The idea of smart cities – infrastructure interlinked by software – isn't new, but it's undeniably cool. Who wouldn't want to live somewhere where programs use data and evidence, not intuition, to actively improve their day-to-day lives? Now imagine that an entire smart city actually exists, but it's even more advanced than you could possibly imagine, where infrastructural systems are altered on the fly by an artificial intelligence (AI). This may sound futuristic, but one such place can already be found in China. As reported back in October 2016, the government of the city of Hangzhou – home to over 9 million people – collaborated with Alibaba and Foxconn to build the "City Brain" project.

Other than simplifying jobs, what can ML do to improve functions in data-intensive industries? Given the rise of machine learning (ML), the disruptive potential on every industry has become commonly accepted knowledge. What's been referred to as the merging of humans and machines is poised to transform society, politics, industrial processes, medicine, business and even war. In fact, IDC projects that 75 percent of future business software will include some form of artificial intelligence (ML being a form of AI) features within the year. The benefits of this type of technology are most significant within data-intensive, algorithm-based industries.

Octavia, a humanoid robot designed to fight fires on Navy ships, has mastered an impressive range of facial expressions. When she's turned off, she looks like a human-size doll. She has a smooth white face with a snub nose. Her plastic eyebrows sit evenly on her forehead like two little capsized canoes. When she's on, however, her eyelids fly open and she begins to display emotion.

The U.S. has so far been relatively permissive toward AI technologies--and we should keep it that way. It's the reason so much innovation happens here rather than in the more prohibitory European nations. The main reason the government hasn't hampered the industry with regulation is that there's no overbearing federal agency dedicated strictly to AI. Instead, we have a patchwork of federal and state authorities scrutinizing these technologies. The Federal Trade Commission and the National Highway Traffic Safety Administration, for example, recently hosted a workshop to determine how to oversee automated-car technologies.

From sales funnel acceleration to network management automation, artificial intelligence (AI) applications have rapidly emerged as key drivers of business advantage. Gartner ranked "AI and Advanced Machine Learning" as of one of its 10 strategic technology initiatives for 2017, citing a wide range of potential use cases including ones in autonomous vehicles, mesh devices and virtual assistants/advisors. AI in networking is key to a future of automation, since WAN connectivity will need to keep pace if all of these AI-enabled innovations are to reach their full potential. Consider the case of chatbots: These AI-powered programs made headlines when Facebook, among other tech titans, highlighted their utility in streamlining basic online activities such as customer support and ticket purchases. AI and machine learning in networking have become more useful as WAN requirements have evolved.

Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. We proposed the Multiscale Generalized Correlation (MGC) in Shen et al. 2017 as a novel correlation measure, which worked well empirically and helped a number of real data discoveries. But there is a wide gap with respect to the theoretical side, e.g., the population statistic, the convergence from sample to population, how well does the algorithmic Sample MGC perform, etc. To better understand its underlying mechanism, in this paper we formalize the population version of local distance correlations, MGC, and the optimal local scale between the underlying random variables, by utilizing the characteristic functions and incorporating the nearest-neighbor machinery. The population version enables a seamless connection with, and significant improvement to, the algorithmic Sample MGC, both theoretically and in practice, which further allows a number of desirable asymptotic and finite-sample properties to be proved and explored for MGC. The advantages of MGC are further illustrated via a comprehensive set of simulations with linear, nonlinear, univariate, multivariate, and noisy dependencies, where it loses almost no power against monotone dependencies while achieving superior performance against general dependencies.

We present an algorithm for classification tasks on big data. Experiments conducted as part of this study indicate that the algorithm can be as accurate as ensemble methods such as random forests or gradient boosted trees. Unlike ensemble methods, the models produced by the algorithm can be easily interpreted. The algorithm is based on a divide and conquer strategy and consists of two steps. The first step consists of using a decision tree to segment the large dataset. By construction, decision trees attempt to create homogeneous class distributions in their leaf nodes. However, non-homogeneous leaf nodes are usually produced. The second step of the algorithm consists of using a suitable classifier to determine the class labels for the non-homogeneous leaf nodes. The decision tree segment provides a coarse segment profile while the leaf level classifier can provide information about the attributes that affect the label within a segment.

WASHINGTON – Some Americans could see a lot more drones flying around their communities as a result of a Trump administration test program to increase government and commercial use of the unmanned aircraft. President Donald Trump gave the go-ahead Wednesday, signing a directive intended to increase the number and complexity of drone flights. The presidential memo would allow exemptions from current safety rules so communities could move ahead with testing of drone operations. States, communities and tribes selected to participate would devise their own trial programs in partnership with government and industry drone users. The administration anticipates approving at least five applications, but there is no limit on the number of communities that can join.

DARPA is looking to develop robotic swarms that can assist troops from the ground and skies, with hundreds of drones all working together. The Pentagon's research branch has revealed its Offensive Swarm-Enabled Tactics (OFFSET) program is seeking ideas for new systems that could allow for'human-swarm teaming.' The program has awarded contracts to teams from Raytheon and Northrop Grumman to design, develop, and deploy the technology in physical and virtual environments – and eventually, they're hoping to create swarms of over 250 robots. The Pentagon's research branch has revealed its Offensive Swarm-Enabled Tactics (OFFSET) program is seeking ideas for new systems that could allow for'human-swarm teaming' in cities High-tech weapons that the Secret Service is not trained to cope with pose a'grave threat' to the White House, a former agent has warned. Swarms of weaponized drones or even 3D printed plastic guns could be used to launch unforeseen attacks on the president, according to Dan Bongino, author of Protecting the President.

Labor has secured support for a Senate inquiry into the dramatic changes about to hit the workforce in the belief the political establishment is not doing enough to prepare people for the impact of innovation and robotics. The move follows a warning sounded last month by the shadow minister for the future of work and the digital economy Ed Husic who told the AFR's innovation summit that Australia risked being swamped by the consequences of technological change due to the reluctance of government and elements of business to take a lead role in preparing for automation and innovation. Mr Husic said 3.5 million Australian jobs stood to be affected by automation and change, including 250,000 vehicle drivers, while the economy stood to gain $1 trillion by 2030. Yet the rapidly-approaching change was barely audible in the national debate, in part due to the government retreating from its key innovation theme following a backlash during the last election campaign and criticism by former prime minister Tony Abbott.