In February, Europe's first sex doll brothel opened in Barcelona, allowing keen visitors to pay to get intimate with animatronic models. Now, the firm behind the brothel has announced that it is looking to set up a second shop in the UK. Lumidolls, which describes its dolls as'totally realistic', is actively looking for a UK investor to finance the brothel. In February, Europe's first sex doll brothel opened in Barcelona, allowing keen visitors to pay to get intimate with animatronic models. Many people can see a variety of benefits that sexbots have to offer.

A consortium of British companies has unveiled a plan to test driverless cars on UK roads and motorways in 2019. The Driven group also plans to try out a fleet of autonomous vehicles between London and Oxford. The cars will communicate with each other about any hazards and should operate with almost full autonomy - but will have a human on board as well. Previous tests of driverless vehicles in the UK have mainly taken place at slow speeds and not on public roads. The Driven consortium is led by Oxbotica, which makes software for driverless vehicles.

We are in the midst of perhaps the largest societal disruption in history. Digitalisation is changing the way we work, consume, communicate, think, live. The industrial revolution in the 18th Century took about 80 years. It resulted in mass urbanisation and mass technological, economic, societal, social, environmental, geostrategic and political change. Digitalisation goes beyond this in many ways.

There is a wide gap between symbolic reasoning and deep learning. In this research, we explore the possibility of using deep learning to improve symbolic reasoning. Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans. To enable the neural network to recognise patterns of algebraic expressions with non-deterministic sizes, reduced partial trees are used to represent the expressions. Also, to represent both top-down and bottom-up information of the expressions, a centralisation technique is used to improve the reduced partial trees. Besides, symbolic association vectors and rule application records are used to improve the rewriting processes. Experimental results reveal that the algebraic reasoning examples can be accurately learnt only if the feedforward neural network has enough hidden layers. Also, the centralisation technique, the symbolic association vectors and the rule application records can reduce error rates of reasoning. In particular, the above approaches have led to 4.6% error rate of reasoning on a dataset of linear equations, differentials and integrals.

Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component. In this work we introduce a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain.

The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others. In this work, we estimate log determinants under the framework of maximum entropy, given information in the form of moment constraints from stochastic trace estimation. The estimates demonstrate a significant improvement on state-of-the-art alternative methods, as shown on a wide variety of UFL sparse matrices. By taking the example of a general Markov random field, we also demonstrate how this approach can significantly accelerate inference in large-scale learning methods involving the log determinant.

Determinantal Point Processes (DPPs) are probabilistic models that arise in quantum physics and random matrix theory and have recently found numerous applications in computer science. DPPs define distributions over subsets of a given ground set, they exhibit interesting properties such as negative correlation, and, unlike other models, have efficient algorithms for sampling. When applied to kernel methods in machine learning, DPPs favor subsets of the given data with more diverse features. However, many real-world applications require efficient algorithms to sample from DPPs with additional constraints on the subset, e.g., partition or matroid constraints that are important to ensure priors, resource or fairness constraints on the sampled subset. Whether one can efficiently sample from DPPs in such constrained settings is an important problem that was first raised in a survey of DPPs by \cite{KuleszaTaskar12} and studied in some recent works in the machine learning literature. The main contribution of our paper is the first resolution of the complexity of sampling from DPPs with constraints. We give exact efficient algorithms for sampling from constrained DPPs when their description is in unary. Furthermore, we prove that when the constraints are specified in binary, this problem is #P-hard via a reduction from the problem of computing mixed discriminants implying that it may be unlikely that there is an FPRAS. Our results benefit from viewing the constrained sampling problem via the lens of polynomials. Consequently, we obtain a few algorithms of independent interest: 1) to count over the base polytope of regular matroids when there are additional (succinct) budget constraints and, 2) to evaluate and compute the mixed characteristic polynomials, that played a central role in the resolution of the Kadison-Singer problem, for certain special cases.

We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence intervals for DTRs has been extensively studied, prediction and tolerance intervals have received little attention. We begin by reviewing in detail different interval estimation and prediction methods and then adapting them to the DTR setting. We illustrate some of the challenges associated with tolerance interval estimation stemming from the fact that we do not typically have data that were generated from the estimated optimal regime. We give an extensive empirical evaluation of the methods and discussed several practical aspects of method choice, and we present an example application using data from a clinical trial. Finally, we discuss future directions within this important emerging area of DTR research.

Many new computer devices do not understand Icelandic, a unique descendant of the Old Norse language filled with ultra-descriptive words such as Hundslappadrifa, or "heavy snowfall with large flakes occurring in calm wind." This omission is compounding a bigger issue on the North Atlantic island of about 340,000 people. SEE ALSO: Companies will have to prove equal pay in Iceland now, and it's pretty great Icelandic, seen by residents as a source of identity and pride, is losing ground as English becomes the lingua franca of mass tourism and voice-activated devices, the Associated Press reported. Linguistic experts have warned the language is at risk of dying out in the modern world, particularly as tourism booms and foreign workers find more jobs on the rugged island. Unless the government, educators, families, and tech developers make a concerted effort to preserve Icelandic, it could easily be relegated to history books.

HOUSTON -- (April 17) -- Rice University computer scientists are mapping a new solution for interior navigational location detection by linking it to existing sensors in mobile devices. Their results were presented in a paper at last month's 2017 Design, Automation and Test in Europe (DATE) Conference in Lausanne, Switzerland. Rice University researchers (from left) Chen Luo, Anshumali Shrivastava and Juan Jose Gonzalez Espana published a paper on location detection for navigation with Krishna Palem (not pictured). Six months ago, the same researchers published a paper on their first technology for a new indoor mobile positioning system called CaPSuLe. The navigational location detection system began as a solution for mobile device users inside large indoor spaces like office complexes or shopping malls where GPS navigation falters under poor signals that quickly deplete battery life.