Markov Chain Monte Carlo methods have revolutionised mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamiltonian dynamics have been proposed as an efficient way of building chains which can explore probability densities efficiently. The method emerges from physics and geometry and these links have been extensively studied by a series of authors through the last thirty years. However, there is currently a gap between the intuitions and knowledge of users of the methodology and our deep understanding of these theoretical foundations. The aim of this review is to provide a comprehensive introduction to the geometric tools used in Hamiltonian Monte Carlo at a level accessible to statisticians, machine learners and other users of the methodology with only a basic understanding of Monte Carlo methods. This will be complemented with some discussion of the most recent advances in the field which we believe will become increasingly relevant to applied scientists.

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Get ready for a bizarre future where your couch speaks to you anytime you land your ass on it – courtesy of none other than your favorite ready-to-assemble furniture retailer, IKEA. The company's Denmark-bound innovation lab Space10 is currently surveying people about what they'd like to see in a hypothetical virtual assistant, which means the Swedish manufacturer could be contemplating embedding its furniture with AI tech in the future. We've teamed up with Product Hunt to offer you the chance to win an all expense paid trip to TNW Conference 2017! Titled Do You Speak Human?, the survey seeks to establish what customers would like to see in a potential virtual assistant for smart furniture. For the most part, the questions circle around things like what preference you have for the assistant's gender (with male, female and gender-neutral as options) and whether you'd like your AI-powered helper to be more humanlike or robotic.

Remember the famous scene in Stanley Kubrick's 1968 2001: A Space Odyssey, when Hal 9000--the intelligent-turned-malevolent computer--regresses to his "childhood" and sings "Daisy Bell" as he's decommissioned by astronaut Dave Bowman? Its inspiration was a real-life Bell Labs demonstration of speech synthesis on an IBM 704 mainframe in 1961, witnessed by Arthur C Clarke, who later incorporated it into his 2001 novel and screenplay. Although Bell Labs' involvement in the field stretches back to the 1930s with Homer Dudley's keyboard-and-footpedal-driven Voder speech synthesis device, it's undoubtedly the classic Kubrick/Clarke movie that cemented the ideas of artificial intelligence (AI) and conversing with computers into the public mind. Depending on how old you are, we're now familiar with computerised voices, thanks to devices like Texas Instruments' popular 1978 Speak & Spell educational toy, Stephen Hawking's speech synthesiser (memorably sampled in the Pink Floyd song Keep Talking), GPS navigational systems in your car, and any number of public information and call handling systems. More recently, the combination of automatic speech recognition (ASR), natural-language understanding (NLU) and text-to-speech (TTS) has come to mainstream attention in virtual assistants such as Apple's Siri, Google Now, Microsoft's Cortana, and Amazon's Alexa. Download this article as a PDF (free registration required). To get a handle on how speech technologies work, we clearly need to know something about the mechanics of human speech and the structure of language. When we speak, air from the lungs passes through the vocal tract to produce "voiced" or "unvoiced" sounds (depending on whether the vocal cords are vibrating or not) that may then be modulated by the tongue, teeth and lips.

When: Wed, May 10, 7:00pm – 8:30pm, 2017 The talk will provide a basic introduction to artificial intelligence focusing on how biology contributes to the field. In particular, it will dive into the field of artificial neural networks, discussing some of the basics and then moving onto new insights from biology and how it can help us build more intelligent machines. Speaker Tomás Maul is Head of the School of Computer Science at University of Nottingham Malaysia. The talk will provide a basic introduction to artificial intelligence focusing on how biology contributes to the field. In particular, it will dive into the field of artificial neural networks, discussing some of the basics and then moving onto new insights from biology and how it can help us build more intelligent machines.

"The connectivity that is the heart of globalisation can be exploited by states with hostile intent to further their aims.[…] The risks at stake are profound and represent a fundamental threat to our sovereignty." "It's not MI6's job to warn of internal threats. It was a very strange speech. Was it one branch of the intelligence services sending a shot across the bows of another? Or was it pointed at Theresa May's government? Does she know something she's not telling us?" Senior intelligence analyst, April 2017 In June 2013, a young American postgraduate called Sophie was passing through London when she called up the boss of a firm where she'd previously interned. The company, SCL Elections, went on to be bought by Robert Mercer, a secretive hedge fund billionaire, renamed Cambridge Analytica, and achieved a certain notoriety as the data analytics firm that played a role in both Trump and Brexit campaigns. But all of this was still to come. London in 2013 was still basking in the afterglow of the Olympics. Britain had not yet Brexited. The world had not yet turned. "That was before we became this dark, dystopian data company that gave the world Trump," a former Cambridge Analytica employee who I'll call Paul tells me. "It was back when we were still just a psychological warfare firm." Was that really what you called it, I ask him. Psychological operations – the same methods the military use to effect mass sentiment change.

In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dependence based on estimating the inverse of the Cholesky factor of the covariance matrix. Penalized maximum likelihood estimation of this matrix yields a simple regression interpretation for local dependence in which variables are predicted by their neighbors. Our proposed method involves solving a convex, penalized Gaussian likelihood problem with a hierarchical group lasso penalty. The problem decomposes into independent subproblems which can be solved efficiently in parallel using first-order methods. Our method yields a sparse, symmetric, positive definite estimator of the precision matrix, encoding a Gaussian graphical model. We derive theoretical results not found in existing methods attaining this structure. In particular, our conditions for signed support recovery and estimation consistency rates in multiple norms are as mild as those in a regression problem. Empirical results show our method performing favorably compared to existing methods. We apply our method to genomic data to flexibly model linkage disequilibrium. Our method is also applied to improve the performance of discriminant analysis in sound recording classification.

The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20\%-50\% of the inputs are not available.

Peter Donnelly asks: "Why rage against machines when we could be friends?" Unfortunately, he neglects to mention the way the technology can be exploited to the detriment of individuals and communities. We(???) gave a similar upbeat prediction of the benefits information technology could provide when it first appeared some decades ago and, indeed, continue to do so. Much of this is justified – information technology has transformed our capabilities and in doing so has been of huge benefit to mankind in many domains. But some of that capability has been exploited by our dark side, ranging from pornography to criminal activities, fraud, trolling and hacking, taking in controversial surveillance and threats to privacy. Much of the current discussion in information technology communities is devoted to ways of fighting the dark side and organisations increasingly deploy more of their resources to fighting off or repairing the damage from the "bad guys".

I have spent a lot of time working with spreadsheets, databases, and data more generally. This work has led to me having a very particular set of skills, skills I have acquired over a very long career. Skills that make me a nightmare for people like you. If you let my daughter go now, that'll be the end of it. I will not look for you, I will not pursue you.