Many people are already using, perhaps without knowing it, tools based on artificial intelligence (AI). Almost everyone probably ignores the absence of laws that govern how it works. We are surrounded by absurd regulations (for example, in Alabama it is illegal to drive while blindfolded; in France, you can't name a pig "Napoleon," etc.), however, there are no guidelines that regulate the use of software for autonomous driving vehicles nor the algorithms that support decision making to prioritize care for hospital patients or that pre-select which job candidates should be interviewed. Fortunately, it seems that our political systems have finally understood the urgency with which we need to apply legislation to this area. Ed Markey (D-Mass.), and Representatives John K. Delaney (D-Md.) and Pete Olson (R-Texas)-- introduced a bill called The Fundamentally Understanding the Usability and Realistic Evolution of Artificial Intelligence Act of 2017, also known as the FUTURE of AI Act.

Secure is a weekly column that dives into the rapidly escalating topic of cybersecurity. Regardless of how much money is poured into increasing cybersecurity, the situation doesn't seem to improve. Could machine learning that adapts to new methods be the solution? For some companies, such machine learning techniques go hand in hand with more traditional signature based detection and others employ behavioral learning to watch out for other, unspecified threats. For threat prevention company Cylance, however, machine learning is all it needs to offer what it claims is the most effective anti-malware solution available today.

The rapid development of big data techniques has offered great opportunities to develop smart city services in public safety, transportation management, city planning, etc. Meanwhile, the smart city development levels of different cities are still unbalanced. For a large of number of cities which just start development, the governments will face a critical cold-start problem, 'how to develop a new smart city service suffering from data scarcity?'. To address this problem, transfer learning is recently leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm. This article investigates the common process of urban transfer learning, aiming to provide city governors and relevant practitioners with guidelines of applying this novel learning paradigm. Our guidelines include common transfer strategies to take, general steps to follow, and case studies to refer. We also summarize a few future research opportunities in urban transfer learning, and expect this article can attract more researchers into this promising area.

While it might not always be easy to pull the plug on your electronics, doing so is rarely a case of moral dilemma. But, powering down might be a lot more difficult if your devices were begging you not to do it. A new study explores the ways in which social robots can manipulate their owners by pulling on our heartstrings. When robots protested, shouting things such as'No! Please do not switch me off' and implying they were afraid of the dark, participants hesitated and sometimes even refused to turn them off.

A decade ago, a group of Internet companies, civil society organizations, academics, and investors launched the Global Network Initiative (GNI), a collaborative effort to promote free expression and protect user privacy on the Internet. Google helped lead this effort and a parallel project devoted to developing a human rights framework for the Internet. In 2010, Google further demonstrated its leadership by making a principled decision to withdraw its search-engine services from China. In a very public way, the company acknowledged the inherent contradiction between Chinese Internet censorship and Google's commitments to its users and the GNI to promote free expression. It was thus disturbing to read recent reports suggesting that Google now is seriously considering re-entering the Chinese market and succumbing to Chinese censorship in exchange for commercial opportunity.

When Tam Mai Huynh found out in 2016 that the source of his nagging cough was lung cancer, it came as a shock. A recently retired Army Special Forces veteran and married father of two young children, Huynh never had smoked. Nor did he have a family history of cancer. Nevertheless, the disease had spread to Huynh's spine, lymph nodes, and brain. He began chemotherapy, driving two hours every two to three weeks from his home to the U.S. Department of Veterans Affairs (VA) hospital in Durham, North Carolina.

President Xi Jinping's "Made in China 2025" strategy, unveiled in 2015 and now thrust back into the limelight by President Trump's bellicose stance on trade, holds three important lesson for global industry. This should be a non-controversial statement, but it is not. Economists often mock "the manufacturing fetish" and argue there is no reason to consider manufacturing a better driver of economic growth than any other sector. As economies get richer, they tend to shift from agriculture to industry, and then to services. Manufacturing accounted for nearly 30% of the U.S. economy in the 1950s; it was still 20% in the 1980s; today it accounts for just 11%.

"On any shortlist I'm considered to be one of the world's leading experts on applied artificial intelligence," said JT Kostman at a NetApp breakfast in Sydney yesterday. The American's claim has substance. Kostman is a former Time Inc chief data officer, was once Samsung Electronics' chief data scientist, and says he has advised the US Department of Homeland Security, the FBI and the Department of Defense on AI and analytics. But even he isn't quite sure exactly what AI is. "I don't think anybody really does know what artificial intelligence is. I'm not sure that I'm really sure, what it really means. My definition of AI, truly, I define artificial intelligence as getting computers to do stuff that they do on TV and in the movies. That's what AI really is," he says.

Science has always relied on a combination of approaches to derive an answer or develop a theory. The seeds for Darwin's theory of natural selection grew under a Herculean aggregation of observation, data, and experiment. The more recent confirmation of gravitational waves by the Laser Interferometer Gravitational-Wave Observatory (LIGO) was a decades-long interplay of theory, experiment, and computation. Certainly, this idea was not lost on the U.S. Department of Energy's (DOE) Argonne National Laboratory, which has helped advance the boundaries of high-performance computing technologies through the Argonne Leadership Computing Facility (ALCF). Realizing the promise of exascale computing, the ALCF is developing the framework by which to harness this immense computing power to an advanced combination of simulation, data analysis, and machine learning.

Getty Images is embracing artificial intelligence, starting with a way to help publishers pick photos. Today, the photo agency debuted a tool that uses AI to analyze a story and suggest photos that might go along with it depending on the text and content. The tool, called Panels, uses natural language processing--a term for how computers can learn to "read" human words, phrases and sentences--to then match a story based on keywords, images, captions and other criteria. Publishers also will then have access to custom filters and a self-improving algorithm to move around keywords or select images through a more human-driven process. Here's how it works: When someone enters in the URL for a story or copies and pastes in the text, Panels will analyze the words before suggesting people, places and things that appear in the story after weighing different options based on frequency and relevance.