New technology is giving astronauts exploring distant worlds a helping hand. Scientists from NASA and the SETI Institute have developed a'smart glove' that lets astronauts control robots, specifically drones, through one-handed gestures. The innovation uses a micro-controller to read an array of sensors that capture even the smallest motion of the fingers and hands. The glove coincides with NASA's latest spacesuit design that aims to add more comfort and efficiency for astronauts as they explore the moon and Mars. Scientists from NASA and the SETI Institute have developed a'smart glove' that lets astronauts control robots, specifically drones, through one-handed gestures The smart glove is a prototype for a human-machine interface (HuMI) that would allow astronauts to wirelessly operate a wide array of robotic assets, including drones, via simple single-hand gestures.

Drone users in the UK must now sit an online test and pay a £9 annual fee or face a £1,000 fine after the launch of a mandatory national registration scheme on Tuesday. Owners are obliged to identify and label all drones by 30 November, and operators must pass a test about legal and safe usage before they can fly them. The Civil Aviation Authority estimates that about 130,000 people will have to pay and register by the end of the month. All drones weighing more than 250g, which encompasses virtually all but the smallest toys, must be registered and labelled with a unique licence number. This means they will have to be grounded to identify their owners, but in future it could be done remotely or while drones are in the air.

One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system. A key first step in reducing this uncertainty is to accurately classify cloud types at high spatial and temporal resolution. In this paper, we introduce Cumulo, a benchmark dataset for training and evaluating global cloud classification models. It consists of one year of 1km resolution MODIS hyperspectral imagery merged with pixel-width 'tracks' of CloudSat cloud labels. Bringing these complementary datasets together is a crucial first step, enabling the Machine-Learning community to develop innovative new techniques which could greatly benefit the Climate community. To showcase Cumulo, we provide baseline performance analysis using an invertible flow generative model (IResNet), which further allows us to discover new sub-classes for a given cloud class by exploring the latent space. To compare methods, we introduce a set of evaluation criteria, to identify models that are not only accurate, but also physically-realistic.

In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labelling effort among a large number of collaborators. For instance, this is being applied to the data acquired by the laureate Laser Interferometer Gravitational Waves Observatory (LIGO), in order to detect glitches which might hinder the identification of true gravitational-waves. The crowdsourcing scenario poses new challenging difficulties, as it deals with different opinions from a heterogeneous group of annotators with unknown degrees of expertise. Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting. However, GPs do not scale well to large data sets, which hampers their broad adoption in real practice (in particular at LIGO). This has led to the recent introduction of deep learning based crowdsourcing methods, which have become the state-of-the-art. However, the accurate uncertainty quantification of GPs has been partially sacrificed. This is an important aspect for astrophysicists in LIGO, since a glitch detection system should provide very accurate probability distributions of its predictions. In this work, we leverage the most popular sparse GP approximation to develop a novel GP based crowdsourcing method that factorizes into mini-batches. This makes it able to cope with previously-prohibitive data sets. The approach, which we refer to as Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR), brings back GP-based methods to the state-of-the-art, and excels at uncertainty quantification. SVGPCR is shown to outperform deep learning based methods and previous probabilistic approaches when applied to the LIGO data. Moreover, its behavior and main properties are carefully analyzed in a controlled experiment based on the MNIST data set.

As consumers, we experience the benefits of artificial intelligence in our lives as it seamlessly integrates into our daily activities. We are aware of the potential for this technology to transform our organizations in a similar fashion, but still struggle to identify where and how we can use it to generate business value. This has certainly been the trend for some time, but artificial intelligence is now a fundamental requirement for the success of your organization. It is no longer an option. Those that can leverage it will survive and thrive, while those that are unable or unwilling will fall far behind.

Google will not allow its artificial intelligence (AI) software to be used in weapons or unreasonable surveillance efforts under new standards for its business decisions in the nascent field, the Alphabet unit said on Thursday. The restriction could help Google management defuse months of protest by thousands of employees against the company's work with the U.S. military to identify objects in drone video. Google instead will seek government contracts in areas such as cybersecurity, military recruitment and search and rescue, CEO Sundar Pichai said in a blog post. "We want to be clear that while we are not developing AI for use in weapons, we will continue our work with governments and the military in many other areas," he said. Breakthroughs in the cost and performance of advanced computers have carried AI from research labs into industries such as defence and health in the last couple of years.

Plan Jericho has introduced Ai-Search – an artificial intelligence (Ai) prototype – to transform airborne search and rescue. The prototype came about after Air Commodore Darren Goldie challenged Jericho to find a way of using a detector on an aircraft to enhance search and rescue (SAR). Plan Jericho's Ai lead Wing Commander Michael Gan said Jericho saw the opportunity to use Ai to augment and enhance SAR. "The idea was to train a machine-learning algorithm and Ai sensors to complement existing visual search techniques. Our vision was to give any aircraft and other Defence platforms, including unmanned aerial systems, a low-cost, improvised SAR capability," Wing Commander Gan said.

Z Advanced Computing, Inc. (ZAC), an AI (Artificial Intelligence) software startup, is developing its Smart Home product line through a paid-pilot for smart appliances for BSH Home Appliances, the largest manufacturer of home appliances in Europe and one of the largest in the world. BSH Home Appliances Corporation is a subsidiary of the Bosch Group, originally a joint venture between Robert Bosch GmbH and Siemens AG. ZAC Smart Home product line uses ZAC Explainable-AI Image Recognition. ZAC is the first to apply Explainable-AI in Machine Learning. "You cannot do this with other techniques, such as Deep Convolutional Neural Networks," said Dr. Saied Tadayon, CTO of ZAC.

This past week, the Interior Department ordered the grounding of its drone fleet that was made in China or contained Chinese parts. This comes on the heels of similar actions taken by the Department of Homeland Security in May and the United States Army in 2017. While political pundits credit the ban to the Trump administration's policy initiatives against the Asian superpower, many cybersecurity analysts cite legitimate security concerns. As a result, there is a bipartisan bill pending, The American Security Drone Act of 2019, to ban all Federal agencies from using any Chinese-made aerial vehicles. As Senator Richard Blumenthal explains, "Like it or not, drones are our future. Without Congressional action, adversaries like China and Iran will use drone technology as tiny Trojan Horses to spy on our government, our critical infrastructure – even our hospitals and homes. This bill will ensure that we don't send China and others a gold-plated, flying invitation to steal our intellectual property, undermine our domestic technology, and spy on our communities."

An Israeli artificial intelligence startup with investments from a number of American companies including Microsoft has been linked to the biometric surveillance of Palestinians. AnyVision is an international tech company based in Israel that raised $78 million in June from an investment group including American tech giant Microsoft. One of their flagship products -- dubbed'Better Tomorrow' -- is a platform that leverages biometrics and facial recognition software to track objects and people on live video, including across independent camera feeds. NBC and Israeli news site Haaretz report that this technology is at the centre of a military surveillance operation focused in the West Bank at "at least 27 checkpoints", according to a statement from the Israeli Defence Forces from February. The aim of the operation is to "upgrade the crossings" and "deter terror attacks" using a network of 1,700 cameras featuring biometric and facial recognition capabilities.