We know that automation is affecting the labor market, but forecasts differ on how swift and dramatic the future impact will be. A new report now suggests things may happen more slowly than some have predicted. Last month, it was reported that Foxconn plans to replace almost every human worker in some of its manufacturing plants with a robot. Perhaps more worrying for office workers was the news that a Japanese insurance firm was sacking over 30 staff because artificial intelligence could calculate payouts just as well as humans. No wonder that two-thirds of Americans believe that robots will soon take on the majority of work currently performed by people.

Now is the time of the year when every marketer should well reflect on the achievements of 2016 and holistically examine what the New Year is going to mean for marketing, branding, and business. With this article I proceed with a good tradition by following-up on my previous and related articles for 2016, 2015, and 2014. As we all know, paying attention to what's on the horizon does offer valuable insights. Knowing the below-listed 16 marketing trends which I consider as highly relevant for 2017, should help you to build, expand, and keep strong brands. Although this should have been ingrained in every marketer s DNA for years, however, with all the data, tech, and buzzwords flying around, one of the biggest marketing challenges of 2017 will be to stay calm, focused, and to be obsessed with the only one that matters: Your customer! How to identify her, to reach her, to engage her, to make her purchase, to have her stay loyal, to make her to recommend you, and to have her even re-purchase more of your products in the future? There is a certain risk that (marketing) managers – more than ever – get confused and fall into the trap of mixing up data-driven insights with a real customer-centric business philosophy. Therefore successful companies and brands don t worship the data gods for the sake of it; instead they have a comprehensive and deeply rooted digital transformation strategy in place which is flanked by a crystal clear marketing action plan. Enhanced and lived by every employee of the organization and not only by a chief digital officer who very often is floating around the company in search for a home base.

The robots are coming, but the march of automation will displace jobs more gradually than some alarming forecasts suggest. A measured pace is likely because what is technically possible is only one factor in determining how quickly new technology is adopted, according to a new study by the McKinsey Global Institute. Other crucial ingredients include economics, labor markets, regulations and social attitudes. The report, which was released Thursday, breaks jobs down by work tasks -- more than 2,000 activities across 800 occupations, from stock clerk to company boss. The institute, the research arm of the consulting firm McKinsey & Company, concludes that many tasks can be automated and that most jobs have activities ripe for automation.

Hours of screen time can be good for teenagers' brains, according to new research from the University of Oxford. The study insists many parents may be too concerned about computers harming their children. In fact, they calculated the'sweet spot' at which point young people get the most out of online activity: 257 minutes. According to their calculations, four hours and 17 minutes is the Goldilocks number, providing enough time to develop social connections and skills. It is only after that point that devices could begin to cripple teenage brains.

Microsoft announced this morning that it has acquired Maluuba, a Toronto startup focused on using deep learning for natural language processing. Deep learning is an approach to artificial intelligence currently in vogue that has driven incredible gains in the field over the last five years. As Microsoft wrote in the blog post announcing the purchase, "We've recently set new milestones for speech and image recognition using deep learning techniques, and with this acquisition we are, as Wayne Gretzky would say, skating to where the puck will be next -- machine reading and writing." The Verge covered Maluuba in the summer of 2016, when the startup shared the results of an AI system that could read and comprehend text with near human capability, outperforming similar systems shown off by Google and Facebook. Along with acquiring the company, Microsoft has also established closer ties with Yoshua Bengio, a pioneer in the field of deep learning who served as an advisor to Maluuba, and will now become and advisor to Microsoft's AI division.

Getty A Tanscorp UU smart robot is displayed at CES 2017 at the Sands Expo and Convention Center in Las Vegas. The European parliament has urged the drafting of a set of regulations to govern the use and creation of robots and artificial intelligence, including a form of "electronic personhood" to ensure rights and responsibilities for the most capable AI. In a 17-2 vote, with two abstentions, the parliament's legal affairs committee passed the report, which outlines one possible framework for regulation. "A growing number of areas of our daily lives are increasingly affected by robotics," said the report's author, Luxembourgish MEP Mady Delvaux. "In order to address this reality and to ensure that robots are and will remain in the service of humans, we urgently need to create a robust European legal framework".

Together they contribute 30% to your overall course mark. Both assignments are to be submitted using the submit command on DICE. You should put all of your work into one prolog file (commenting out any written sections) and submit the file before the deadline. Details of how to use the submit command will be provided at the end of each assignment. Both assignments require you to develop complete Prolog programs. You may develop these programs at home on a PC or Mac but you must test that they run under the DICE version of sicstus before submission.

We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives. In DyNet's dynamic declaration strategy, computation graph construction is mostly transparent, being implicitly constructed by executing procedural code that computes the network outputs, and the user is free to use different network structures for each input. Dynamic declaration thus facilitates the implementation of more complicated network architectures, and DyNet is specifically designed to allow users to implement their models in a way that is idiomatic in their preferred programming language (C++ or Python). One challenge with dynamic declaration is that because the symbolic computation graph is defined anew for every training example, its construction must have low overhead. To achieve this, DyNet has an optimized C++ backend and lightweight graph representation. Experiments show that DyNet's speeds are faster than or comparable with static declaration toolkits, and significantly faster than Chainer, another dynamic declaration toolkit. DyNet is released open-source under the Apache 2.0 license and available at http://github.com/clab/dynet.

The promise of compressive sensing (CS) has been offset by two significant challenges. First, real-world data is not exactly sparse in a fixed basis. Second, current high-performance recovery algorithms are slow to converge, which limits CS to either non-real-time applications or scenarios where massive back-end computing is available. In this paper, we attack both of these challenges head-on by developing a new signal recovery framework we call {\em DeepInverse} that learns the inverse transformation from measurement vectors to signals using a {\em deep convolutional network}. When trained on a set of representative images, the network learns both a representation for the signals (addressing challenge one) and an inverse map approximating a greedy or convex recovery algorithm (addressing challenge two). Our experiments indicate that the DeepInverse network closely approximates the solution produced by state-of-the-art CS recovery algorithms yet is hundreds of times faster in run time. The tradeoff for the ultrafast run time is a computationally intensive, off-line training procedure typical to deep networks. However, the training needs to be completed only once, which makes the approach attractive for a host of sparse recovery problems.

Most high-dimensional estimation and prediction methods propose to minimize a cost function (empirical risk) that is written as a sum of losses associated to each data point. In this paper we focus on the case of non-convex losses, which is practically important but still poorly understood. Classical empirical process theory implies uniform convergence of the empirical risk to the population risk. While uniform convergence implies consistency of the resulting M-estimator, it does not ensure that the latter can be computed efficiently. In order to capture the complexity of computing M-estimators, we propose to study the landscape of the empirical risk, namely its stationary points and their properties. We establish uniform convergence of the gradient and Hessian of the empirical risk to their population counterparts, as soon as the number of samples becomes larger than the number of unknown parameters (modulo logarithmic factors). Consequently, good properties of the population risk can be carried to the empirical risk, and we can establish one-to-one correspondence of their stationary points. We demonstrate that in several problems such as non-convex binary classification, robust regression, and Gaussian mixture model, this result implies a complete characterization of the landscape of the empirical risk, and of the convergence properties of descent algorithms. We extend our analysis to the very high-dimensional setting in which the number of parameters exceeds the number of samples, and provide a characterization of the empirical risk landscape under a nearly information-theoretically minimal condition. Namely, if the number of samples exceeds the sparsity of the unknown parameters vector (modulo logarithmic factors), then a suitable uniform convergence result takes place. We apply this result to non-convex binary classification and robust regression in very high-dimension.