Sign up for Next China, a weekly email on where the nation stands now and where it's going next. When China's wireless carriers debut their 5G networks this year, early adopters whose mobile phones can handle the ultra-fast speeds won't be the only beneficiaries. Rolling 5G service out to the world's biggest population also should give a boost to China's digital economy, including makers of telecommunications equipment, platforms and applications for the internet of things, autonomous driving, surveillance and factory automation. It's the kind of head start that will be expensive at first but could pay off well into the future. Major cities including Beijing, Shanghai and Shenzhen will get broad 5G -- or fifth-generation -- wireless coverage first, while some other cities will start with 5G hotspots.

Over the next decade, automation and artificial intelligence could throw 54 million Americans out of work. Here's everything you need to know: Why is automation a threat? Rapid technological advances are enabling machines to perform a growing number of tasks traditionally done by humans. Law firms now use artificial intelligence (AI) -- sophisticated computer programs that can learn from experience -- to conduct contract analysis, hunt for client conflicts, and even craft litigation strategy. McDon ald's is replacing drive-thru workers with order-taking AI, and cashiers with self-checkout kiosks.

Airport hubs increasingly are embracing technology in their operations. As part of a three-day Airport IT conference in Munich, Amadeus head of airport IT product management Holger Mattig outlined the future of airport management and said that aviation hubs will witness more use of wearables, internet of things (IoT) applications and predictive analysis in the future. Talking about how the IoT has impacted the aviation industry, Mattig said that computing devices are already exchanging data between each other. "If you look at the apron, all of the devices that go on there – the push back tractors, the de-icing elements, all of these are actually able to talk to each other and give data about every stage of activity," he said. "In terms of flight handling, we now have technologies from companies like Assaia who can make prediction through videos generated by machine learning, and technologies like geofencing, where you can manage drones and improve safety. "We have the same for indoor where there are a lot of initiatives that are used to engage with the mobile phones of passengers in events of potential disruptions." While aviation companies are increasingly using technologies such as IoT and machine learning, Mattig said that going forward, airport and airline companies will start using wearable technology to improve efficiency. He added that employees could start wearing devices such as "smart sunglasses" and "smart bracelets" to track passenger activity, and that monitoring how passengers prefer to shop, eat and spend their time in an airport could help authorities to understand consumer behaviour. "Airports must start to build what I would call airport-centric visible analytics by implementing CRM solutions with the aim to look at the profile of passengers.

Editor's note: In concert with the Defense Entrepreneurs Forum's Project Gutenberg, a futurist imagines a post-mortem on an artificial intelligence-aided Chinese invasion. The Department of Defense's chief testified before Congress, revealing details of China's efforts to deter the United States during last year's invasion of Taiwan. WASHINGTON -- The Chinese People's Liberation Army shocked the world last November when they activated nearly two million reservists, mobilized the People's Armed Forces Maritime Militia, and executed a surprisingly successful cross-strait invasion of Taiwan. In a marathon day of testimony before the House Armed Services Committee, Secretary of Defense Barry McDermott revealed how the PLA's cyber branch used artificial intelligence and the "internet of things" to help Chinese conventional forces achieve strategic military aims far from the conventional battlefield in Taiwan. McDermott told committee members the artificial-intelligence capabilities China employed will force a redefinition of "the battlefield" and must change how the US military trains for future conflict.

The context: The vast majority of Facebook's moderation is now done automatically by the company's machine-learning systems, reducing the amount of harrowing content its moderators have to review. In its latest community standards enforcement report, published earlier this month, the company claimed that 98% of terrorist videos and photos are removed before anyone has the chance to see them, let alone report them. So, what are we seeing here?: The company has been training its machine-learning systems to identify and label objects in videos--from the mundane, such as vases or people--to the dangerous, such as guns or knives. Facebook's AI uses two main approaches to look for dangerous content. One is to employ neural networks that look for features and behaviors of known objects and label them with varying percentages of confidence (as we can see in the video, above.)

There's no question that artificial intelligence and data analytics are reshaping the resources sector. These new technologies bring new challenges in the way companies consider risk, adapt to new ways of working and the skills needed for the future. These issues were the topic of conversation at a business roundtable lunch hosted by professional services firm Accenture in its new Perth Innovation Hub this week as part of the Resources Technology Showcase program of events. Invited guests, including leading policymakers and industry heavyweights, heard former SAS commander and Mettle Global managing partner Ben Pronk speak about the need to take calculated risks in the battlefield. He explained how the resources industry could adopt that philosophy to take advantage of the fourth industrial revolution.

In computational models of argumentation, the justification of statements has drawn less attention than the construction and justification of arguments. As a consequence, significant losses of sensitivity and expressiveness in the treatment of statement statuses can be incurred by otherwise appealing formalisms. In order to reappraise statement statuses and, more generally, to support a uniform modelling of different phases of the argumentation process we introduce multi-labelling systems, a generic formalism devoted to represent reasoning processes consisting of a sequence of labelling stages. In this context, two families of multi-labelling systems, called argument-focused and statement-focused approach, are identified and compared. Then they are shown to be able to encompass several prominent literature proposals as special cases, thereby enabling a systematic comparison evidencing their merits and limits. Further, we show that the proposed model supports tunability of statement justification by specifying a few alternative statement justification labellings, and we illustrate how they can be seamlessly integrated into different formalisms.

Transfer learning makes use of data or knowledge in one task to help solve a different, yet related, task. Many ex isting TL approaches are based on a joint probability distribution metric, which is a weighted sum of the marginal distribution and the c ondi-tional distribution; however, they optimize the two distri butions independently, and ignore their intrinsic dependency. This p aper proposes a novel and frustratingly easy Joint Probability Dist ribution Adaptation (JPDA) approach, to replace the frequently-use d joint maximum mean discrepancy metric in transfer learning. Duri ng the distribution adaptation, JPDA improves the transferabili ty between the source and the target domains by minimizing the joint pro b-ability discrepancy of the corresponding class, and also in creases the discriminability between different classes by maximiz ing their joint probability discrepancy. Experiments on six image cl assifica-tion datasets demonstrated that JPDA outperforms several s tate-of- the-art metric-based transfer learning approaches.

The motivation, concept, design and implementation of latent semantic search for search engines have limited semantic search, entity extraction and property attribution features, have insufficient accuracy and response time of latent search, may impose privacy concerns and the search results are unavailable in offline mode for robotic search operations. The alternative suggestion involves autonomous search engine with adaptive storage consumption, configurable search scope and latent search response time with built-in options for entity extraction and property attribution available as open source platform for mobile, desktop and server solutions. The suggested architecture attempts to implement artificial general intelligence (AGI) principles as long as autonomous behaviour constrained by limited resources is concerned, and it is applied for specific task of enabling Web search for artificial agents implementing the AGI.

Is this some sort of weird dream? It is, the scientists at the cutting edge of AI research would have you believe, a glimpse into the future of fully autonomous motor vehicles -- where you might still have to turn a key but you will not have to accelerate, indicate, steer or grumble about another car going too slowly. Once the stuff of wild fantasy, a world where the cars have no drivers behind the wheel -- where road deaths and injuries plummet -- is rapidly becoming more of a vision. Deloitte spokesman Scott Corwin -- the company runs a ''future of mobility team'', so keeps a keen eye on developments -- told the Financial Times this week that the automotive industry was spending massively to investigate self-driving technology, creating a ''race to win the Willy Wonka golden ticket''. Corwin predicted the roll-out of autonomous vehicles would gather pace but with ''limited market launches in pretty controlled environments''.