BEIJING – It can crack your smartphone password in seconds, rip personal data from call and messaging apps, and peruse your contact book. The Chinese-made XDH-CF-5600 scanner -- or "mobile phone sleuth," as sales staff described it when touting its claimed features -- was one of hundreds of surveillance gadgets on display at a recent police equipment fair in Beijing. The China International Exhibition on Police Equipment is something of a one-stop shop for China's police forces looking to arm up with the latest in "black tech" -- a term widely used to refer to cutting-edge surveillance gadgets. The fair underscores the extent to which China's security forces are using technology to monitor and punish behavior that runs counter to the ruling Communist Party. That sort of monitoring -- both offline and online -- is stoking concerns from human rights groups about the development of a nationwide surveillance system to quell dissent.

Yesterday, Amazon's quiet Rekognition program became very public, as new documents obtained by the ACLU of Northern California showed the system partnering with the city of Orlando and police camera vendors like Motorola Solutions for an aggressive new real-time facial recognition service. Amazon insists that the service is a simple object-recognition tool and will only be used for legal purposes. But even if we take the company at its word, the project raises serious concerns, particularly around racial bias. Facial recognition systems have long struggled with higher error rates for women and people of color -- error rates that can translate directly into more stops and arrests for marginalized groups. And while some companies have responded with public bias testing, Amazon hasn't shared any data on the issue, if it's collected data at all. At the same time, it's already deploying its software in cities across the US, its growth driven by one of the largest cloud infrastructures in the world.

Machine learning, a popular subfield of artificial intelligence that is revolutionizing everything from legal research to medical diagnostics, depends on three major parts: a model, a dataset, and the hardware that it's backed by. So how do researchers, startups and companies evaluate its overall effectiveness? Options were limited until the recent formation of MLPerf, a consortium of industry and academic partners including Google, Intel, Baidu, Harvard University, Stanford University and the University of Toronto, who are working together to offer a new benchmark suite to evaluate machine learning (ML) performance, from speed to system cost and power efficiency. "Current benchmark suites give some basic numbers to say how well these benchmarks perform on certain hardware, but do not provide any insight into why these applications perform one way or another," says Gennady Pekhimenko, an assistant professor in the department of computer and mathematical sciences at U of T Scarborough and the tri-campus graduate department of computer science. "To know which design decision is bad or not for ML applications, you want to have some representative reference model," he says.

Norms allow system designers to specify the desired behaviour of a sociotechnical system. In this way, norms regulate what the social and technical agents in a sociotechnical system should (not) do. In this context, a vitally important question is the development of mechanisms for monitoring whether these agents comply with norms. Proposals on norm monitoring often assume that monitoring has no costs and/or that monitors have unlimited resources to observe the environment and the actions performed by agents. In this paper, we challenge this assumption and propose the first practical resource-bounded norm monitor. Our monitor is capable of selecting the resources to be deployed and use them to check norm compliance with incomplete information about the actions performed and the state of the world. We formally demonstrate the correctness and soundness of our norm monitor and study its complexity. We also demonstrate in randomised simulations and benchmark experiments that our monitor can select monitored resources effectively and efficiently, detecting more norm violations and fulfilments than other tractable optimization approaches and obtaining slightly worse results than intractable optimal approaches.

Scientists are working on a kickass new twist to the classic buddy cop movie genre. Get this: cyberterrorist Marcus Hurricane is going to walk free unless police detective Rick Danger can place him at the scene of the crime. But all he has to go on are some grainy security camera images, and he can't quite make out Hurricane's signature badass face scars. Enter: detective Danger's trusty AI cyborg sidekick, Sparky. Together, they have what it takes to save the day. But researchers did recently determine that, when it comes to difficult facial recognition tasks, a trained professional teamed up with an AI sidekick is better than a team of two human pros –or even an AI algorithm on its own.

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide you a one-stop source of late-breaking news to help you keep abreast of this fast-paced ecosystem. We're working hard on your behalf with our extensive vendor network to give you all the latest happenings. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

The developers of PlayerUnknown's Battlegrounds haven't been shy about accusing Epic Games of riding their bandwagon with Fortnite, and now they're taking legal action. We've asked Epic for comment, but it's safe to say the situation is... complicated. There's little doubt that Fortnite's mode was designed in response to PUBG's success. Fortnite was initially unveiled as a single-player game (the Save the World mode), but it tacked on Battle Royale in September 2017, about half a year after PUBG reached Steam's Early Access. And it's hard to deny certain similarities beyond just the basic concept of 100 players forced to gather resources and fight in an ever-shrinking safe zone.

"Loot boxes are like scratch-off cards: you open one out of curiosity, get a little prize, think'darn, maybe next time,' and then it just turns into a habit," says Brian. "I got a big prize with my first $20 and thought, 'Hey, maybe I'll get something good again,' and spent another $5 next week, and then $5 more. It's a disturbingly easy trap to fall into." Brian (not his real name), a 25-year-old American Reddit user who responded to a Guardian call-out, is one of millions of players who buy "loot boxes", lucky-dip boxes that cost real money and yield random virtual rewards. Loot boxes have attracted controversy and comparisons to gambling in recent months, prompting countries including Belgium and the Netherlands to determine that their inclusion in popular games such as Fifa, Overwatch and Final Fantasy: Brave Exvius contravenes local gambling legislation. Now, politicians and gambling-awareness organisations in the UK are calling for regulation, too.

Artificial intelligence has seen a resurgence of late in the cultural consciousness. Doomsayers worry AI will prove an existential threat to humanity, futurists wonder how we'll integrate aware algorithms into our social fabric, and optimists see further develops as the path to unimaginable human prosperity. But it is often unclear which "AI" is being discussed in any given debate or hot take. The umbrella term can signify artificial general intelligence, also called "strong AI," essentially a program that could perform cognitive tasks similar to, or beyond those of, people. It is also completely theoretical and conclusions about it are, at best, guesswork.

Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants regulated by the Environmental Protection Agency in the United States. There is strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of mortality and hospitalization. Large scale epidemiological studies on the health effects of PM$_{2.5}$ provide the necessary evidence base for lowering the safety standards and inform regulatory policy. However, ambient monitors of PM$_{2.5}$ (as well as monitors for other pollutants) are sparsely located across the U.S., and therefore studies based only on the levels of PM$_{2.5}$ measured from the monitors would inevitably exclude large amounts of the population. One approach to resolving this issue has been developing models to predict local PM$_{2.5}$, NO$_2$, and ozone based on satellite, meteorological, and land use data. This process typically relies developing a prediction model that relies on large amounts of input data and is highly computationally intensive to predict levels of air pollution in unmonitored areas. We have developed a flexible R package that allows for environmental health researchers to design and train spatio-temporal models capable of predicting multiple pollutants, including PM$_{2.5}$. We utilize H2O, an open source big data platform, to achieve both performance and scalability when used in conjunction with cloud or cluster computing systems.