyday tasks, a series of breakthroughs in the research community and industry have recently spurred momentum and investment in the development of this field. Today's AI is confined to narrow, specific tasks, and isn't anything like the general, adaptable intelligence that humans exhibit. Despite this, AI's influence on the world is growing. The rate of progress we have seen will have broad implications for fields ranging from healthcare to image- and voice-recognition.

When you study a painting, chances are that you can make several inferences about it. In addition to understanding the subject matter, for example, you may be able to classify it by period, style, and artist. Could a computer algorithm "understand" a painting well enough to perform these classification tasks as easily as a human being? We also addressed two other intriguing questions about the capabilities and limitations of AI algorithms: whether they can identify which paintings have had the greatest influence on later artists, and whether they can measure a painting's creativity using only its visual features. We wanted to develop algorithms capable of classifying large groups of paintings by style (for example, as Cubist, Impressionist, Abstract Expressionist, or Baroque), genre (for example, landscape, portrait, or still life), and artist. One requirement for this classification is the ability to recognize color, composition, texture, perspective, subject matter, and other visual features.

Google may have DeepMind, but Baidu, China's homegrown Google, has Deep Speech. Deep Speech, which debuted in December 2015, is a speech recognition system that uses an artificial neural network to translate audio input directly to transcribed output. By contrast, most speech recognition systems, including Siri, use multiple, engineer-crafted steps to make translations. The system has learned how to recognize and transcribe both English and Mandarin, and according to a Baidu paper released in February 2016, it has a recognition rate that is more accurate than most native Mandarin speakers. Baidu announced earlier in April that it will begin rolling out the deep speech technology in collaboration with Peel, a smart remote app that will be available in both English and Mandarin for Android, followed by iOS.

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In his first letter to company shareholders, Sundar Pichai, Google CEO, said that devices will become a thing of the past, and that computing will be driven by artificial intelligence. He said that the next wave is all about machine learning. The letter outlines how the search giant plans to win with artificial intelligence. Now that Pichai is running the Mountain View Internet firm, the founders are letting him write their letter, too. Re/code noted that while the letter does not really contain anything mind-blowing or new, it reveals areas of focus for the famously unfocused tech firm.

Is the probability of winning the Imitation Game independent of time, culture and social class? Arguably, as we in the West approach a time of more fluid definitions of gender, that original Imitation Game would be more difficult to win. In the 21st century, our communications are increasingly with machines (whether we like it or not). Texting and messaging have dramatically changed the form and expectations of our communications. For example, abbreviations, misspellings and dropped words are now almost the norm.

Neuroimaging research has predominantly drawn conclusions based on classical statistics, including null-hypothesis testing, t-tests, and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity, including cross-validation, pattern classification, and sparsity-inducing regression. These two methodological families used for neuroimaging data analysis can be viewed as two extremes of a continuum. Yet, they originated from different historical contexts, build on different theories, rest on different assumptions, evaluate different outcome metrics, and permit different conclusions. This paper portrays commonalities and differences between classical statistics and statistical learning with their relation to neuroimaging research. The conceptual implications are illustrated in three common analysis scenarios. It is thus tried to resolve possible confusion between classical hypothesis testing and data-guided model estimation by discussing their ramifications for the neuroimaging access to neurobiology.