Want to be a part of an elite team where our innovative technical solutions are delivered to customers that advance the state of the art while addressing long-term problems of importance to national security? At our Leidos' Multi-Spectrum Warfare Research and Analytics Systems (MSWRAS) Division, an organization in the Leidos Innovation Center (LInC), we are looking for you, our next Scientist who specializes in remote sensing data analytics. Join our team of Ph.D. level peers in designing and developing advanced technology-based solutions for contract research and development projects working in our Arlington, VA office. Fun roles you will have in this job: Describe instances of successful, proven, and demonstrable experience contributing to the technical work as part of cross-discipline teams in the development and integration of software-based solutions for competitive, contract-based applied research programs Work with teams composed of members from industry, small businesses, and academic-based researchers and should have experience working on projects focused on multiple technical fields such as machine learning, artificial intelligence, engineering, and software development and integration Describe how the work products to which they contributed had solved customers' problems in such domains as energy, health, and national security or in the commercial sector Work within the MSWRAS Division and across the LInC, performing basic and applied contract research and development projects both leading and working under the guidance of senior scientists and engineers. Processing, interpreting and analyzing large volumes of data collected by remote sensing platforms but may also include other types of phenomenological data such as field measurements, or weather data Independently design and undertake new research as well as partner in a team environment across organizations Contribute to the development of creative and innovative R&D approaches to solving major remote sensing analytics challenges and work with potential sponsors (customers or internal champions) to secure funding for new research efforts based on those topics Contribute to the productivity of teams composed of fellow researchers, data scientists, data engineers, and software engineers to execute complex R&D programs Under the guidance of a senior scientist or engineer, design and develop or integrate secure and scalable applications that are part of broader solutions, that are applicable across multiple domains.

The Sisense analytics platform is known for its augmented analytics capabilities and ease of use, and as it moves forward it will do so with a new leader in charge of its product development. Just over a year after its acquisition of Periscope Data, a purchase that added capabilities aimed at data scientists to the features geared toward business users Sisense was already know for, the New York-based vendor is focused on third-generation analytics in which AI and business intelligence embedded throughout the workflow will be prominent. Most recently, Sisense updated its analytics platform with new natural language query capabilities and introduced Knowledge Graph, a graph analytics engine the vendor developed that was trained on more than 650 billion past analytic events and informs the machine learning capabilities of the query tool. Now, to help shape its vision, Sisense has added Ashley Kramer as its first chief product officer. Kramer began her career as a software engineering manager at NASA.

Yes, Artificial Intelligence is having its drive now. In our market research, we found that AI is a growing force for any kind of businesses from a wide range variety of industries including healthcare, banking and financial, manufacturing, retail, mining, and e-commerce. In particular, digital marketing and sales divisions are fueling their businesses by engaging customers automatically using virtual assistants and chatbots, etc. According to a research company, digital marketing experts predict that AI will be going to occupy over 80% of businesses by 2020. And they believe that the revolutionizing AI technology can completely change the existing marketing paradigms in the next three to five years.

Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule's structure affects its properties. They synthesize and test countless variants, and most are failures. "Coming up with new molecules is still an art, because you have such a huge space of possibilities," says Barzilay. "It takes a long time to find good drug candidates." By speeding up this critical step, deep learning could offer far more opportunities for chemists to pursue, making drug discovery much quicker.

The growing demand for edge computing resources, particularly due to increasing popularity of Internet of Things (IoT), and distributed machine/deep learning applications poses a significant challenge. On the one hand, certain edge service providers (ESPs) may not have sufficient resources to satisfy their applications according to the associated service-level agreements. On the other hand, some ESPs may have additional unused resources. In this paper, we propose a resource-sharing framework that allows different ESPs to optimally utilize their resources and improve the satisfaction level of applications subject to constraints such as communication cost for sharing resources across ESPs. Our framework considers that different ESPs have their own objectives for utilizing their resources, thus resulting in a multi-objective optimization problem. We present an $N$-person \emph{Nash Bargaining Solution} (NBS) for resource allocation and sharing among ESPs with \emph{Pareto} optimality guarantee. Furthermore, we propose a \emph{distributed}, primal-dual algorithm to obtain the NBS by proving that the strong-duality property holds for the resultant resource sharing optimization problem. Using synthetic and real-world data traces, we show numerically that the proposed NBS based framework not only enhances the ability to satisfy applications' resource demands, but also improves utilities of different ESPs.

The web accessibility market has undergone a tremendous amount of upheaval over the past five years. Most recently, the societal aftershocks of the coronavirus pandemic have reminded everyone of the importance of universal access to digital services. Since 2015, there has also been an explosion of litigation, including class-action lawsuits filed under the ADA (Americans with Disabilities Act) against organizations that have failed to make their websites accessible to disabled people. In 2018, the number of web accessibility lawsuits in the U.S. increased by 177% from the previous year to 2,258. Up to 20% of the population have a disability, be it visual, auditory, or motor, requiring a computer access intervention.

The Trump administration has reportedly awarded a contract to a California-based tech startup to set up hundreds of "autonomous surveillance towers" along the U.S.-Mexico border to aid its immigration enforcement efforts. U.S. Customs and Border Protection (CBP) announced on Thursday that the towers, which use artificial intelligence and imagery to identify people and vehicles, were now a "program of record" for the agency and that 200 would be deployed along the southern border by 2022. CBP did not mention the contract in its announcement, though the Washington Post reported that the effort includes a five-year agreement with Anduril Industries, a tech startup backed by investors such as Peter Thiel. Anduril executives told the Post that the deal is worth hundreds of millions of dollars. The company, which specializes in AI and other technologies, is valued at $1.9 billion, according to Bloomberg News.

The growth potential of the data economy is mind-blowing. In Europe alone, the figures and forecasts are eye-catching, to say the least. The European Commission expects the value of the data economy to rise to €829 billion by 2025, up from €301 billion in 2018. Focusing on the headline economic figures alone overlooks the enormous potential to use data to create lasting social change and improve the personal and professional lives of millions of European citizens. The term digital economy is a catch-all for a wide range of digital transformation activities.

Researchers at the U.S. Army Combat Capabilities Development Command's Army Research Laboratory say this may be changing as they endeavor to design computers inspired by the human brain's neural structure. As part of a collaboration with Lehigh University, Army researchers have identified a design strategy for the development of neuromorphic materials. "Neuromorphic materials is a name given to the material categories or combination of materials that provide both computing and memory capabilities in devices," said Dr. Sina Najmaei, a research scientist and electrical engineer with the laboratory. Najmaei and his colleagues published a paper, Dynamically reconfigurable electronic and phononic properties in intercalated Hafnium Disulfide (HfS2), in the May 2020 issue of Materials Today. The neuromorphic computing concept is an in-memory solution that promises orders of magnitude reductions in power consumption over conventional transistors, and is suitable for complex data classification and processing.

Moscow City Hall has been instructed to determine the conditions, requirements and procedure for the development, creation, introduction and implementation of artificial intelligence technologies, as well as the cases and procedures for using the results of the application of artificial intelligence. It is expected that large IT companies using artificial intelligence in the field of medicine, urban infrastructure, face recognition and other uses will take part in the experiment. The Law separately outlines certain provisions relating to the storage and processing of personal data that will be obtained during the experiment. As a result, the Law makes it possible to use the previously anonymised personal data of individuals participating in the experiment to increase the effectiveness of the state or municipal government. However, the Law specifically establishes that such personal data can only be transferred to participants in the experiment and must be stored in Moscow.