Artificial intelligence has been a dream in technology ever since Alan Turing first wrote his seminal paper, Computing Machinery and Intelligence, Now, thanks to advances in hardware power and algorithm design, AI is a growth industry – and it has no shortage of vocal advocates. These are some of the most vocal and influential leaders working on artificial intelligence, robotics, chat bots, virtual reality, the ethics of autonomous software and vehicles and more. Organizes the London AI meet up and the annual Research and Applied AI Summit. Thanks to all who kicked off discussion on the back of my piece on "6 areas of #AI/ML to watch closely" Keep going! She has expertise in designing intelligent systems into working AI systems to help understand natural intelligence.

Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning. The 2016 edition of the conference comprised more than 2,400 paper submissions, 3,000 reviewers, and 8,000 attendees, representing a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and over 100% in terms of attendees as compared to the previous year. In this report, we analyze several aspects of the data collected during the review process, including an experiment investigating the efficacy of collecting ordinal rankings from reviewers (vs. usual scores aka cardinal rankings). Our goal is to check the soundness of the review process we implemented and, in going so, provide insights that may be useful in the design of the review process of subsequent conferences. We introduce a number of metrics that could be used for monitoring improvements when new ideas are introduced.

We consider a variation on the problem of prediction with expert advice, where new forecasters that were unknown until then may appear at each round. As often in prediction with expert advice, designing an algorithm that achieves near-optimal regret guarantees is straightforward, using aggregation of experts. However, when the comparison class is sufficiently rich, for instance when the best expert and the set of experts itself changes over time, such strategies naively require to maintain a prohibitive number of weights (typically exponential with the time horizon). By contrast, designing strategies that both achieve a near-optimal regret and maintain a reasonable number of weights is highly non-trivial. We consider three increasingly challenging objectives (simple regret, shifting regret and sparse shifting regret) that extend existing notions defined for a fixed expert ensemble; in each case, we design strategies that achieve tight regret bounds, adaptive to the parameters of the comparison class, while being computationally inexpensive. Moreover, our algorithms are anytime, agnostic to the number of incoming experts and completely parameter-free. Such remarkable results are made possible thanks to two simple but highly effective recipes: first the "abstention trick" that comes from the specialist framework and enables to handle the least challenging notions of regret, but is limited when addressing more sophisticated objectives. Second, the "muting trick" that we introduce to give more flexibility. We show how to combine these two tricks in order to handle the most challenging class of comparison strategies.

We introduce features for massive data streams. These stream features can be thought of as "ordered moments" and generalize stream sketches from "moments of order one" to "ordered moments of arbitrary order". In analogy to classic moments, they have theoretical guarantees such as universality that are important for learning algorithms.

The Chirikov standard map and the 2D Froeschlé map are investigated. A few thousand values of the Hurst exponent (HE) and the maximal Lyapunov exponent (mLE) are plotted in a mixed space of the nonlinear parameter versus the initial condition. Both characteristic exponents reveal remarkably similar structures in this space. A tight correlation between the HEs and mLEs is found, with the Spearman rank ρ 0.83 and ρ 0.75 for the Chirikov and 2D Froeschlé maps, respectively. Based on this relation, a machine learning (ML) procedure, using the nearest neighbor algorithm, is performed to reproduce the HE distribution based on the mLE distribution alone. A few thousand HE and mLE values from the mixed spaces were used for training, and then using 2 2.4 10 The ML procedure allowed to reproduce the structure of the mixed spaces in great detail.

This particular solution finds the most exciting parts of a match by analyzing the crowd's cheers, as well as the players' gestures and facial expressions. It then automatically generates videos of the most thrilling moments, which are then posted on Facebook and published on the US Open apps. Noah Syken, IBM VP of Sports & Entertainment Partnerships, explained that USTA turned to Watson for help, because there could be as many as 18 matches going on at the same time. Even the fastest video team will have a hard time analyzing matches and stitching the best moments together as they happen. It probably also helped that IBM tested Cognitive Highlights as a proof of concept at the Master's Tournament earlier this year, and Wimbledon also used the technology to generate some videos. In addition to Cognitive Highlights, the US Open is also using Watson's Conversation API to power its Cognitive Concierge app.

A couple of weeks ago, Facebook revealed that two of its artificial intelligence (AI) machines had developed their own language to communicate in a more efficient fashion. The response was wide-scale scaremongering from pundits who lamented the evolution of computers. It might be a while before robots take over, but a recent study from Oxford University suggests that robots and AI will replace most human tasks by as early as 2051 and all human jobs by 2136. Technology has already progressed enough to give us driverless cars, robot police and autonomous delivery drones, but the true impact will go beyond making large swaths of the population redundant and drastically alter our society as we know it – from education and health care, to the criminal justice system. "Traditionally, to get a computer to do something, you had to write code and algorithms, but AI is different...the algorithm works independently," said Duncan Angove, president of software company Infor at a recent conference in New York.

IMAGE: MouseAge.Org provides tools for cross-species analysis, and provide correlations between health and appearance. Tuesday, 29th of August, 2017, Baltimore, MD - Insilico Medicine, Inc, a Baltimore-based next-generation artificial intelligence company, today announced its participation in the MouseAge.org The scientists from Insilico Medicine will collaborate with scientists from Harvard, Oxford, Youth Laboratories, the Biogerontology Research Foundation, and other institutions to enable scientists worldwide to derive more information from rodent studies, develop novel biomarkers of aging and various diseases in mice, develop tools for cross-species analysis, and provide correlations between health and appearance. The project campaign has been launched today at research crowdfunding platform Lifespan.io. The project was conceived by Vadim Gladyshev, Professor of Medicine at Brigham and Women's Hospital, Harvard Medical School, and Alex Zhavoronkov, CEO of Insilico Medicine.

Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments. Several issues, including slow dynamics, continue to affect their real world performances. At the same time, the need for estimating pollutant concentrations on board the devices, espe- cially for wearables and IoT deployments, is becoming highly desirable. In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers. This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches. We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology. Our results can guide researchers and engineers in the choice of optimal strategy. They show that non-linear multivariate techniques yield reproducible results, outperforming lin- ear approaches. Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios. We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs. We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response. The latter have been shown to be best choice whenever prompt and precise response is needed.

The n-Queens problem is to place n chess queens on an n by n chessboard so that no two queens are on the same row, column or diagonal. The n-Queens Completion problem is a variant, dating to 1850, in which some queens are already placed and the solver is asked to place the rest, if possible. We show that n-Queens Completion is both NP-Complete and #P-Complete. A corollary is that any non-attacking arrangement of queens can be included as a part of a solution to a larger n-Queens problem. We introduce generators of random instances for n-Queens Completion and the closely related Blocked n-Queens and Excluded Diagonals Problem. We describe three solvers for these problems, and empirically analyse the hardness of randomly generated instances. For Blocked n-Queens and the Excluded Diagonals Problem, we show the existence of a phase transition associated with hard instances as has been seen in other NP-Complete problems, but a natural generator for n-Queens Completion did not generate consistently hard instances. The significance of this work is that the n-Queens problem has been very widely used as a benchmark in Artificial Intelligence, but conclusions on it are often disputable because of the simple complexity of the decision problem. Our results give alternative benchmarks which are hard theoretically and empirically, but for which solving techniques designed for n-Queens need minimal or no change.