"It was a one-day competition with the aim of building machine learning models and increasing the awareness of students on Artificial Intelligence.

Artificial intelligence is improving the ability of healthcare providers to effectively respond to the coronavirus pandemic – allowing for faster diagnoses and speedy dissemination of trusted information as well as detecting fraudulent insurance claims and accurately evaluating patient data in real time. SoftBank-backed AI company Automation Anywhere is offering free healthcare bots to help the industry manage increased workloads due to the outbreak. "Bots are software that will be configured within the company's system in 24 to 48 hours. They can keep a track of infected people, analyse data, find new trends and perform clerical tasks," Milan Sheth, the company's executive vice president for India, the Middle East and Africa, told The National. Collaborating with one of its technology partners in Macau, Automation Anywhere has developed a global positioning system-enabled dashboard that shows local statistics, sites of infection, hospital wait times, local availability of masks and other useful information which is updated every few minutes.

Jennifer Griffin takes us back through the history of US-Vietnam relations. Researchers at Ohio State University are using satellite images and sophisticated artificial intelligence technology to search for unexploded bombs from the Vietnam War. The technology already has been used to survey Vietnam War-era bomb craters in Cambodia. "The new method increased true bomb crater detection by more than 160 percent over standard methods," researchers explain in a statement. "The model, combined with declassified U.S. military records, suggests that 44 to 50 percent of the bombs in the area studied may remain unexploded."

On March 31st the Her Future Summit powered by the Global Startup Ecosystem will take place virtually with 1000 digital delegates. This will be the largest virtual summit for women to date featuring digital stakeholders from over 60 countries. The Her Future Summit aims to identify, train, and empower the next generation of female pioneers. The summit also serves to teach fundamentals of future technology and the leading social impact applications of Artificial Intelligence, among other technologies. Her Future Summit was scheduled to take place in 7 global cities - DC, Silicon Valley, New York, Accra, Port-au-Prince, London, and Dubai - throughout the month of March.

The Global Artificial Intelligence in Healthcare Market is expected to grow from USD 2,178. The positioning of the Global Artificial Intelligence in Healthcare Market vendors in FPNV Positioning Matrix are determined by Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinders, N: Niche, and V: Vital). The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Healthcare Market including are Google, IBM, Intel, Microsoft, NVIDIA, Amazon Web Services, General Electric Company, Medtronic, Micron Technology, and Siemens Healthineers. On the basis of Offering, the Global Artificial Intelligence in Healthcare Market is studied across Hardware, Services, and Software. On the basis of Technology, the Global Artificial Intelligence in Healthcare Market is studied across Computer Vision, Context-Aware Computing, Machine Learning, Natural Language Processing, and Querying Method.

The concept of smart homes is not something new. In 1975, a Scotland company developed a communication protocol that enables smart home devices to talk to each other with the help of existing electrical wires of a home. Over the years, numerous technological developments made smart homes accessible to a large number of people around the globe. If you have yet to try smart home technology a try, here are some of the main reasons why you should have it. No matter what type of neighborhood you live in, it's of the utmost importance to do everything you can to keep your home secure.

An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model. The main contribution of this paper is to avoid heuristic-based methods for string preprocessing and segmentation, the need for task-oriented classifiers, and also the use of specific constraints related to the string length. A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition. Besides, it reduces the complexity of the string recognition task considerably since it drops out classical steps, in special preprocessing, segmentation, and a set of classifiers devoted to strings with a specific length.

Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately. While reliable decision-tree methods exist to triage patients based on their presentation, those trees implicitly require human inference and are not immediately applicable in a fully automated setting. On the other hand, learning triage policies directly from experts may correct for some of the limitations of hard-coded decision-trees. In this work, we present a Deep Reinforcement Learning approach (a variant of DeepQ-Learning) to triage patients using curated clinical vignettes. The dataset, consisting of 1374 clinical vignettes, was created by medical doctors to represent real-life cases. Each vignette is associated with an average of 3.8 expert triage decisions given by medical doctors relying solely on medical history. We show that this approach is on a par with human performance, yielding safe triage decisions in 94% of cases, and matching expert decisions in 85% of cases. The trained agent learns when to stop asking questions, acquires optimized decision policies requiring less evidence than supervised approaches, and adapts to the novelty of a situation by asking for more information. Overall, we demonstrate that a Deep Reinforcement Learning approach can learn effective medical triage policies directly from expert decisions, without requiring expert knowledge engineering. This approach is scalable and can be deployed in healthcare settings or geographical regions with distinct triage specifications, or where trained experts are scarce, to improve decision making in the early stage of care.

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.

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.