COVID-19 researchers are expected to begin using artificial intelligence (AI) and machine learning to data-mine new clues and insights regarding COVID-19. The Office of Science and Technology Policy issued a call to action for researchers to start using this technology to sort through the scholarly data on the topic using a newly created database. Major healthcare organizations would like a better understanding of how COVID-19 originated and how it spreads to better develop a potential treatment. AI could help in this regard, as this technology is capable of scanning the 29,000 or so scholarly articles on the subject much faster than a human can. This could lead to the discovery of new clues and insights that otherwise would be near impossible for a person to find.

When a precision-guided artillery projectile exploded an enemy target from 64km (39.8 miles) away in the Arizona desert during a recent live-fire exercise, the Army took a new step toward redefining land-attack tactics and paving the way toward a new warfare era in long-range fires. In a March 2020 demonstration firing of the emerging Long Range Precision Fires program at Yuma Proving Grounds, Ariz., an Army Howitzer blasted an Excalibur 155m artillery round out to ranges twice that of what existing artillery weapons are now capable of. The new weapon in development, called Extended Range Cannon Artillery, not only preserves the GPS-guided precision attack options characteristic of present-day artillery, but also extends attack ranges from roughly 30km (18.6 miles) out to nearly 70km (43.5 miles). This, senior Army weapons developers explain, gives ground artillery commanders the ability to destroy previously unreachable air and ground targets. "This provides a longer range capability, enabling commanders to attack helicopters, UAVs and go after other new targets farther range," Gen.

The COVID-19 Open Research Dataset (CORD-19), a repository of more than 29,000 scholarly articles on the coronavirus family from around the world, is being released today for free. The data set is the result of work by Microsoft Research, the Allen Institute for AI, the National Library of Medicine at the National Institutes of Health (NIH), the White House Office of Science and Technology (OSTP), and others. It includes machine-readable research from more than 13,000 scholarly articles. The aim is to empower the medical and machine learning research communities to mine text data for insights that can help fight COVID-19. "The White House worked with the National Academies of Science, Engineering, and Medicine and the World Health Organization to identify dozens of high-priority scientific questions related to COVID-19 to inform the call to action," White House CTO Michael Kratsios said today in a teleconference call.

The White House has urged AI experts to analyze a dataset of 29,000 scholarly articles about coronavirus that could offer insights into how to manage the pandemic. These questions have been published on Kaggle, a machine learning community owned by Google. The entire COVID-19 Open Research Dataset (CORD-19) has been made available on SemanticScholar, a free, nonprofit, academic search engine. The collection will be updated whenever new research is published in archival services and peer-reviewed publications. "Decisive action from America's science and technology enterprise is critical to prevent, detect, treat, and develop solutions to COVID-19," said Michael Kratsios, the USA's Chief Technology Officer.

On Christmas Eve, 1968, the astronauts aboard NASA's Apollo 8 spacecraft became the first humans to behold the entirety of Earth with their own eyes. That day, crew member Bill Anders took an iconic photograph called "Earthrise'' that captured our home world emerging from behind the Moon's horizon. "We came all this way to explore the Moon, and the most important thing is that we discovered the Earth," Anders famously said of his mission. More than 50 years later, Earth is being rediscovered from space once again, but this time it is through the "eyes" of satellites, supercomputers, and artificial intelligence (AI) networks. Geospatial science, a sprawling and multifaceted field dedicated to resolving ever-finer details about Earth and its systems, is poised to undergo an unprecedented growth spurt powered by this confluence of technologies across both the public and private sectors. "With the proliferation of satellite platforms, essentially this is something that's almost become impossible to keep a handle on because there are so many new systems being launched and developed by so many different actors globally," said Jonathan Chipman, director of Dartmouth College's Citrin Family GIS/Applied Spatial Analysis Laboratory, in a call. "It's just mind-boggling the amount of data that's now being collected from low-Earth orbit." The feeling of epiphanic connection with the planet experienced by astronauts gazing at Earth is known popularly as "the overview effect," a term coined by author Frank White in his book of the same name. The new geospatial view of Earth, however, may offer something closer to an "overwhelm effect," as our home world is imaged, valued, and monitored by millions of sensors on thousands of spacecraft orbiting Earth. How will we deal with the petabytes of Earth-observation data that may document the collapse of whole ecosystems or the wreckage of natural disasters? What will we do with geospatial information that predicts such dire outcomes but also demands nimble and dramatic changes to our lifestyles? It will take foresight to ensure that the deluge of information is managed in a way that equitably benefits communities and ecosystems around the world, and remains as accessible to the public as possible. "The biggest challenge will be in making sense of all these data," said Dawn Wright, chief scientist of the Environmental Systems Research Institute (Esri), a major geospatial software and data science company, in an email. "It is one thing to store, to distribute, even to analyze, but how do truly understand it?

