Forests are having their moment. Because trees can vacuum carbon from the atmosphere and lock it away in wood, governments and businesses are embracing efforts to fight climate change by reforesting cleared areas and planting trees on a massive scale. But scientists have warned that the enthusiasm and money flowing to forest-based climate solutions threaten to outpace the science. Two papers published this week seek to put such efforts on a firmer footing. One study quantifies how much carbon might be absorbed globally by allowing forests cleared for farming or other purposes to regrow. The other calculates how much carbon could be sequestered by forests in the United States if they were fully “stocked” with newly planted trees. Each strategy has promise, the studies suggest, but also faces perils. To get a worldwide perspective on the potential of second-growth forests, an international team led by ecologist Susan Cook-Patton of the Nature Conservancy (TNC) assembled data from more than 13,000 previously deforested sites where researchers had measured regrowth rates of young trees. The team then trained a machine-learning algorithm on those data and dozens of variables, such as climate and soil type, to predict and map how fast trees could grow on other cleared sites where it didn't have data. > Can the forest regenerate naturally, or can we do something to help? > > Susan Cook-Patton , the Nature Conservancy A TNC-led team had previously calculated that some 678 million hectares, an area nearly the size of Australia, could support second-growth forests. (The total doesn't include land where trees might not be desirable, such as farmland and ecologically valuable grasslands.) If trees were allowed to take over that entire area, new forests could soak up one-quarter of the world's fossil fuel emissions over the next 30 years, Cook-Patton and colleagues report in Nature . That absorption rate is 32% higher than a previous estimate, based on coarser data, produced by the Intergovernmental Panel on Climate Change. But the total carbon drawdown is 11% lower than a TNC-led team estimated in 2017. The study highlights “what nature can do all on its own,” Cook-Patton says. And it represents “a lightning step forward” in precision compared with earlier studies, says geographer Matthew Fagan of the University of Maryland, Baltimore County, who was not involved in the work. But, Fagan adds, “Natural regrowth is not going to save the planet.” One problem: There is often little economic incentive for private landowners to allow forests to bounce back. Under current policies and market pricing, “nobody will abandon cattle ranching or agriculture for growing carbon,” says Pedro Brancalion, a forest expert at the University of São Paulo in Piracicaba, Brazil. And even when forests get a second life, they often don't last long enough to store much carbon before being cleared again. Fagan notes that even in Costa Rica, renowned as a reforestation champion for doubling its forest cover in recent decades, studies have found that half of second-growth forests fall within 20 years. Given such realities, some advocates are pushing to expand tree planting in existing forests. To boost that concept, a team of researchers at the U.S. Forest Service (USFS) quantified how many additional trees U.S. forests could hold. Drawing on a federal inventory, they found that more than 16% of forests in the continental United States are “understocked”—holding fewer than 35% of the trees they could support. Fully stocking these 33 million hectares of forest would ultimately enable U.S. forests to sequester about 18% of national carbon emissions each year, up from 15% today, the team reports in the Proceedings of the National Academy of Sciences . But for that to happen, the United States would have to “massively” expand its annual tree-planting efforts, from about 1 billion to 16 billion trees, says lead author Grant Domke, a USFS research forester in St. Paul, Minnesota. Cook-Patton says planting trees might make sense in some places, but natural regeneration, where possible, provides more bang for the buck. “For any given site,” she says, “we should always ask ourselves first: ‘Can the forest regenerate naturally, or can we do something to help?’”

