The unmistakable glare of car headlights cutting through the darkness on roads at night could soon vanish thanks to new self-driving technology. Car giant Ford has revealed it is testing the ability of its autonomous vehicles to navigate in pitch black conditions without any headlights. The cars use laser sensing technology, called LiDAR, to map the vehicles' surroundings with infrared light, allowing them to steer along even the most winding country roads. Ford has tested its self-driving cars on winding country roads in pitch darkness to show that the vehicles can navigate at night without any headlights. Ford said it is essential its self-driving vehicles are able to stay on the road at times of the day when they are not able to use its camera technology as there is not enough light.

Drones are opening up a world of new possibilities for consumers. They may have started off as aerial cameras, but now, as the technology has matured, they're finding applications far outside the realm of photography. Nowadays we've got drones that deliver goods, fight deforestation, and even patrol our coastlines to warn swimmers of nearby sharks -- but this is just the beginning. In the not so distant future, drones will also be used to save lives. Staring very soon, DJI, the world's largest drone manufacturer, will be supplying drones to the European Emergency Number Association for use in high-impact situations like rescue missions -- a deal that could fundamentally change the way first responders operate.

Campaigning is often based on feeling or experience by marketeers, but the speed of digital developments are forcing businesses to rapidly adapt to the new needs of consumers created by digitization. Of course, marketeers can't leave their data out of the equation nowadays, however this remains a struggle as this often calls for allocating expensive resources such as data scientists. Luckily, technology is catching up on the smart brains of data scientists. So what does this mean for the future of campaigning? And how can big data, machine learning and artificial intelligence be used to improve campaigns?

Everyone's excited for self-driving cars that can ferry us from home to work and back again, but the most recent success in autonomous vehicles is a bit more business-based. A squad of a dozen or so self-driving cargo trucks just completed a demonstration in Europe that covered over a thousand miles and crossed several borders, all with minimal human help. The trucks were all part of the European Truck Platooning Challenge, an event sponsored by the Dutch government, and which is aimed at making fleets of smart, self-driving cargo trucks a practical business reality. "Platooning" is essentially just close, connected, fleet-based driving where follower trucks connect to the vehicle ahead of them with Wi-Fi and maintain a very tight driving formation that wouldn't be safe for more fallible human drivers. A number of different companies participated, each using trucks and technology of its own.

A "new" Rembrandt has been unveiled in Amsterdam, but it's not a long lost dusty relic that was found in someone's loft--it was created by data analysts and computers. The portrait of a man in a black hat is the result of 18 months work by art historians, data scientists, developers, 3-D print technicians, and organizations like Microsoft, Delft University of Technology, the Mauritshuis in The Hague, and Amsterdam's Rembrandt House Museum. It consists of more than 148 million pixels, based on 168,263 painting fragments from Rembrandt's output. The initiative is the brainchild of Bas Korsten, executive creative director at ad agency JWT Amsterdam, and was created for Dutch financial services giant ING. Korsten says ING approached the agency with a brief to "find a way to bring their innovative spirit to their sponsorship of Dutch art and culture in a way that would get people thinking."

In a major breakthrough for artificial intelligence, a computing system developed by Google researchers in Great Britain has beaten a top human player at the game of Go, the ancient Eastern contest of strategy and intuition that has bedeviled AI experts for decades. Machines have topped the best humans at most games held up as measures of human intellect, including chess, Scrabble, Othello, even Jeopardy!. But with Go--a 2,500-year-old game that's exponentially more complex than chess--human grandmasters have maintained an edge over even the most agile computing systems. Earlier this month, top AI experts outside of Google questioned whether a breakthrough could occur anytime soon, and as recently as last year, many believed another decade would pass before a machine could beat the top humans. But Google has done just that.

Russian entrepreneur and venture capitalist Yuri Milner, founder of investment firm Digital Sky Technologies Ltd (DST Global), has made a new investment in a tech company Horizon Robotics, a China-based startup focused on artificial intelligence (AI). The size of the investment venture was not disclosed. Horizon Robotics will use the latest proceeds on research and development, including team expansion, reported the China Money Network. The startup was established in July 2015 by Dr. Kai YU, the founder and former head of Institute of Deep Learning (IDL) at Baidu. Horizon Robotics previously received seed funding from Hillhouse Capital, Morningside Ventures, GSR Ventures, Sequoia Capital, among others.

Yesterday, The Guardian published a provocative opinion piece titled, "Are Robots Going To Steal Your Job? Probably." At first glance, the author's pessimism would seem justified. From robotic gardeners and farmers to robotic pizza delivery services, it seems like every day robots make new forays into jobs traditionally done by humans. But pause to consider technology in historical perspective. Pessimism about new technologies is not new.

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz 63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.

Many important forms of data are stored digitally in XML format. Errors can occur in the textual content of the data in the fields of the XML. Fixing these errors manually is time-consuming and expensive, especially for large amounts of data. There is increasing interest in the research, development, and use of automated techniques for assisting with data cleaning. Electronic dictionaries are an important form of data frequently stored in XML format that frequently have errors introduced through a mixture of manual typographical entry errors and optical character recognition errors. In this paper we describe methods for flagging statistical anomalies as likely errors in electronic dictionaries stored in XML format. We describe six systems based on different sources of information. The systems detect errors using various signals in the data including uncommon characters, text length, character-based language models, word-based language models, tied-field length ratios, and tied-field transliteration models. Four of the systems detect errors based on expectations automatically inferred from content within elements of a single field type. We call these single-field systems. Two of the systems detect errors based on correspondence expectations automatically inferred from content within elements of multiple related field types. We call these tied-field systems. For each system, we provide an intuitive analysis of the type of error that it is successful at detecting. Finally, we describe two larger-scale evaluations using crowdsourcing with Amazon's Mechanical Turk platform and using the annotations of a domain expert. The evaluations consistently show that the systems are useful for improving the efficiency with which errors in XML electronic dictionaries can be detected.