Over the last few years, artificial intelligence (AI) has been the enfant terrible of the business world: a technology full of unconventional and sometimes controversial behaviour that has shocked, provoked and enchanted audiences worldwide. But now it's time for AI to grow up. Businesses and consumers are tired of having the same debates around the hype vs reality of AI. In 2020, I see three opportunities for this to happen across responsibility, advocacy and regulation. As AI becomes more pervasive, we're likely to see those wronged by it inspired to take action.

A new algorithm is enabling deep learning that is more collaborative and communication-efficient than traditional methods. Army researchers developed algorithms that facilitate distributed, decentralized and collaborative learning capabilities among devices, avoiding the need to pool all data at a central server for learning. "There has been an exponential growth in the amount of data collected and stored locally on individual smart devices," said Dr. Jemin George, an Army scientist at the U.S. Army Combat Capabilities Development Command's Army Research Laboratory. "Numerous research efforts as well as businesses have focused on applying machine learning to extract value from such massive data to provide data-driven insights, decisions and predictions." However, none of these efforts address any of the issues associated with applying machine learning to a contested, congested and constrained battlespace, George said.

This legislation would accelerate and coordinate Federal investments and facilitate new public-private partnerships in research, standards, and education in artificial intelligence, in order to ensure the United States leads the world in the development and use of responsible artificial intelligence systems. "The United States must act now to cement our global leadership in artificial intelligence and ensure the development and adoption of trustworthy AI systems," said Chairwoman Johnson. "We can accomplish this by accelerating our investments in research, development, and the education and training of an AI workforce, all governed by principles of ethics, safety, security, fairness, and transparency. That is exactly what H.R. 6216 seeks to do. I want to thank Ranking Member Lucas for joining me in the development and introduction of this important legislation and the many stakeholders who advised us during its development."

The new strain of coronavirus sweeping across the world has put the UK Government on alert as the number of cases in the country surged past 100 this week. Warnings have been issued that up to one in five workers in the UK could be off sick at the peak of the outbreak. Babylon, which has been campaigning for a "digital-first approach" to health services, has taken additional measures to support patients who might turn to the app for advice, including revisions to information cards on coronavirus within its app. It is coordinating with the NHS and World Health Organisation as part of an effort to "identify, contain and help delay the spread of the virus". It also operates a 24/7 video service in its app, which gives patients access to a GP from their smartphone. Dr Grimes said it is "really helpful at a time like this", as patients can access clinicians remotely and "stop the spread of illness between patients congregating at a clinic".

As of March 10, 2020, 467 confirmed cases of COVID-19 have been reported to the Centers for Disease Control and Prevention in the United States. While governments across the globe are working in collaboration with local authorities and health-care providers to track, respond to and prevent the spread of disease caused by the coronavirus, health experts are turning to advanced analytics and artificial intelligence to augment current efforts to prevent further infection. Data and analytics have proved to be useful in combating the spread of disease, and the federal government has access to ample data on the U.S. population's health and travel as well as the migration of both domestic and wild animals -- all of which can be useful in tracking and predicting disease trajectory. Machine learning's ability to consider large amounts of data and offer insights can lead to deeper knowledge about diseases and enable U.S. health and government officials to make better decisions throughout the entire evolution of an outbreak. As the global human population grows and continues to interact with animals, other opportunities for viruses that originate in animals (like COVID-19) could make the jump from to humans and spread.