Most ground-based observatories require a dark night sky to uncover answers to some of the most fundamental questions about the nature of our Universe. However, a number of companies and governments are in various stages of planning or deploying bright satellites in low-Earth orbit (or LEOsats) in greater numbers than ever before. These “megaconstellations” will fundamentally change astronomical observing at visible wavelengths. Nighttime images will be contaminated by streaks caused by the passage of Sun-illuminated satellites. If proposals calling for 100,000 or more LEOsats are realized, no combination of mitigations will be able to fully avoid the negative impact on astronomy. This threat comes at a time when new technology offers unprecedented scientific opportunities, all requiring access to dark skies. One example is the Vera C. Rubin Observatory, which is nearing completion. Its Legacy Survey of Space and Time (LSST) will soon offer a dramatic new view of the changing sky. Rubin Observatory will employ the 8.4-m Simonyi Survey Telescope and the 3200-megapixel LSST Camera to capture about 1000 images of the sky, every night, for 10 years. A single 30-s exposure will reveal distant objects that are about 40 million times fainter than those visible with the unaided eye. The observatory's combination of a large light-collecting area and field of view is unparalleled in the history of astronomy, which is why the project was the top ground-based priority for U.S. astronomers in the 2010 National Academies Decadal Survey of Astronomy and Astrophysics. LSST six-color images will contain data for about 20 billion ultrafaint galaxies and a similar number of stars, and will be used for investigations ranging from cosmological studies of the Universe to searches for potentially hazardous Earth-impacting asteroids. However, the discoveries anticipated from Rubin and other observatories could be substantially degraded by the deployment of multiple LEOsat constellations. The most exciting science to come out of current and planned astronomical facilities may be the discovery of types of objects and phenomena not yet observed or predicted. Such profound surprises have the potential to revolutionize our understanding of every field from exobiology to cosmology. Rubin Observatory's LSST, for example, opens the prospect of observing how ultrafaint objects change over time. It is precisely this kind of astronomy that is most at risk from image artifacts arising from LEOsat megaconstellations. These satellites scatter sunlight for several hours after sunset or before sunrise, are relatively close and bright, and thus can affect ground-based telescopes observing at visible wavelengths. Constellations in orbits well above 600 km will be illuminated by the Sun all night long. Astronomers worldwide are seeking ways to diminish the satellites' most damaging effects—the focus of a recent virtual workshop[*][1] sponsored by the U.S. National Science Foundation—and are collaborating with SpaceX (in particular, the Rubin Observatory), the first operator to launch a substantial constellation of LEOsats. SpaceX has shown that satellite operators can reduce reflected sunlight through satellite orientation, Sun shielding, and surface darkening. A joint effort to obtain higher-accuracy public data on the predicted location of individual satellites could help astronomers point their instruments to avoid some of the interference. Although all of these measures are helpful, there are no guarantees, and the research community is left to hope for good corporate citizenship. Future constellations owned and operated by foreign governments pose a different sort of challenge. Although there are international regulations covering radio-frequency interference, there are no such regulations in place for visible-frequency light pollution from space. Earth orbit is a natural resource without environmental protections, and we are now witnessing its industrialization. Currently there are about a thousand bright LEOsats, but that may be just the beginning. Proposals to expand telecommunications and data relay to serve new technologies like self-driving cars could lead to a 100-fold increase in the number of LEOsats in the next decade. The American Astronomical Society is working with astronomy stakeholders, commercial satellite operators, and international organizations to begin to forge policy on light pollution from space. It is unclear how long this will take and how effective it can be. What is clear is that without productive industry-observatory collaboration, voluntary operator compliance with best practices for mitigation, and subsequent regulatory action, we are slated to lose a clear view of the Universe and its secrets. [1]: #fn-1

Peggy answers some key questions surrounding AI (artificial intelligence) including: what is happening with research and development; what is happening in Washington; who is for AI; and who is trying to save the U.S. from the dark side of AI. She also discusses: The history of the federal government playing a big role in driving innovation. Reports that dig into AI research and development needs. The biggest considerations when it comes to the worker of the future and security. (09.22.20 - #687) IoT, Internet of Things, Peggy Smedley, artificial intelligence, machine learning, big data, digital transformation, cybersecurity, blockchain, 5G cloud, sustainability, future of work, podcast

Artificial Intelligence is transforming the world at a rapid and accelerating pace, offering huge potential, but also posing social and economic challenges. Human beings are naturally fearful of machines – this is a constant. Technological advancements tend to outpace cultural shifts. It has taken the shock of a global pandemic to accelerate the uptake of many technologies that have been around for at least a decade. Unsurprisingly, much of the public discussion on AI has focused on recent controversies around facial recognition, automated decision-making and exam algorithms.

