To get a sense of the extent to which drones have captured the public imagination, look to the skies. In Folsom, Calif., more than 950 drones took to the skies earlier this month to create a glowing, real-world version of Time magazine's iconic cover, hovering 400 feet above the ground. "Up in the sky, I saw the future," a local resident told a local news station. In recent years, there have been no shortage of publicity-grabbing announcements involving unmanned aerial vehicles (UAVs)--just consider Amazon's headline-grabbing goal of drone-delivered packages. But reality is catching up with these long-stated aspirations and, through a combination of drone-friendly legislation and practical research, Virginia is poised to become a key player in determining how to make day-to-day drone operations a reality in a wide range of sectors.

Give us your feedback Thank you for your feedback. Parker's first day at work at the law firm Norton Rose Fulbright in Australia involved 1,000 conversations with potential clients. Even the most super-energetic young lawyer would normally manage only a fraction of that but Parker is, of course, a chatbot -- a computer program that simulates human conversation. The new recruit is a prime example of how law firms in Asia-Pacific are experimenting with artificial intelligence to improve efficiency. Chatbots, which use AI to answer simple questions from people wanting to learn more about a subject, are already being adopted in industries ranging from banking to medicine.

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This document contains the outcome of the first Human behaviour and machine intelligence (HUMAINT) workshop that took place 5-6 March 2018 in Barcelona, Spain. The workshop was organized in the context of a new research programme at the Centre for Advanced Studies, Joint Research Centre of the European Commission, which focuses on studying the potential impact of artificial intelligence on human behaviour. The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs. The document is made of short position statements and identification of challenges provided by each expert, and incorporates the result of the discussions carried out during the workshop. In the conclusion section, we provide a list of emerging research topics and strategies to be addressed in the near future.

Recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historical prejudices may lead to unfair decision policies. We connect these lines of work and study the residual unfairness that arises when a fairness-adjusted predictor is not actually fair on the target population due to systematic censoring of training data by existing biased policies. This scenario is particularly common in the same applications where fairness is a concern. We characterize theoretically the impact of such censoring on standard fairness metrics for binary classifiers and provide criteria for when residual unfairness may or may not appear. We prove that, under certain conditions, fairness-adjusted classifiers will in fact induce residual unfairness that perpetuates the same injustices, against the same groups, that biased the data to begin with, thus showing that even state-of-the-art fair machine learning can have a "bias in, bias out" property. When certain benchmark data is available, we show how sample reweighting can estimate and adjust fairness metrics while accounting for censoring. We use this to study the case of Stop, Question, and Frisk (SQF) and demonstrate that attempting to adjust for fairness perpetuates the same injustices that the policy is infamous for.

According to Gartner's hype cycle of emerging technologies, 2017; Deep Learning and Machine Learning have reached the peak of inflated expectations. Artificial General Intelligence (AGI) and Deep Reinforcement Learning are in the phase of innovation trigger. The sentiment over Artificial Intelligence (AI) is euphoric. Every technology firm is jumping on the AI first bandwagon. Companies like Google, Microsoft, Amazon, and Alibaba are pushing the frontiers.

The democratization of mass surveillance is upon us. Insanely cheap tools with the power to track individuals en masse are now available for anyone to use, as exemplified by a Forbes test of an Amazon facial recognition product, Rekognition, that made headlines last month. Jeff Bezos' behemoth of a business is seen by most as a consumer-driven business, not a provider of easy-to-use spy tech. But as revealed by the American Civil Liberties Union (ACLU) last week, Amazon Web Services (AWS) is shipping Rekognition to various U.S. police departments. And because Rekognition is open to all, Forbes decided to try out the service. Based on photos staff consensually provided, and with footage shot across our Jersey City and London offices, we discovered it took just a few hours, some loose change and a little technical knowledge to establish a super-accurate facial recognition operation.

It's easy to conceive of there being challenges for those drafting contracts relating to legal'risk' and liability. For example, what about the healthcare organisation that uses AI to analyse huge volumes of patient data (as well as data from other sources), to understand symptoms and provide suggested treatment options? For example, if something using AI'does what it does' and comes out with a new invention… without any human involvement… surely that would make it the legal'inventor'? But under UK patent law, an inventor is defined as a'person'. So how does this work if the inventor is a computer? Furthermore, as things currently stand, if that person discloses their invention to the state, they are given a 20 year'patent bargain' (a monopoly on that invention).

What's the first thing that comes to mind when you think about the soon-to-come widespread introduction of robots and artificial intelligence (AI)? Endless queues of people waiting to get unemployment benefits? Or the opposite: idleness and equality provided by the labour of mechanical slaves? In all likelihood the reality will be less flashy, though that doesn't mean we should ignore the social consequences of the technological changes taking place before our very eyes. The Fourth Industrial Revolution with its robotics, bio and nanotechnologies, 3D printing, Internet of things, genetics, and artificial intelligence is rapidly spreading across the world [1]. The coming technological changes will have direct consequences for a number of existing professions and promise in the very least to transform the labour market in developed countries.

As the planet continues to warm, climate change impacts are worsening. In 2016, there were 772 weather and disaster events, triple the number that occurred in 1980. Twenty percent of species currently face extinction, and that number could rise to 50 percent by 2100. And even if all countries keep their Paris climate pledges, by 2100, it's likely that average global temperatures will be 3 C higher than in pre-industrial times. But we have a new tool to help us better manage the impacts of climate change and protect the planet: artificial intelligence (AI).