A s the United States emerges from the pandemic, many state and local government agencies are struggling to hire and retain workers. Not only have many retirement-age employees decided to accelerate their plans to retire, but agencies face stiff competition for workers from the private sector. As a result, for a number of key positions, especially those in IT, many government agencies are receiving fewer qualified applicants than the number of jobs available. To address this challenge, government agencies should start making use of AI tools to improve how they acquire and retain workers. A growing number of tools make use of AI to help organizations recruit and hire talent more effectively.

Sharjah 24: As part of the Sharjah Research Technology and Innovation Park (SRTI Park)'s continuous efforts to keep pace with the fourth industrial revolution, take advantage of modern technologies, and shape the future of service institutions, SRTI Park, in cooperation with Amazon Web Services, organised a workshop on strategies for artificial intelligence applications, and ways to benefit from its innovative technologies in the fields of work. The workshop was attended by a number of technology and innovation officials, managers, technical experts and management of information systems in government departments, entities and universities in Sharjah. The workshop, which was presented by a team of engineers and experts from Amazon Web Services, aims to present the latest developments and the latest uses of artificial intelligence and machine learning techniques, and to explore opportunities for employing these technologies in various work sectors and harnessing them to serve humans and society. The workshop touched on how to use artificial intelligence and machine learning in organisations to drive business success, in addition to offering a number of services and solutions to help government institutions in Sharjah, drive innovation and the growth of their businesses and services. His Excellency Hussain Al Mahmoudi, CEO of STRI Park, stated that STRI Park is pleased to organise such kind of workshops through cooperating with Amazon Web Services, on artificial intelligence strategies and ways to benefit from its innovative technologies in the areas of work of institutions and government entities in Sharjah.

Over the last couple of years, there has been much discussion about the benefits of artificial intelligence (AI) for improving healthcare. But how much of this is true and how much simply hype? Is the technology really a godsend to radiologists and other healthcare professionals, or is it making their lives more difficult? There is no doubt that AI-based image recognition technology has improved enormously in recent years. Many researchers and companies are now working on different types of programs with a view to improving speed, accuracy and costs of cancer screening.

New images from NASA show the Perseverance Mars rover hard at work as it searches the Red Planet for signs of ancient microbial life. Since "Percy" landed in Mars' Jezero Crater in February, the agency said the rocks there are beginning to reveal a picture of its history billions of years ago. In a Thursday release, NASA credited Perseverance's seven science cameras for the team's progress "The imaging cameras are a huge piece of everything," Vivian Sun, the co-lead for Perseverance's first science campaign at NASA's Jet Propulsion Laboratory (JPL) in Southern California, said in a statement. "We use a lot of them every single day for science. Perseverance looks back with one of its navigation cameras toward its tracks on July 1, 2021 (the 130th sol, or Martian day, of its mission), after driving autonomously 358 feet (109 meters) – its longest autonomous drive to date. The image has been processed to enhance the contrast. NASA shared a photo from Perseverance's navigation cameras on the rover's longest autonomous drive to date, an enhanced-color panorama from the mast's Mastcam-Z camera system, a shot of the crater's "Delta Scarp" taken by Perseverance's Remote Microscopic Imager (RMI) camera and a close-up of a rock target nicknamed "Foux" taken using its WATSON (the Wide Angle Topographic Sensor for Operations and eNgineering) camera. After receiving Mastcam-Z images of the scarp and SuperCam RMI to provide a more detailed view of the scarp, SuperCam principal investigator Roger Wiens said the images showed there had been a large flash flooding event that occurred, washing boulders down into the delta formation. Perseverance Mars rover used its Mastcam-Z camera system to create this enhanced-color panorama, which scientists used to look for rock-sampling sites. The panorama is stitched together from 70 individual images taken on July 28, 2021, the 155th Martian day, or sol, of the mission. "These large boulders are partway down the delta formation," Wiens, of New Mexico's Los Alamos National Laboratory, said. "If the lakebed was full, you would find these at the very top.

Can robots and workers co-exist? Workers, policymakers, and the media are concerned with the idea that automation, or technological change, will displace millions of American workers--and they are partially right. Andrew Yang, an early 2020 Presidential hopeful is already running on the idea that "the robots are coming" – though the story is not so simple. There have been, and will continue to be, technological breakthroughs that replace workers and reshape our economy. The next big worker-displacing technology is supposedly artificial intelligence (AI), which is thought to have the potential to replace millions of workers performing routine and menial tasks.

The system, called Battle Management Training NEXT (BMTN), provides command and control battle management operators sustained, high quality, low cost training repetitions. "BMTN was developed in partnership with Vectrona, Breakaway Games and Sentrana to provide a host of first-ever combined artificial intelligence, machine learning, biometric, and natural language processing capabilities consolidated into one command and control training system," explained Lt. Col. Kip Trausch, Western Air Defense Sector chief innovation officer. "BMTN will be the fulcrum for the Battle Control Center to break the negative training feedback loop and enable consistent and meaningful wartime preparation." BMTN, which was also rolled-out to the Air National Guard Battle Control Center enterprise, solves the negative feedback loop generated from an ever-present and high operations tempo coupled with training that can only be conducted internally that results in a lack of time, instructors, and system resources to conduct comprehensive wartime readiness on pace with friendly capability and enemy threat evolution. "BMTN is a direct tactical-level answer to CSAF Brown's Accelerate Change or Lose and systems like this have the flexibility baked in to allow headquarters, commanders, and end-users to create the latest training content to drive familiarity, proficiency, and, potentially for the first time, fluency," Trausch said.

The US Food and Drug Administration (FDA) has authorized marketing of artificial intelligence (AI) software to help pathologists detect prostate cancer. The program, called Paige Prostate, is the first approved AI system in pathology. "We really believe this product can make a huge difference," Paige CEO Leo Grady, PhD, told Medscape Medical News. The program was approved as an adjunct to pathologist review, not a replacement. Grady explained that "for a second opinion today, you ship a glass slide to somebody else or you do another stain that's really expensive or you do another molecular test."

As we move towards a future where we lean on cybersecurity much more in our daily lives, it's important to be aware of the differences in the types of AI being used for network security. Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning). Supervised Learning relies on a process of labeling in order to "understand" information.

A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world -- especially in problematic regions unable to cope with these conditions. However, the movement to a low-carbon energy supply can not happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required, however, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. A well choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods.

We present an overview of the second edition of the CheckThat! Lab at CLEF 2019. The lab featured two tasks in two different languages: English and Arabic. Task 1 (English) challenged the participating systems to predict which claims in a political debate or speech should be prioritized for fact-checking. Task 2 (Arabic) asked to (A) rank a given set of Web pages with respect to a check-worthy claim based on their usefulness for fact-checking that claim, (B) classify these same Web pages according to their degree of usefulness for fact-checking the target claim, (C) identify useful passages from these pages, and (D) use the useful pages to predict the claim's factuality. CheckThat! provided a full evaluation framework, consisting of data in English (derived from fact-checking sources) and Arabic (gathered and annotated from scratch) and evaluation based on mean average precision (MAP) and normalized discounted cumulative gain (nDCG) for ranking, and F1 for classification. A total of 47 teams registered to participate in this lab, and fourteen of them actually submitted runs (compared to nine last year). The evaluation results show that the most successful approaches to Task 1 used various neural networks and logistic regression. As for Task 2, learning-to-rank was used by the highest scoring runs for subtask A, while different classifiers were used in the other subtasks. We release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important tasks of check-worthiness estimation and automatic claim verification.