Brexit could help usher in the rise of robots As headlines go, " Brexit leads to robot takeover" sounds like satire. It's up there with Brexit being "the opportunity to create a second Elizabethan Golden Age". Both have been written recently - but I would argue that the former may actually be true. Recessions force companies to make difficult choices to survive. As headlines go, " Brexit leads to robot takeover" sounds like satire.

We present a robust generalization of the synthetic control method for comparative case studies. Like the classical method, we present an algorithm to estimate the unobservable counterfactual of a treatment unit. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of donors, overcomes the challenges of missing data, and continues to work well in settings where covariate information may not be provided. To begin, we establish the condition under which the fundamental assumption in synthetic control-like approaches holds, i.e. when the linear relationship between the treatment unit and the donor pool prevails in both the pre- and post-intervention periods. We provide the first finite sample analysis for a broader class of models, the Latent Variable Model, in contrast to Factor Models previously considered in the literature. Further, we show that our de-noising procedure accurately imputes missing entries, producing a consistent estimator of the underlying signal matrix provided $p = \Omega( T^{-1 + \zeta})$ for some $\zeta > 0$; here, $p$ is the fraction of observed data and $T$ is the time interval of interest. Under the same setting, we prove that the mean-squared-error (MSE) in our prediction estimation scales as $O(\sigma^2/p + 1/\sqrt{T})$, where $\sigma^2$ is the noise variance. Using a data aggregation method, we show that the MSE can be made as small as $O(T^{-1/2+\gamma})$ for any $\gamma \in (0, 1/2)$, leading to a consistent estimator. We also introduce a Bayesian framework to quantify the model uncertainty through posterior probabilities. Our experiments, using both real-world and synthetic datasets, demonstrate that our robust generalization yields an improvement over the classical synthetic control method.

In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate expert (or mentor), we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program that mimics the behavior of a superior expert. The resulting evaluation function of the evolved program consists of a much smaller number of parameters than the expert's. The extended experimental results provided in this paper include a report of our successful participation in the 2008 World Computer Chess Championship. In principle, our expert-driven approach could be used in a wide range of problems for which appropriate experts are available. Keywords Computer chess, Fitness evaluation, Games, Genetic algorithms, Parameter tuning 1 Introduction Since the dawn of modern computer science, game playing has posed a formidable challenge in the field of Artificial Intelligence. A preliminary version of this paper appeared in Proceedings of the 2008 Genetic and Evolutionary Computation Conference [13] and received the Best Paper Award in the conference's Real-World Applications track. John McCarthy, Ken Thompson, Herbert Simon, and others) developed game-playing programs and used games in AI research. The ongoing key role played by and the impact of computer games on AI should not be underestimated.

This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program. This is achieved by combining supervised and unsupervised learning. In the supervised learning phase the organisms are evolved to mimic the behavior of human grandmasters, and in the unsupervised learning phase these evolved organisms are further improved upon by means of coevolution. While past attempts succeeded in creating a grandmaster-level program by mimicking the behavior of existing computer chess programs, this paper presents the first successful attempt at evolving a state-of-the-art evaluation function by learning only from databases of games played by humans. Our results demonstrate that the evolved program outperforms a two-time World Computer Chess Champion.

In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate mentor, we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program with a smaller number of parameters in its evaluation function to mimic the behavior of a superior mentor which uses a more extensive evaluation function. In principle, our mentor-assisted approach could be used in a wide range of problems for which appropriate mentors are available.

This paper discusses about an R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF algorithm. It also contains a function which automates all other functions to obtain optimized prediction results. The aim of this package is to promote the PSF algorithm and to ease its implementation with minimum efforts. This paper describes all the functions in the PSF package with their syntax. It also provides a simple example of usage. Finally, the usefulness of this package is discussed by comparing it to auto.arima and ets, well-known time series forecasting functions available on CRAN repository.

The last time I sat down to interview Sir Michael Barber, the world looked and felt a very different place. Even a few years ago, Donald Trump's tweets were making international headlines, Brexit had its supporters and detractors, and North Korea was an unpredictable rogue state harbouring nuclear ambitions. And just like today, governments the world over were striving to gain control over a fast-changing environment pock-marked by new technologies, diverse policy approaches and different ways to improve public impact. But of course, some changes are hard to miss. Artificial intelligence (AI), for example, is fast moving from concept to reality.

The Faroe Islands didn't have Google street view, but they wanted to. So they strapped 360-degree cameras on the backs of sheep to make their own.

It's been a few weeks since I've done a news wrap-up, thanks to a knee-deep pile of review games I'm still slowly churning through. But my Total War sensors went off this week, so it's time to return. Also up this week: Pillars of Eternity 2 hits beta, Thermaltake's new gaming chair cools your butt off, the end of Marvel Heroes, the start for Verdun sequel Tannenberg, and of course all the internet (and real-world) drama surrounding loot boxes. This is gaming news for November 13 to 17. We've got two free-to-try titles this weekend, with both available for steep discounts if you wind up enjoying the games. For strategy fans it's Endless Space 2, launched earlier this year and updated with new diplomatic options this week.

"Justice League" opens in theaters today.Courtesy of Warner Brothers. Once upon a time, in the long long ago, this bad guy from a lava planet comes to Earth with these three magic boxes and tries to turn our pretty blue marble into a red marble, but is defeated by some people who live in the sea (Atlanteans), some warrior women who live on an island (Amazons), and "the tribes of man" (you, me and the bourgeoisie). The bad man escapes and loses these three precious boxes he really adores. One goes under the sea, one goes to the island, and then the humans bury one in some ditch in a forest. Are you still with me?