BEIJING – The Canbot can say its name, respond to voice commands, and "dance" as it plays Michael Jackson's "Billie Jean." Other robots China is displaying at the World Robot Conference can play badminton, sand cell phone cases and sort computer chips. China is showcasing its burgeoning robot industry at the five-day exhibition in Beijing, part of a national effort to promote use of more advanced technologies in Chinese factories and create high-end products that redefine the meaning of "Made in China." Apart from the cool factor, China's sweeping plans to upgrade its factories and production lines depend on building and better using advanced robots. Automation is crucial for industries facing rising labor costs and slowing growth in the work force thanks to the "one-child" policy era and aging of the population.

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.

It used to be that even sophisticated mobile robots could be easily defeated by using (say) a table to block its way. The robot would sense the table, categorize it as an obstacle, try to plan a path around it, and then give up when its planner fails. This works because robots generally don't know what most objects are, or how they work, or what you can do with them: They just get turned into obstacles to be avoided, because in most cases, that's the easiest and safest thing to do. You can't normally use a table across a hallway to deter a human, because humans understand that tables are physical objects that can be moved, and the human will just pull the table out of the way and keep on going. Even if the table doesn't behave exactly the way we'd expect it to (like, one of the wheels is stuck), we can adapt, and figure it out.

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.

Cross-validation (CV) is a technique for evaluating the ability of statistical models/learning systems based on a given data set. Despite its wide applicability, the rather heavy computational cost can prevent its use as the system size grows. To resolve this difficulty in the case of Bayesian linear regression, we develop a formula for evaluating the leave-one-out CV error approximately without actually performing CV. The usefulness of the developed formula is tested by statistical mechanical analysis for a synthetic model. This is confirmed by application to a real-world supernova data set as well.

This paper studies how to capture dependency graph structures from real data which may not be multivariate Gaussian. Starting from marginal loss functions not necessarily derived from probability distributions, we use an additive over-parametrization with shrinkage to incorporate variable dependencies into the criterion. An iterative Gaussian graph learning algorithm is proposed with ease in implementation. Statistical analysis shows that with the error measured in terms of a proper Bregman divergence, the estimators have fast rate of convergence. Real-life examples in different settings are given to demonstrate the efficacy of the proposed methodology.

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.

This past May, China announced its AI roadmap for the next three years: The National Development and Reform Commission expects their artificial intelligence sector will give them a market worth topping 100 billion yuan (that's 15.26 billion) within that time period. Where is China's AI sector at now, and what steps could plausibly deliver that strong a return by 2019? Tech.Co had a chat with Luke Tang, General Manager of global entrepreneurship company TechCode, who drew on his experience working with AI companies worldwide to detail China's unique opportunities. Here are the biggest takeaways on AI in China. "China has already got a great start in both research and business applications in AI -- right behind the U.S. […] On the research side, the White House recently published a report showing that China has now surpassed the U.S. in total papers and citations published in Deep Learning or Neural Network, a crucial area in AI, but overall both the U.S. and China are way ahead of other countries. In fact, author's names with Chinese-origin represent about 50 percent of the articles published yearly in Deep Learning. Of course, there are other keywords one can use to search, e.g. computer vision, autonomous cars, natural language processing, etc. but it's very clear that the two leading countries in AI research are U.S. and China. This is actually very surprising to me, since China is not usually very strong in other fundamental sciences or computer science areas."

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.