The European Commission launches an initiative to collect ideas about deployable Artificial Intelligence (AI) and Robotics solutions as well as information on other initiatives that could help face the ongoing COVID-19 crisis. The initiative aims to create a unique repository that is easily accessible to all citizens, stakeholders and policymakers and become part of the common European response to the outbreak of COVID-19.

The US presidential election campaign is in its final days. Donald Trump is behind in the polls and the pundits are predicting a win for his Democrat challenger, former vice president Joe Biden. He boasts that he will win again. With two weeks to go, his campaign unleashes an offensive in the crucial swing states: adverts, Facebook posts, WhatsApp groups and tweets. They warn of violent crime and civil unrest driven by immigrants and gangs, playing up Trump's endorsement by evangelicals and smearing Biden as a closet atheist. The initiative works and Trump snatches another unlikely victory.

Researchers at the University of Notre Dame are using artificial intelligence to develop an early warning system that will identify manipulated images, deepfake videos and disinformation online. The project is an effort to combat the rise of coordinated social media campaigns to incite violence, sew discord and threaten the integrity of democratic elections. The scalable, automated system uses content-based image retrieval and applies computer vision-based techniques to root out political memes from multiple social networks. "Memes are easy to create and even easier to share," said Tim Weninger, associate professor in the Department of Computer Science and Engineering at Notre Dame. "When it comes to political memes, these can be used to help get out the vote, but they can also be used to spread inaccurate information and cause harm."

As thousands of workers commence working from home due to Coronavirus, the Internet is awash with memes about the tempting distractions of YouTube. The artificial intelligence used by YouTube to continually serve relevant distractions is a modern shoulder devil for the home worker. Baby Shark has nearly 4.8 billion views on YouTube. In 2011 Google revealed that streaming 1-minute of video on YouTube consumes 0.0002 KwH of energy. That means that so far, people watching the 136-second-long Baby Shark video have collectively consumed about 2,112,000 KwH of energy. To give that context, in 2019 the average UK home consumed 3,100 KwH of electricity.

Ethics of AI: While artificial intelligence promises significant benefits, there are concerns it could make unethical decisions. Prefer to listen to this story? Here it is in audio format. Artificial intelligence (AI) is fast becoming important for accountants and businesses, and how it is used raises several ethical issues and questions. While autonomous AI algorithms teach themselves, concerns have been raised that some machine learning techniques are essentially "black boxes" that make it technically impossible to fully understand how the machine arrived at a result. It will become increasingly important to develop AI algorithms that are transparent to inspection, auditable, secure and robust against manipulation and misuse.

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.

While artificial intelligence (AI), machine learning (ML), and automated machine-driven processes are increasingly important in providing better cybersecurity, as well as fraud and risk management, in the financial services sector, Mastercard believes there will still be a place to keep a human in the loop. "We do believe that humans will continue to play an integral role," Mastercard APAC executive vice president and head of services Matthew Driver told ZDNet. "As we increase the number of areas where we apply tools, there is a need for human oversight and reviews in many stages but critically in system design and control systems." Rather than being mutually exclusive, Driver said Mastercard sees the roles of humans and the application of automated tools to be complementary. "Humans are able to make manual reviews and, with experience, can help move these decisions to rules or embed them into models. But machines cannot attribute or deduct causality, so while there will always be newer areas where we are applying AI and modelling, there is a constant need for these to have a human overlay in design and governance," he said.

The Army Research Laboratory is exploring new applications of AI designed to better enable forward operating robot "tanks" to acquire targets, discern and organize war-crucial information, surveil combat zones and even fire weapons when directed by a human. "For the first time the Army will deploy manned tanks that are capable of controlling robotic vehicles able to adapt to the environment and act semi-independently. Manned vehicles will control a number of combat vehicles, not small ones but large ones. In the future we are going to be incorporating robotic systems that are larger, more like the size of a tanks," Dr. Brandon Perelman, Scientist and Engineer, Army Research Laboratory, Combat Capabilities Development Command, Army Futures Command, told Warrior in an interview, Aberdeen Proving Ground, Md. The concept is aligned with ongoing research into new generations of AI being engineered to not only gather and organize information for human decision makers but also advance networking between humans and machines.

In a briefing on Monday, research leaders across tech, academia and the government joined the White House to announce an open data set full of scientific literature on the novel coronavirus. The COVID-19 Open Research Dataset, known as CORD-19, will also add relevant new research moving forward, compiling it into one centralized hub. The new data set is machine readable, making it easily parsed for machine learning purposes -- a key advantage according to researchers involved in the ambitious project. In a press conference, U.S. CTO Michael Kratsios called the new data set the "most extensive collection of machine readable coronavirus literature to date." Kratsios characterized the project as a "call to action" for the AI community, which can employ machine learning techniques to surface unique insights in the body of data.

We study the problem of estimating treatment effects when the outcome of primary interest (e.g., long-term health status) is only seldom observed but abundant surrogate observations (e.g., short-term health outcomes) are available. To investigate the role of surrogates in this setting, we derive the semiparametric efficiency lower bounds of average treatment effect (ATE) both with and without presence of surrogates, as well as several intermediary settings. These bounds characterize the best-possible precision of ATE estimation in each case, and their difference quantifies the efficiency gains from optimally leveraging the surrogates in terms of key problem characteristics when only limited outcome data are available. We show these results apply in two important regimes: when the number of surrogate observations is comparable to primary-outcome observations and when the former dominates the latter. Importantly, we take a missing-data approach that circumvents strong surrogate conditions which are commonly assumed in previous literature but almost always fail in practice. To show how to leverage the efficiency gains of surrogate observations, we propose ATE estimators and inferential methods based on flexible machine learning methods to estimate nuisance parameters that appear in the influence functions. We show our estimators enjoy efficiency and robustness guarantees under weak conditions.