Artificial Intelligence (AI) is often regarded as “Great and Powerful;” it can add tremendous value by transforming business workflows with faster, smarter decisions. At the same time, AI can be mysterious and even scary. In order to build trust, AI needs to be transparent and explainable: “out from behind the curtain” so to speak. As IBM’s recent study on AI Ethics found, corporate boards are looking to Data and Technology leaders to make that happen, and I couldn’t agree more. CDOs and CTOs can be instrumental in bringing forth both human value and human values in enterprise AI. Putting the human first To build trust in business AI, we must always put the value of the human first. This should happen at the data-provider level and the decision-maker level. At the provider level, building trust starts with data governance to ensure that the data itself can be trusted. In our organization, embedded within this is the IBM…

The novel coronavirus pandemic has fundamentally changed doctor-patient dynamics worldwide. On one hand, healthcare workers and doctors are tirelessly working to treat COVID patients, while on the other, elderly patients and those with chronic diseases who need routine medical check-ups are faced with increased vulnerability and the risk of contracting coronavirus during regular hospital visits. While all of this is going on, patients with milder symptoms are being encouraged to use telehealth platforms to alleviate the strain on hospital facilities. A part of the solution to all these problems is one word: telemedicine. Here's a look at what telemedicine is, and how Nvidia (NVDA) is supporting it as the broader digital health ecosystem.

Mexico's increasingly militarized crackdown of powerful drug cartels has left nearly 39,000 unidentified bodies languishing in the country's morgues – a grotesque symbol of the ever-burgeoning war on drugs and rampant violence. Investigative NGO Quinto Elemento Labs, in a recent report, found that an alarming number of people have been simply buried in common graves without proper postmortems, while others were left in funeral homes. The so-called war of drugs has claimed the lives of nearly 300,000 people over the last 14 years, while another 73,000 have gone missing. All the while, these cartels have yet to be designated formal terrorist organizations despite boasting well-documented arsenals of sophisticated weaponry to rival most fear-inducing militias on battlefields abroad. Just last month, reports surfaced that Mexico's Jalisco New Generation Cartel (CJNG) now possess bomb-toting drones – which The Drive's Warzone depicts as "small quadcopter-type drones carrying small explosive devices to attack its enemies."

In the breaking decade of technology, government agencies are quick to adopt new innovative measures. Automation and Artificial Intelligence (AI) are laying the path to achieve lofty productivity and decision-making. 'High value' work is the main focus of government agencies. For the purpose, robotic process automation and similar technologies are being embraced in order to decrease errors, improve compliance, and eliminate repetitive administration tasks. Robot process automation (RPA) is an application of technology that partially or fully automates human activities that are manual, rule-based, and repetitive.

Artificial Intelligence is a hot topic. Applications based on machine learning make the news on a near-daily basis. Smart police cameras steered by algorithms can register drivers holding cell phones with great precision. Algorithms can dynamically determine the real-time prices for taxi rides, hotel rooms, airplane seats, and so on. High-frequency traders are getting rich as they sleep by letting their secret algorithms do the work.

In June, a crisis erupted in the artificial intelligence world. Conversation on Twitter exploded after a new tool for creating realistic, high-resolution images of people from pixelated photos showed its racial bias, turning a pixelated yet recognizable photo of former President Barack Obama into a high-resolution photo of a white man. Researchers soon posted images of other famous Black, Asian, and Indian people, and other people of color, being turned white. Two well-known AI corporate researchers -- Facebook's chief AI scientist, Yann LeCun, and Google's co-lead of AI ethics, Timnit Gebru -- expressed strongly divergent views about how to interpret the tool's error. A heated, multiday online debate ensued, dividing the field into two distinct camps: Some argued that the bias shown in the results came from bad (that is, incomplete) data being fed into the algorithm, while others argued that it came from bad (that is, short-sighted) decisions about the algorithm itself, including what data to consider.