When Elon Musk isn't outlining plans to use his massive rocket to leave a decaying Planet Earth and colonize Mars, he sometimes talks about his belief that Earth isn't even real and we probably live in a computer simulation. "There's a billion to one chance we're living in base reality," he said at a conference in June. Musk is just one of the people in Silicon Valley to take a keen interest in the "simulation hypothesis", which argues that what we experience as reality is actually a giant computer simulation created by a more sophisticated intelligence. If it sounds a lot like The Matrix, that's because it is. According to this week's New Yorker profile of Y Combinator venture capitalist Sam Altman, there are two tech billionaires secretly engaging scientists to work on breaking us out of the simulation.

Sitting in a studio in Kensington, London, the designer Sebastian Conran walks me through a worst-case scenario. "Basically, what it's looking for is a break in routine," he explains, pointing to a drawing of an elderly woman, collapsed on the floor of her home. The e-sensor in the room notices that you've fallen over. MiRo is a robotic dog. There is an early model close to where we are sitting.

DeepMind will not build an unstoppable AI on its own. Instead, both companies will release a series of programming tools on early 2017 that will allow researchers and hobbyist around the world build and train their bots to play Starcraft II. Google's DeepMind researcher Orion Vinyals made the announcement during BlizzCon 2016 at Anaheim, California. Vinyals was the top-ranked SC2 player in Spain's leaderboards before becoming a top scientist in the British AI startup. Vinyals believes the results of the investigation could translate to the real life.

Vyacheslav is a PhD candidate at the Oxford Internet Institute. His research uses social psychology and machine learning to understand networks of people and networks of ideas. Imagine a typical day in 2020: Your personal AI assistant wakes you up with a friendly greeting before preparing your favorite breakfast. During your morning workout, it plays new songs that perfectly match your musical tastes. For your driverless commute to work, it has pre-selected a few articles based on the duration of your commute and what you've read in the past.

Facebook has been forced to end a hugely controversial data sharing agreement with WhatsApp. The decision would have seen WhatsApp hand out information on all of its users to Facebook, letting the latter use data about people's chats to inform its advertising. It would also have gone the other way – allowing companies to send WhatsApp's to people based on things they've bought on Facebook, for instance. But now the UK's Information Commissioner's Office has told the company that it needs to bring that arrangement to an end because it does not have "valid consent" from its users. Facebook had looked to gain permission from its users to have their data used as part of the deal.

Lip reading is a tricky business. Test results vary, but on average, most people recognize just one in 10 words when watching someone's lips, and the accuracy of self-proclaimed experts tends to vary -- there are certainly no lip-reading savants. Now, though, some researchers claim that AI techniques like deep learning could help solve this problem. After all, AI methods that focus on crunching large amounts of data to find common patterns have helped improve audio speech recognition to near-human levels of accuracy, so why can't the same be done for lip reading? The researchers from the University of Oxford's AI lab have made a promising -- if crucially limited -- contribution to the field, creating a new lip-reading program using deep learning.

It seems to me that the hype about AI makes really difficult for experienced investors to understand where the real value and innovation are. I would like then to humbly try to bring some clarity to what is happening on the investment side of the artificial intelligence industry. We have seen as in the past the development of AI has been stopped by the absence of funding, and thus studying the current investment market is crucial to identify where AI is going. First of all, it should be clear that investing in AI is extremely cumbersome: the level of technical complexity goes out of the pure commercial scope, and not all the venture capitalists are able to fully comprehend the functional details of machine learning. This is why the figures of the "Advisors" and "Scientist-in-Residence" are becoming extremely important nowadays.

Alan Turing Institute lends support to'Commission on Artificial Intelligence' published by the Science and Technology Committee. The establishment of an AI ethics board in the UK has taken a big step forward, with the Alan Turing Institute agreeing to work with the UK government to explore the ethics questions surrounding the development of artificial development. In a letter to the Science and Technology Committee, the Alan Turing Institute welcomed the Committee's recent Report on Robotics and Artificial Intelligence and put itself forward as an institution prepared to take a leading role in taking AI forward. The letter to Committee chair Stephen Metcalfe was in response to a report published by the Committee on 12 October 2016, in which it recommended that a'standing Commission on Artificial Intelligence' be established at the Alan Turing Institute to examine the social, ethical and legal implications of recent and potential developments in AI. "Your Report recommends that a standing Commission on Artificial Intelligence be Should this recommendation be taken forward, we would very much welcome the opportunity to lead the creation of the Commission." MP Stephen Metcalfe returned support in kind, saying in response to the Institute's letter: "We welcome the Alan Turing Institute's support for our report on Robotics and Artificial Intelligence and are pleased that, as the UK's new data science research institute, it is ready to lead the standing Commission on Artificial Intelligence that we recommended establishing" Debate surrounding the ethics concerned with AI has been gathering speed in recent times, with Melanie Mitchell recently telling CBR that the AI community is'not very well prepared' when it comes to the ethical issues that come with using AI in life-critical areas.

The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators. Performing statistically convergent scans of high-dimensional BSM theories is consequently challenging, and in practice unfeasible for very high-dimensional BSM theories. We present here a new machine learning method that accelerates the interpretation of LHC data, by learning the relationship between BSM theory parameters and data. As a proof-of-concept, we demonstrate that this technique accurately predicts natural SUSY signal events in two signal regions at the High Luminosity LHC, up to four orders of magnitude faster than standard techniques. The new approach makes it possible to rapidly and accurately reconstruct the theory parameters of complex BSM theories, should an excess in the data be discovered at the LHC.

We introduce a doubly stochastic proximal gradient algorithm for optimizing a finite average of smooth convex functions, whose gradients depend on numerically expensive expectations. Our main motivation is the acceleration of the optimization of the regularized Cox partial-likelihood (the core model used in survival analysis), but our algorithm can be used in different settings as well. The proposed algorithm is doubly stochastic in the sense that gradient steps are done using stochastic gradient descent (SGD) with variance reduction, where the inner expectations are approximated by a Monte-Carlo Markov-Chain (MCMC) algorithm. We derive conditions on the MCMC number of iterations guaranteeing convergence, and obtain a linear rate of convergence under strong convexity and a sublinear rate without this assumption. We illustrate the fact that our algorithm improves the state-of-the-art solver for regularized Cox partial-likelihood on several datasets from survival analysis.