From healthcare and retail to automotive and manufacturing, every industry is getting smarter with technologies like IoT. Failing to stay competitive in this space can result in significant losses. The global pandemic was a significant roadblock for IoT growth in 2020. Although a November 2019 forecast predicted that IoT spending would grow 14.9% in 2020, it could only grow 8.2%. Based on forecasting from the International Data Corporation, IoT will return in stride this year and achieve a growth rate of 11.3% from 2020 to 2024. Recently, a shortage in semiconductors and other IoT components poses questions for IoT's growth in 2021.

Finding the similarities and differences between groups of datasets is a fundamental analysis task. For high-dimensional data, dimensionality reduction (DR) methods are often used to find the characteristics of each group. However, existing DR methods provide limited capability and flexibility for such comparative analysis as each method is designed only for a narrow analysis target, such as identifying factors that most differentiate groups. This paper presents an interactive DR framework where we integrate our new DR method, called ULCA (unified linear comparative analysis), with an interactive visual interface. ULCA unifies two DR schemes, discriminant analysis and contrastive learning, to support various comparative analysis tasks. To provide flexibility for comparative analysis, we develop an optimization algorithm that enables analysts to interactively refine ULCA results. Additionally, the interactive visualization interface facilitates interpretation and refinement of the ULCA results. We evaluate ULCA and the optimization algorithm to show their efficiency as well as present multiple case studies using real-world datasets to demonstrate the usefulness of this framework.

Widespread and rapid dissemination of false news has made fact-checking an indispensable requirement. Given its time-consuming and labor-intensive nature, the task calls for an automated support to meet the demand. In this paper, we propose to leverage commonsense knowledge for the tasks of false news classification and check-worthy claim detection. Arguing that commonsense knowledge is a factor in human believability, we fine-tune the BERT language model with a commonsense question answering task and the aforementioned tasks in a multi-task learning environment. For predicting fine-grained false news types, we compare the proposed fine-tuned model's performance with the false news classification models on a public dataset as well as a newly collected dataset. We compare the model's performance with the single-task BERT model and a state-of-the-art check-worthy claim detection tool to evaluate the check-worthy claim detection. Our experimental analysis demonstrates that commonsense knowledge can improve performance in both tasks.

Artificial intelligence-enabled armies are being designed and planned -- right now. "We are not organized to win this competition. We just are not," he says. "We have got to take this competition seriously, and we need to win it." The Pentagon promises to build an "ethical" AI army.

The Covid-19 coronavirus crisis is accelerating the pace of digital transformation among companies of all shapes and sizes, and the public sector is no exception, as decision-makers rally to find digital solutions to meet fast-changing requirements despite underlying legacy challenges. While the immediate focus is to limit the human, social and economic loss, operating in the "new normal" will mean extra pressure on IT in the months to come. Public sector bodies need to use digital channels to inform and serve citizens, while at the same time, many functions have gone all-digital during the coronavirus outbreak, increasing demand for efficient back-end systems. The pandemic has exposed the need to improve technology efficiency for the continuity of government. Computer Weekly spoke to specialists operating in the public sector to gain an overall view of the key trends and hurdles facing buyers as the crisis unfolds.

NASA's Perseverance Mars rover has stumbled into an issue during its first sampling attempt. "#SamplingMars is one of my most complicated tasks. Early pics and data show a successful drill hole, but no sample in the tube–something we've never seen in testing on Earth," the rover's Twitter account announced on Friday. In a release from the agency, the Southern California-based Jet Propulsion Laboratory (JPL) which leads the mission wrote that data sent back to Earth after Perseverance's first rock sampling attempt indicated "no rock was collected." The 2,260-pound rover carries 43 titanium sample tubes in its mission to search for interesting rocks and sediment – potentially leading to signs of ancient microbial life on the red planet – and is exploring Jezero Crater: the region where it first landed in February. "While this is not the'hole-in-one' we hoped for, there is always risk with breaking new ground," Thomas Zurbuchen, associate administrator of NASA's Science Mission Directorate in Washington, D.C., said in a statement.

NASA's Perseverance rover just had a rare misstep. The space agency has revealed that the robotic vehicle failed to collect Mars rock samples during its first attempt. While the percussive drill, coring bit and sample tube processing worked "as intended," a probe indicated that the tube was empty -- not exactly what scientists were expecting when everything else checked out. Scientists are still investigating what happened and may not have an answer for a few days. Perseverance project manager Jennifer Trosper said the team suspected the rock might have reacted in an unexpected way during the coring process.

SANTA CLARA, Calif., August 02, 2021--(BUSINESS WIRE)--Stellar Cyber, the leading next-generation security operations platform, announced today that it has partnered with BlackBerry to accelerate adoption of AI-powered security solutions for enterprises and managed security service providers (MSSPs). Both companies are pioneers in using AI and machine learning to eliminate signatures and manual scripts from security solutions, and they are going to market together as a catalyst for AI-based security adoption worldwide by offering an integrated solution that combines BlackBerry's prevention-first AI-driven Unified Endpoint Security offerings with Stellar Cyber's security operations platform. BlackBerry has redefined what an endpoint protection solution can and should do for organizations by utilizing an AI-driven, prevention-first approach. Stellar Cyber pioneered adding AI to the security operations environments, helping to eliminate manual rule creation and management. By joining together in the marketplace, they offer enterprises and MSSPs higher efficiency, a lower rate of false positives, and significantly better protection against today's multi-layered cyberattacks.

Robotics is one of the fast-growing areas of technology that is opening doors to a wide range of industries such as security, automation, healthcare, consumer products, customized manufacturing, and interactive entertainment. According to the latest research of the U.S. The Bureau of Labor Statistics, Robotics Engineering is expected to grow 4% by 2028. The area of Robotics is likely to be in demand due to the emergence of new technologies. Since the future is of robots the demand and interest among robotics enthusiasts is also growing day by day. Here are the top online masters in robotics programs for robotic lovers.

Scale is a vital part of bringing ML-enabled technologies to the world, from autonomous driving to drones, robots, and AV / AR. As our customer base is growing exponentially, you will be leading our field engineering efforts for our computer vision based projects, ensuring that the world's most innovative companies become passionate, lifelong Scale customers. Scale works with the world's top self-driving car and robotics ML teams on annotating data across Lidar, Camera, and Radar modalities. For example, our CTO, Brad Porter, was Amazon's top robotics leader, our Director of Engineering, Elliot Branson, lead perception at Cruise Automation, and one of our product leaders, Russell Kaplan, built out the core vision model for Tesla Autopilot. As our Field Engineering team continues to grow, we're looking for a player-coach to lead by example and build out the processes, systems, and structures that will support Scale's ambitious growth goals.