Significant technological advances have reshaped society as we know it. But the World Economic Forum (WEF) warned that while this is pushing us into "the fourth industrial revolution" and is transforming the labour markets beyond all recognition from decades ago, it will lead to a net loss of over 5 million jobs in 15 major developed and emerging economies by 2020. These countries include Australia, China, France, Germany, India, Italy, Japan, the UK, and the US. WEF said in its report, entitled "The Future of Jobs," which was published on Monday, that while skills and jobs displacement will affect every industry and geographical region, these job losses can be offset by employment growth in other areas. WEF estimated that 7.1 million jobs could be lost through redundancy, automation, or disintermediation, while the creation of 2.1 million new jobs, mainly in more specialised areas such as computing, math, architecture, and engineering, could partially offset some of the losses. "Without urgent and targeted action today to manage the near-term transition and build a workforce with futureproof skills, governments will have to cope with ever-growing unemployment and inequality, and businesses with a shrinking consumer base," said Klaus Schwab, founder and executive chairman of the World Economic Forum, in the report.

Lara Croft, who turns 20 today, has been described as all of these. Born at the height of Britpop, the female protagonist of computer game Tomb Raider became one of the pillars of Cool Britannia - but also provoked the ire of feminists who criticised her sexualised image. Her journey took in two Hollywood films, numerous magazine covers and advertising campaigns but began in the comparatively unglamorous English city of Derby. Tomb Raider was created by a small team of people working for Core Design, a video game developer founded in the city in 1988. "The story goes that within the industry it wasn't easy to sell a female heroine," says Heather Gibson, one of the six developers who created the original game.

These days there's no shortage of things to keep you awake at night, wherever you stand on the political spectrum. For others it's the prospect of Brexit being thwarted. For others still, it's whether the Chinese economy will hold up, what the outcome of the US presidential election will be or the risk of artificial intelligence taking over your job. So what's the best way to handle the inevitable anxiety that goes hand-in-hand with all that uncertainty? Will Borrell studied that anxiety up close after the Brexit vote in the UK earlier this year.

LONDON--(BUSINESS WIRE)--MediaGamma has announced the launch of a new Audience Prediction product, which is set to make a major impact on the ad tech market. By applying deep learning to unique data sets, coupled with MediaGamma's unique AI Decision Support Engine, the product is set to provide players in the ecosystem with over 90% certainty about a user's interests and demographic profile. The new product will help people to navigate uncertainty to make better decisions, and a major telecoms company has already signed up. The Audience Prediction product is the latest in a broad portfolio of products created by MediaGamma (http://www.mediagamma.com/), The start-up's world-renown team of data scientists deliver bespoke real time, prediction-based data science solutions focusing on online user behaviour.

Borderline personality disorder and narcissistic personality disorder are important nosographic entities and have been subject of intensive investigations. The currently prevailing psychodynamic theory for mental disorders is based on the repertoire of defense mechanisms employed. Another line of research is concerned with the study of psychological traumas and dissociation as a defensive response. Both theories can be used to shed light on some aspects of pathological mental functioning, and have many points of contact. This work merges these two psychological theories, and builds a model of mental function in a relational context called Quadripolar Relational Model. The model, which is enriched with ideas borrowed from the field of computer science, leads to a new therapeutic proposal for psychological traumas and personality disorders.

We present a fast variational Bayesian algorithm for performing non-negative matrix factorisation and tri-factorisation. We show that our approach achieves faster convergence per iteration and timestep (wall-clock) than Gibbs sampling and non-probabilistic approaches, and do not require additional samples to estimate the posterior. We show that in particular for matrix tri-factorisation convergence is difficult, but our variational Bayesian approach offers a fast solution, allowing the tri-factorisation approach to be used more effectively.

In this paper, we introduce a new image representation based on a multilayer kernel machine. Unlike traditional kernel methods where data representation is decoupled from the prediction task, we learn how to shape the kernel with supervision. We proceed by first proposing improvements of the recently-introduced convolutional kernel networks (CKNs) in the context of unsupervised learning; then, we derive backpropagation rules to take advantage of labeled training data. The resulting model is a new type of convolutional neural network, where optimizing the filters at each layer is equivalent to learning a linear subspace in a reproducing kernel Hilbert space (RKHS). We show that our method achieves reasonably competitive performance for image classification on some standard "deep learning" datasets such as CIFAR-10 and SVHN, and also for image super-resolution, demonstrating the applicability of our approach to a large variety of image-related tasks.

In this paper we extend the classical notion of strong and weak backdoor sets for SAT and CSP by allowing that different instantiations of the backdoor variables result in instances that belong to different base classes; the union of the base classes forms a heterogeneous base class. Backdoor sets to heterogeneous base classes can be much smaller than backdoor sets to homogeneous ones, hence they are much more desirable but possibly harder to find. We draw a detailed complexity landscape for the problem of detecting strong and weak backdoor sets into heterogeneous base classes for SAT and CSP.

AUSTIN, Texas -- The race is on in Formula One. Not just to the checkered flag, but to see which team can marshall the best technology. In its 70th year, the preeminent auto-racing circuit has become a tech arms race. At the U.S. Grand Prix here this past weekend, the Internet of Things, big data, virtual reality, machine learning, 3-D printing, flash storage, predictive analytics and design play integral roles in the success (or failure) of the 22 drivers that compete in 21 races globally each year. The slightest advancement, or tweak, can mean the difference between first place and 10th place -- often the difference of one second.

World chess champion Magnes Carlsen (right) won't play his computer or play the game like a computer. Instead, he chooses his strategy based on what he knows about his opponent. World chess champion Magnes Carlsen (right) won't play his computer or play the game like a computer. Instead, he chooses his strategy based on what he knows about his opponent. Next month, there's a world chess championship match in New York City, and the two competitors, the assembled grandmasters, the budding chess prodigies, the older chess fans -- everyone paying attention -- will know this indisputable fact: A computer could win the match hands down. They've known as much for almost 20 years -- ever since May 11, 1997.