More than a decade after the improvised explosive device became the scourge of the wars in Iraq and Afghanistan, the Pentagon is battling another relatively rudimentary device that threatens to wreak havoc on American troops: the drone. Largely a preoccupation of hobbyists and experimenting companies, the vehicles are beginning to become a menace on the battlefield, where their benign commercial capabilities have been transformed into lethal weapons and intelligence tools. Instead of delivering packages, some have been configured to drop explosives. Instead of inspecting telecommunications towers, others train their cameras to monitor troops and pick targets. Instead of spraying crops, they could spread toxic gas, commanders worry.

Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, many different types of spatially localized climate patterns are of interest including hurricanes, extra-tropical cyclones, weather fronts, and blocking events among others. Existing labeled data for these patterns can be incomplete in various ways, such as covering only certain years or geographic areas and having false negatives. This type of climate data therefore poses a number of interesting machine learning challenges. We present a multichannel spatiotemporal CNN architecture for semi-supervised bounding box prediction and exploratory data analysis. We demonstrate that our approach is able to leverage temporal information and unlabeled data to improve the localization of extreme weather events. Further, we explore the representations learned by our model in order to better understand this important data. We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change. The dataset is available at extremeweatherdataset.github.io and the code is available at https://github.com/eracah/hur-detect.

One of the few bright spots this past year was supposed to be the revival of journalism. And to be sure, it's been a great time for muckraking, with newsrooms bringing home scoop after scoop on the Trump administration. Subscriptions to everything from the New York Times to Mother Jones are up. And for the first time in decades, trust in news media is rising too: Today, 54 percent of the public have confidence in journalists to tell the truth, while only 36 percent trust the president. Here's where the story turns more complicated.

Casinos are now adding skill-based video games to traditional games of chance. It's part of an effort by casinos to attract more millennials. Chicago-based Rush Street Interactive tells The Associated Press it has received approval from New Jersey regulators to let patrons bet online on the outcome of virtual computer-generated sporting "events," including soccer, horse, dog and vehicle racing. The technology exists in some Nevada casinos and is widely used throughout Europe, but this is the first time it will be available over the internet in the U.S. The site and mobile app launched Wednesday. Technology company Inspired Entertainment created the product, which debuts shortly before a U.S. Supreme Court ruling that could legalize real-world sports betting in the U.S. Chicago-based Rush Street Interactive recently received approval from New Jersey regulators to let patrons bet online on the outcome of virtual computer-generated sporting "events," including soccer, horse, dog and vehicle racing.

On November 7, China announced plans to open an unmanned police station powered by artificial intelligence (AI) in Wuhan, one of its capital cities. The AI police station will likely focus on vehicle- and driver-related issues, which makes it more analogous to an American Department of Motor Vehicles (DMV) than a precinct (sorry, Robocop), but the decision to build it is still right inline with China's plans to be a world leader in AI by 2030. According to a report from the Chinese financial paper Caijing Neican, the futuristic station will offer simulated driver examinations and provide registration services. Cutting-edge facial-recognition technology developed by Tencent will identify citizens within the station. The idea is that this will eliminate the need for users to sit at stations for long periods of time, sign up for accounts, or download apps -- the AI will access all pertinent information as soon as it sees the person's face.

Support for the development and promotion of Artificial Intelligence is born from the deepest roots of state power in China that defined, for the new qualitative leap of that society, Innovation as its axis and Science and Technology as its sustenance. Li Keqiang, the Prime Minister, summed it up by stating that "science and technology change the world, innovation forms the future". The AI is expressly included. Xi Jinping, the President of China, described it this way: "We need to build an innovative world economy to generate new drivers of growth. Innovation holds the key to fundamentally unleashing the growth potential. The new round of scientific and industrial revolution with Internet at its core is gathering momentum, and new technologies such as artificial intelligence and virtual reality are developing by leaps and bounds. The combination of the virtual economy and the real economy will bring revolutionary changes to our way of work and way of life".

It was just a few weeks ago that Sophia, an artificial intelligence (AI)-powered humanoid robot developed by Hong Kong-based Hanson Robotics, was given honorary citizenship by Saudi Arabia. While AI is making huge inroads into our day-to-day life, was this something expected to happen so soon? Nevertheless, the success of Sophia as well as other humanoids in activities ranging from shop floor management to complex jobs--sometimes doing tasks better than humans--is a testimony to the disruptions happening with technological advancements. Meanwhile, even though the popular perception is that AI is only going to be a job killer, reports also say that 2020 onwards, AI will start adding more jobs than it would take away. According a report by research firm Gartner, Inc., AI will create 2.3 million new jobs while eliminating only 1.8 million jobs in 2020.

Machine learning jobs are supposed to be the big new thing in financial services. After all, Goldman Sachs has created an elite new AI team, J.P. Morgan's assigned ex-credit trader Samik Chandarana to develop machine learning strategies and has already unleashed LOXM, a new self-teaching trading algorithm, and UBS CEO Sergio Ermotti says 30% of banking jobs are due to dissolve because of this kind of automation in the next 10 years. It might seem therefore that you should be positioning yourself now to chase machine learning jobs (even though Bank of America CTO Catherine says you're already too late). But what if the finance jobs of the future are a lot less exciting than the finance jobs of the past? This is a possibility raised by Saeed Amen in a new blog post.

The science-fiction writer Robert Heinlein once wrote, "Each generation thinks it invented sex." He was presumably referring to the pride each generation takes in defining its own sexual practices and ethics. But his comment hit the mark in another sense: Every generation has to reinvent sex because the previous generation did a lousy job of teaching it. In the United States, the conversations we have with our children about sex are often awkward, limited, and brimming with euphemism. At school, if kids are lucky enough to live in a state that allows it, they'll get something like 10 total hours of sex education.1

The government's latest attempt to tackle Britain's poor productivity record focused on extra funding for artificial intelligence, skills and technology, as the chancellor introduced measures to boost economic growth. Philip Hammond said there would be an expansion in the national productivity investment fund which he launched last year. The fund would rise from £23bn to £31bn to help kickstart improvements in efficiency levels across the UK. Productivity is an economic measure of the efficiency of a workforce. It typically measures the level of output per hour of work, or per worker.