COVID-19 triggered a wave of US government spending never seen before. IN 2020 total research and development spending rose in response to the need for technology to fight COVID-19. This rapid increase in funds was available to researchers around the United States and artificial intelligence was no exception. The massive influx of public funds into AI research was supplemented with private funding from companies like Microsoft and C3.ai [2]. If money solves problems and creates opportunities then surely AI would have improved health outcomes.

Health care organizations are using artificial intelligence (AI)--which the U.S. Food and Drug Administration defines as "the science and engineering of making intelligent machines"--for a growing range of clinical, administrative, and research purposes. This AI software can, for example, help health care providers diagnose diseases, monitor patients' health, or assist with rote functions such as scheduling patients. Although AI offers unique opportunities to improve health care and patient outcomes, it also comes with potential challenges. AI-enabled products, for example, have sometimes resulted in inaccurate, even potentially harmful, recommendations for treatment.1 These errors can be caused by unanticipated sources of bias in the information used to build or train the AI, inappropriate weight given to certain data points analyzed by the tool, and other flaws. The regulatory framework governing these tools is complex. FDA regulates some--but not all--AI-enabled products used in health care, and the agency plays an important role in ensuring the safety and effectiveness of those products under its jurisdiction. The agency is currently considering how to adapt its review process for AI-enabled medical devices that have the ability to evolve rapidly in response to new data, sometimes in ways that are difficult to foresee.2 This brief describes current and potential uses of AI in health care settings and the challenges these technologies pose, outlines how and under what circumstances they are regulated by FDA, and highlights key questions that will need to be addressed to ensure that the benefits of these devices outweigh their risks.

Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs. On the other hand, the explanation of GNNs (GNNExplainer) provides a better understanding of a trained GNN model by generating a small subgraph and features that are most influential for its prediction. In this paper, we first perform empirical studies to validate that GNNExplainer can act as an inspection tool and have the potential to detect the adversarial perturbations for graphs. This finding motivates us to further initiate a new problem investigation: Whether a graph neural network and its explanations can be jointly attacked by modifying graphs with malicious desires? It is challenging to answer this question since the goals of adversarial attacks and bypassing the GNNExplainer essentially contradict each other. In this work, we give a confirmative answer to this question by proposing a novel attack framework (GEAttack), which can attack both a GNN model and its explanations by simultaneously exploiting their vulnerabilities. Extensive experiments on two explainers (GNNExplainer and PGExplainer) under various real-world datasets demonstrate the effectiveness of the proposed method.

Many different technologies are used to detect pests in the crops, such as manual sampling, sensors, and radar. However, these methods have scalability issues as they fail to cover large areas, are uneconomical and complex. This paper proposes a crowdsourced based method utilising the real-time farmer queries gathered over telephones for pest surveillance. We developed data-driven strategies by aggregating and analyzing historical data to find patterns and get future insights into pest occurrence. We showed that it can be an accurate and economical method for pest surveillance capable of enveloping a large area with high spatio-temporal granularity. Forecasting the pest population will help farmers in making informed decisions at the right time. This will also help the government and policymakers to make the necessary preparations as and when required and may also ensure food security.

Voting systems have a wide range of applications including recommender systems, web search, product design and elections. Limited by the lack of general-purpose analytical tools, it is difficult to hand-engineer desirable voting rules for each use case. For this reason, it is appealing to automatically discover voting rules geared towards each scenario. In this paper, we show that set-input neural network architectures such as Set Transformers, fully-connected graph networks and DeepSets are both theoretically and empirically well-suited for learning voting rules. In particular, we show that these network models can not only mimic a number of existing voting rules to compelling accuracy --- both position-based (such as Plurality and Borda) and comparison-based (such as Kemeny, Copeland and Maximin) --- but also discover near-optimal voting rules that maximize different social welfare functions. Furthermore, the learned voting rules generalize well to different voter utility distributions and election sizes unseen during training.

Modern-day technical advancements are rapidly changing the world. Twenty years back, the internet was nothing as compared to today. Like the internet, the next big thing which is supposed to revolutionize the world is Artificial Intelligence (AI). When you hear Artificial Intelligence, the first thing that comes to your mind is probably the intelligent robot that can make its own decision based on the situation. In actuality, AI has a lot more applications than just creating a robot.

NASA said on Friday it is looking for a few good men and women to help it progress in its plan to go to send humans to Mars by 2037. The US space agency is seeking'highly motivated individuals' to participate in year-long Mars surface simulation, where they will live a 1,700-square-foot module 3D-printed by ICON, called Mars Dune Alpha. The program consists of three simulations, with the first starting in 2022, and each will see four crew members spend the 365 days completely isolated in the mock habitats of the Red Planet. 'The habitat will simulate the challenges of a mission on Mars, including resource limitations, equipment failure, communication delays, and other environmental stressors,' NASA shared in the announcement. 'Crew tasks may include simulated spacewalks, scientific research, use of virtual reality and robotic controls, and exchanging communications.

Artificial intelligence--long predicted to be a game-changer in HR--already is making its mark on the industry. AI is fueling new workplace innovations, improving employee safety and reducing low-value or repetitive work, yielding direct workplace benefits, especially in the area of engagement and productivity. Yet, according to Beena Ammanath, executive director of the Deloitte AI Institute, a key factor in achieving success with AI involves building trust among businesses and employees that adopting it will be good for the economy and society. A recent survey from the Deloitte AI Institute, which connects organizations, think tanks and government leaders on all aspects of AI, and the U.S. Chamber of Commerce found a deep interest in the ethical use of AI innovation and investments among the 250 respondents--mostly senior leaders involved in AI projects at primarily large, U.S-based businesses across a range of industries. The survey specifically included those in HR roles, along with IT/data and research and development, "because of their likely exposure to AI technologies and their impacts," the authors wrote.

Dean Chester is a cybersecurity expert. Artificial Intelligence (AI) tools and resources have become indispensable to today's industry. The 2019 study by Gartner shows that in the last four years, the use of AI has increased by 270%. In the last year alone, the number of organizations that have deployed AI in some way has more than triples from 4% to 14%. Why is AI becoming so popular with businesses of all sizes?

The nature of war continues to evolve through the 21st century with conflict zones shifting from jungles and deserts to coastal cities. Not to mention the rapidly increasing commercial availability of cutting-edge technologies including UAVs and wireless communications. To help the Marine Corps best prepare for these increased complexities and challenges, the Department of Defense tasked DARPA with developing a digital training and operations planning tool. The result is the Prototype Resilient Operations Testbed for Expeditionary Urban Scenarios (PROTEUS) system, a real-time strategy simulator for urban-littoral warfare. When the PROTEUS program first began in 2017, "there was a big push across DARPA under what we call a sustainment focus area, and that included urban warfare," Dr. Tim Grayson, director of DARPA's Strategic Technology Office, told Engadget, looking at how to best support and "sustain" US fighting forces in various combat situations until they can finish their mission.