While AFR tech has been trialled by a number of UK police forces, this appears to be the first time it has led to an arrest. South Wales Police didn't provide details about the nature of the arrest, presumably because it's an ongoing case. Back in April, it emerged that South Wales Police planned to scan the faces "of people at strategic locations in and around the city centre" ahead of the UEFA Champions League final, which was played at the Millennium Stadium in Cardiff on June 3. On May 31, though, a man was arrested via AFR. "It was a local man and unconnected to the Champions League," a South Wales Police spokesperson told Ars.

How can we ensure that artificial intelligence provides the greatest benefit to all of humanity? By that, we don't necessarily mean to ask how we create AIs with a sense of justice. That's important, of course--but a lot of time is already spent weighing the ethical quandaries of artificial intelligence. How do we ensure that systems trained on existing data aren't imbued with human ideological biases that discriminate against users? Can we trust AI doctors to correctly identify health problems in medical scans if they can't explain what they see?

The terrifying faces may look like creatures from a horror movie, but these digital images were actually generated by artificial intelligence (AI). Pix2pix project has unleashed a new tool that analyzes portraits and fills them in with colors and textures using a technique called generative adversarial networks (GANs). During the process, the system determines if its result match the sketch and will keep repeating the generation process until its own passes as'real' – regardless of how nightmarish the results may look. The terrifying faces may look like creatures from a horror movie, but these digital images were actually generated by artificial intelligence (AI). Users are presented with an input box and an output box and are prompted to draw a face in input, select process and in seconds, the AI will reveal its version of the sketch.

Artificial Intelligence (AI) is making headlines as the hottest technology on the market for businesses. Many organizations are seeking ways to incorporate it into their operations. However, one area that shouldn't go full-throttle with AI is customer service. People like to interact with other people, so businesses need to create a blended model of AI and the human element. We used to have a milkman in England who delivered milk to our doorstep every day.

Did you have a good, relaxing break over the summer? Are you refreshed and re-energised, looking forward to a new start, a new you and brushing up on your data analysis skills? If so, I've thrown together a collection of a few excellent (and free!) statistics eBooks for your Kindle to sharpen up your stats while you're on the long commute to work. Just try not to read them while driving! These books require different levels of existing knowledge, and while some are for early-stage data scientists others are for more hard-core physicists and mathematicians.

In the world's wealthiest neighbourhoods, artificial intelligence (AI) systems are starting to steer self-driving cars down the streets, and homeowners are giving orders to their smart voice-controlled speakers. But the AI revolution has yet to offer much help to the 3 billion people globally who live in poverty. That discrepancy lies at the heart of a meeting in Geneva, Switzerland, on 7–9 June, grandly titled the AI for Good Global Summit. The meeting of United Nations agencies, AI experts, policymakers and industrialists will discuss how AI and robotics might be guided to address humanity's most enduring problems, such as poverty, malnutrition and inequality. Development agencies are buzzing with ideas, although only a few have reached the stage of pilot experiments. But scientists caution that the rise of AI will also bring societal disruption that will be hard to foresee or manage, and that could harm the world's most disadvantaged.

In March this year, PWC released a report saying that 10 million UK jobs are at risk of being replaced by AI within 15 years. Prior to this in November 2016, the Bank of England said that AI posed a risk to almost half those employed in the UK and that a "third machine age" would hollow out the labour market, widening the gap between rich and poor. In a recent report the McKinsey Global Institute said that by 2025 AI will do the jobs of 140m knowledgeable and skilled workers globally. Let's not forget the truck drivers. In his speech at Harvard University recently, Mark Zuckerberg said that his generation will have to deal with "tens of millions" of jobs replaced by self-driving cars and trucks. In late May 2017, the International Transport Forum published a report warning that of the 6.4 million professional drivers that are projected to be needed in the US and Europe by 2030, that 4.4 million will be replaced by self-driving trucks.

National Electric Vehicle Sweden (NEVS), the company that discontinued the Saab name last June, debuted its InMotion electric level 5 autonomous car concept at CES Asia that's essentially a modular room on wheels. There aren't even any dashboard controls: Occupants adjust the seating arrangements, lighting and environmental settings with a paired app. A concept for the self-driving living/working space of the future has arrived. With its plastic rhomboid shape and wheel pods, the InMotion looks the part of a forward-looking concept car. Even the windows are intended to operate as displays, maximizing workspace.

We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression. Driven by the idea of using the kernel to explicitly model user-item similarities, we formulate the GP in a way that allows the incorporation of low-rank matrix factorisation, arriving at our model, the Tucker Gaussian Process (TGP). Consequently, TGP generalises classical Bayesian matrix factorisation models, and goes beyond them to give a natural and elegant method for incorporating side information, giving enhanced predictive performance for CF problems. Moreover we show that it is a novel model for regression, especially well-suited to grid-structured data and problems where the dependence on covariates is close to being separable.

Active learning algorithms propose which unlabeled objects should be queried for their labels to improve a predictive model the most. We study active learners that minimize generalization bounds and uncover relationships between these bounds that lead to an improved approach to active learning. In particular we show the relation between the bound of the state-of-the-art Maximum Mean Discrepancy (MMD) active learner, the bound of the Discrepancy, and a new and looser bound that we refer to as the Nuclear Discrepancy bound. We motivate this bound by a probabilistic argument: we show it considers situations which are more likely to occur. Our experiments indicate that active learning using the tightest Discrepancy bound performs the worst in terms of the squared loss. Overall, our proposed loosest Nuclear Discrepancy generalization bound performs the best. We confirm our probabilistic argument empirically: the other bounds focus on more pessimistic scenarios that are rarer in practice. We conclude that tightness of bounds is not always of main importance and that active learning methods should concentrate on realistic scenarios in order to improve performance. Key words and phrases: Active Learning, Learning Theory, Generalization, Maximum Mean Discrepancy, Discrepancy.