NASA's InSight lander is struggling to retain power as it explores Mars as dust is accumulating on its solar panels, which could result in its mission ending within the next year. The American space agency announced Tuesday that 80 percent of the solar panels are obstructed by dust, leaving less than 700 watt-hours of power per Martian day. It was hoped that winds would clean the lander and allow it to continue to collect seismic data on its extended mission, which was supposed to last until the end of 2022. NASA attempted to remove dust from the top on InSight earlier this month using the lander's robotic arm, which trickled sand near one solar panel with the hopes wind would carry off the panel's dust. NASA's InSight lander is struggling to retain power as it explores Mars due to Martian dust accumulating on its solar panels, which could result in its mission within the next year The death of InSight was discussed at a June 21 meeting of NASA's Mars Exploration Program Analysis Group, SpaceNews reports.

Farming robots that can move autonomously in an open field or greenhouse promise a cleaner, safer agricultural future. But there are also potential downsides, from the loss of much-needed jobs to the safety of those working alongside the robots. To ensure that the use of autonomous robots on farms creates more benefits than losses, a process of responsible development is required. Society as a whole needs to be involved in setting the trajectories for future farming. We are part of a project called Robot Highways, which is currently demonstrating multiple uses for autonomous robots made by Saga Robotics on a fruit farm in south-east England.

The development and adoption of advanced technologies including smart automation and artificial intelligence has the potential not only to raise productivity and GDP growth but also to improve well-being more broadly, including through healthier life and longevity and more leisure. Alongside such benefits, these technologies also have the potential to reduce disruption and the potentially destabilizing effects on society arising from their adoption. Tech for Good: Smoothing disruption, improving well-being (PDF–1MB) examines the factors that can help society achieve such benefits and makes a first attempt to calculate the impact of technology adoption on welfare growth beyond GDP. Our modeling suggests that good outcomes for the economy overall and for individual well-being come about when technology adoption is focused on innovation-led growth rather than purely on labor reduction and cost savings through automation. This needs to be accompanied by proactive transition management that increases labor market fluidity and equips workers with new skills. Technology for centuries has both excited the human imagination and prompted fears about its effects. Today's technology cycle is no different, provoking a broad spectrum of hopes and fears.

Artificial intelligence (AI) is used in a wide variety of products and services, including maps embedded on our smart phones and "chat bots" that help answer our questions on websites. Many hope that AI will transform our economy in ways that drive growth, similar to how steam engines did in the late 19th century and electricity did in the early 20th century. But it is hard to imagine that maps on smart phones, chatbots, and other existing AI-enabled services will drive the type of economic growth we saw from stream and electricity. What we need to see are some dramatic new AI-enabled products and services that transform our way of life--in short, we are waiting for an AI "killer app." Autonomous vehicles (AVs)--vehicles that accelerate, brake, and turn on their own, requiring little or no input from a human driver--may be such a killer app that transforms our economy significantly.

A British-built solar powered drone with a 115ft wingspan that can stay in the air for over a year will be an alternative to low Earth orbit satellites, its developers claim. PHASA-35 is a cutting edge drone being developed by BAE systems at their facility in Warton, Lancashire that can fly about at 70,000ft above the surface for 20 months. It harnesses power from the Sun to stay airborne, charging a bank of small batteries during the day to keep it flying overnight, allowing for longer operations. The 150kg drone is able to carry a payload of up to 15kg including cameras, sensors and communications equipment to allow troops to talk to each other or provide internet access to rural locations during a natural disaster or emergency. BAE systems say it will be available by the middle of the decade and provide a'persistent and affordable alternative to satellite technology.'

In this chapter, we will give comprehensive examples of applying RL in optimizing the physical layer of wireless communications by defining different class of problems and the possible solutions to handle them. In Section 9.2, we present all the basic theory needed to address a RL problem, i.e. Markov decision process (MDP), Partially observable Markov decision process (POMDP), but also two very important and widely used algorithms for RL, i.e. the Q-learning and SARSA algorithms. We also introduce the deep reinforcement learning (DRL) paradigm and the section ends with an introduction to the multi-armed bandits (MAB) framework. Section 9.3 focuses on some toy examples to illustrate how the basic concepts of RL are employed in communication systems. We present applications extracted from literature with simplified system models using similar notation as in Section 9.2 of this Chapter. In Section 9.3, we also focus on modeling RL problems, i.e. how action and state spaces and rewards are chosen. The Chapter is concluded in Section 9.4 with a prospective thought on RL trends and it ends with a review of a broader state of the art in Section 9.5.

Almost three-quarters (72%) of IT leaders are already using edge computing to provide innovative services, according to Intel. From ultra-connected autonomous cars to low-latency AR, VR and gaming: to remain competitive in the digital age, businesses will have little choice but to fully embrace the new opportunities that come with the deployment of edge computing, according to a new report published by Intel. Almost three-quarters (72%) of IT leaders are already using edge computing to provide innovative services, according to the chip giant, whether that is to create new products, open new revenue streams or boost efficiencies. "Businesses can no longer afford to ignore the edge," says the report, stressing the technology's potential to better access and understand the unprecedented amounts of data that are generated over networks every second. As the name suggests, edge technologies consist of moving the hosting of computer services to the edge of the network, so that the process happens as close as possible to the people that use the service, which significantly reduces latency.

When NVIDIA purchased mobile-chip designer Arm Holdings from SoftBank last year, NVIDIA CEO Jensen Huang made the bold prediction that in the years ahead, there will be trillions of artificial intelligence (AI)-enabled Internet of Things (IoT) devices. Regardless of whether that holds true, it is safe to say the growth of IoT devices is exploding. All those devices will require power sources, and the way Josiah Hester sees it, that's problematic for the environment and society. "When I see the'trillion' number, I see a trillion dead batteries, basically," says Hester, an assistant professor of computer engineering at Northwestern University. "There's piles of batteries in landfills in China and elsewhere sitting there unrecycled; or they're put in furnaces and melted down, which is not a carbon-neutral event."

Fighting climate change is both an urgent global imperative and a massive business opportunity. Climate change is the most pressing threat that the human species faces today. Artificial intelligence is the most powerful tool that humanity has at its disposal in the twenty-first century. Can we deploy the second to combat the first? A group of promising startups has emerged to do just that. Both climate change and artificial intelligence are sprawling, cross-disciplinary fields. Both will transform literally every sector of the economy in the years ahead. There is therefore no single "silver bullet" application of AI to climate change. Instead, a wide range of machine learning use cases can help in the race to decarbonize our world. Nearly every major activity that humanity engages in today contributes to our carbon footprint to some extent: building things, moving things, powering things, eating things, computing things.

Artificial Intelligence is considered one of the most revolutionary developments in the history of technology. Within a few years, the world has already witnessed the transformative capabilities of this tech. Not to our surprise, AI is already driving several innovations and powering some of the most cutting-edge everyday solutions. Already, a captivating conversation is taking place about the future of artificial intelligence and what it will/should mean for humanity. There are stirring controversies where the world's leading experts disagree such as AI's future impact on the job market; what will happen if human-level AI will be developed, will it lead to an intelligence explosion, and whether we should welcome or fear this advancement.