NASA has released a mesmerizing new video of the moon set to Claude Debussy's famous piano composition, Clair de Lune. The stunning nature documentary-style footage uses high-resolution images from the Lunar Reconnaissance Orbiter to reveal an unprecedented look at the surface of Earth's only natural satellite. It follows the sun over the course of one lunar day, slowly panning over an endless landscape of craters and other surface features brought to life by elevation maps and image mosaics. The new visualization was created to accompany a performance of Claude Debussy's Clair de Lune by the National Symphony Orchestra Pops. It was performed at the Kennedy Center for the Performing Arts in Washington, DC, on June 1 and 2, 2018, led by conductor Emil de Cou, as part of a celebration of NASA's 60th anniversary.

In October, the largest self-driving car project backed by the British government wrapped up three years worth of testing aimed at getting autonomous vehicles onto roads by 2021. Many of the autonomous car and pod tests took place in Milton Keynes, a town built for cars that represents one of the fastest-growing city or town economies in the United Kingdom. Originally founded as a new "model town" in 1967, Milton Keynes is a city in all but name after having grown to 280,000 people in 50 years. But the same economic success means that Milton Keynes--built in a grid layout and suburban style--faces a number of growing pains that it's looking to ease with the help of autonomous vehicle technology. The recent UK Autodrive tests were designed to test the capabilities of both self-driving cars and smaller autonomous pod vehicles made by Coventry, UK-based Aurrigo, a division of RDM Group, with an eye toward easing traffic congestion and possibly even eliminating the need for cars in the city center.

Though it's often talked about as if it's a single continuous structure, China's legendary Great Wall is actually a series of stone fortifications that crawl across the country's changing landscape from the Korean border to the Gobi desert. Thousands of miles long and more than 2,000 years old in some places, as much as 30 percent of the wall "lies crumbling into ruins" as it is slowly reclaimed by the natural world, according to National Geographic. To reach some of the most vulnerable sections of the ancient wall –– deteriorating portions that people have been completely cut off from or that remain too dangerous to traverse --Chinese authorities have deployed a new tool: drones. The drones have allowed Chinese authorities to map and measure sections of the wall, offering precise data that is already being used to rehabilitate a structure that is widely recognized as one of mankind's greatest feats of engineering, the BBC reported in a video published this week. Data collected by the drones has helped workers build support structures for vulnerable sections of the wall, the BBC reported.

After a decade of stop-and-go development, Artificial Intelligence has now begun to provide real, tangible value to the business world. McKinsey published an 80-page report titled "Artificial Intelligence: The Next Digital Frontier?" which provides a comprehensive analysis of the value that Artificial Intelligence (AI) creates for businesses. The report points out that "wide application of Artificial Intelligence technology will bring great returns to businesses." This means that the disruptive nature of AI will continue to become more apparent in the future. Governments, enterprises, and developers should all be clear on this point. Currently, researchers and businesses are focusing on Artificial Intelligence systems such as robotics and automated transportation, virtual agents, and Machine Learning (including Deep Learning and the foundations of several recent advancements in AI technologies).

The US and China are in a race to become the AI superpower of the century. The perceived stakes are high: not only would the victor reap massive economic benefits,but it could also establish a new military edge. As Russian president Vladimir Putin phrased it last year, "Whoever becomes the leader in this sphere will become the ruler of the world." Not all experts agree--and most AI researchers don't see themselves in an arms race at all. But that hasn't stopped leaders in both countries from rapidly escalating their offensives.

Abstract: Photo-inducedprocesses are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of reach with contemporary approaches. Instead of expensive quantum chemistry during molecular dynamics simulations, we use deep neural networks to learn the relationship between a molecular geometry and its high-dimensional electronic properties. As an example, the time evolution of the methylenimmonium cation for one nanosecond is used to demonstrate that machine learning algorithms can outperform standard excited-state molecular dynamics approaches in their computational efficiency while delivering the same accuracy. Introduction Machine learning (ML) is revolutionizing the most diverse domains, like image recognition [1], playing board games [2], or society integration of refugees [3]. Also in chemistry, anincreasing range of applications is being tackled with ML, for example, the design and discovery of new molecules and materials [4, 5, 6]. In the present study, we show how ML enables efficient photodynamics simulations. Photodynamics is the study of photo-induced processes that occur after a molecule is exposed to light. Photosynthesis or DNA photodamage leading to skin cancer are only two examples of phenomena that involve molecules interacting with light [7, 8, 9, 10, 11]. The simulation of such processes has been key to learn structure-dynamicsfunction relationshipsthat can be used to guide the design of photonic materials, such as photosensitive drugs [12], photocatalysts [4] and photovoltaics [13, 14].

This paper reviews the reasons that Human-in-the-Loop is both critical for preventing widely-understood failure modes for machine learning, and not a practical solution. Following this, we review two current heuristic methods for addressing this. The first is provable safety envelopes, which are possible only when the dynamics of the system are fully known, but can be useful safety guarantees when optimal behavior is based on machine learning with poorly-understood safety characteristics. The second is the simpler circuit breaker model, which can forestall or prevent catastrophic outcomes by stopping the system, without any specific model of the system. This paper proposes using heuristic, dynamic safety envelopes, which are a plausible halfway point between these approaches that allows human oversight without some of the more difficult problems faced by Human-in-the-Loop systems. Finally, the paper concludes with how this approach can be used for governance of systems where otherwise unsafe systems are deployed.

I'm not sure if I should be feeling so comfortable in the apocalypse. That's the thought that keeps working its way through my mind as I play the first hours of Fallout 76 and its now-foundational rituals. As I leave the nuclear war refuge of the Vault. As I find ugly leather armor. As I get into a series of siege fights against giant mole rats. And as I begin to hoard anything and everything I suspect might be useful later.

The current UK government has made its vision for artificial intelligence use in the NHS very clear. It wants AI, data and innovation to "transform the prevention, early diagnosis and treatment of chronic diseases by 2030", with the UK to be "at the forefront of the use of AI and data in early diagnosis, innovation, prevention and treatment". Under this vision, AIs could ultimately become the first point of contact for the sick instead of a human doctor, could help healthcare professionals to diagnose medical conditions, and even monitor individuals' health by analysing data from their wearable devices or smart-home sensors. It's a huge ambition for a set of technologies that are still developing, and whose use is relatively restricted in the health service today. Can AI really make a difference to the future of the NHS? Certainly there are signs of a growing appetite in the health service for AI technology: around half of NHS trusts are now said to be investing in artificial intelligence in some form.

Government regulation is necessary to prevent harm. But regulation is also a blunt and slow-moving instrument that is easily subject to political interference and distortion. When applied to fast-moving fields like AI, misplaced regulations have the potential to stifle innovation and derail the enormous potential benefits that AI can bring in vehicle safety, improved productivity, and much more. We certainly do not want rules hastily cobbled as a knee-jerk response to a popular outcry against AI stoked by alarmists such as Elon Musk (who has urged U.S. governors to regulate AI "before it's too late"). To address this conundrum, I propose a middle way: that we avoid regulating AI research, but move to regulate AI applications in arenas such as transportation, medicine, politics, and entertainment.