National security and antitrust are rarely part of the same conversation. The realities of today's AI ecosystem should challenge that dynamic. American AI innovation is concentrated in the private sector--particularly within its largest, most dominant firms. As these firms face antitrust scrutiny, policymakers and lawmakers alike need to consider the AI ecosystem that they will have a hand in creating. They will need to contemplate its competitiveness, its innovativeness, its responsiveness to defense and national-security needs, and its accessibility to government.

Analytics Insight predicts that the global AI market is expected to reach US$53.2 billion in 2020 and will further grow on to reach US$152.9 billion in 2023. Beyond that, businesses are bound to become even more innovative with rising AI trends this year. According to a report, AI will help with the repetitive and labor extensive tasks that people carry mostly on their machines. The extensive form filling work, generating reports, and diagrams all can be done more quickly. According to Forbes, approximately 23% of businesses have implemented Artificial intelligence into processing and product services, and more than 60 businesses are still in process. However, this number will increase by 80-90% until 2022.

What do Ted Cruz, Neil deGrasse Tyson and Goldman Sachs all have in common? They predict that the world's first trillionaire will make their innumerable fortune in space. While Cruz is not precisely sure how this will come to be, Tyson and Goldman Sachs believe that the gateway to this immense wealth is through mining asteroids. The reason why space mining is so sought after is due to what is happening here on Earth. Based on known terrestrial reserves and estimates of the growing consumption in countries, essential elements needed for modern industry and food production (such as lead, phosphorus and gold) could be exhausted within the next 60 years.

We introduce powerful but simple methodology for identifying anomalous observations against a corpus of `normal' observations. All data are observed through a vector-valued feature map. Our approach depends on the choice of corpus and that feature map but is invariant to affine transformations of the map and has no other external dependencies, such as choices of metric; we call it conformance. Applying this method to (signatures) of time series and other types of streamed data we provide an effective methodology of broad applicability for identifying anomalous complex multimodal sequential data. We demonstrate the applicability and effectiveness of our method by evaluating it against multiple data sets. Based on quantifying performance using the receiver operating characteristic (ROC) area under the curve (AUC), our method yields an AUC score of 98.9\% for the PenDigits data set; in a subsequent experiment involving marine vessel traffic data our approach yields an AUC score of 89.1\%. Based on comparison involving univariate time series from the UEA \& UCR time series repository with performance quantified using balanced accuracy and assuming an optimal operating point, our approach outperforms a state-of-the-art shapelet method for 19 out of 28 data sets.

Prior to January 2020, the artificial intelligence and machine learning (AI/ML) for healthcare community had many reasons to be pleased with the recent progress of their field. Learning-based algorithms had been shown to accurately forecast the onset of septic shock [1], MLbased pattern recognition methods classified skin lesions with dermatologist level accuracy [2], diagnostic AI systems successfully identified diabetic retinopathy during routine primary care visits [3], AIbased breast cancer screening outperformed radiologists by a fairly large margin [4], MLdriven triaging tools improved outcome differentiation beyond the emergency severity index [5], AIenabled assistance systems simplified interventional workflows [6], and algorithm-driven organizational studies enabled redesign of infusion centers [7]. Many would have argued that, after nearly 60 years on the test bench [8], AI in healthcare had finally reached a level of maturity, performance, and reliability that was compatible with the unforgiving requirements imposed by clinical practice. Today, only a few months later, this rather sunny outlook has become overcast. The worlds healthcare systems are facing the outbreak of a novel respiratory disease, COVID-19.

In recent years, the paradigms of data-driven science have become essential components of physical sciences, particularly in geophysical disciplines such as climatology. The field of hydrology is one of these disciplines where machine learning and data-driven models have attracted significant attention. This offers significant potential for data scientists' contributions to hydrologic research. As in every interdisciplinary research effort, an initial mutual understanding of the domain is key to successful work later on. In this work, we focus on the element of hydrologic rainfall--runoff models and their application to forecast floods and predict streamflow, the volume of water flowing in a river. This guide aims to help interested data scientists gain an understanding of the problem, the hydrologic concepts involved, and the details that come up along the way. We have captured lessons that we have learned while "coming up to speed" on streamflow prediction and hope that our experiences will be useful to the community.

Several custom web scrapers were created for retrieving news articles from various online news organizations. All web scrapers were run every two hours to retrieve articles from the following five news sites: the Atlantic, the British Broadcasting Corporation (BBC) News, Fox News, the New York Times and Slate Magazine. Web scrapers continue to run every two hours in perpetuity, scraping additional news articles. Collectively, the web scrapers used each news organization's RSS feed as input, storing the scraped output into a custom database. Article URLs were used for disambiguation; where two scraped articles shared a URL, the most recently retrieved article replaced previous versions of articles. As of November 2019, we collected a total of 105,000 news articles from five media organizations. Figure 2 depicts the number of cumulative articles scraped for each news organization over time. Even though articles from Fox News were regularly scraped four months later than other news sources, the number of articles scraped rose quickly, and now constitutes the news organization with the most scraped articles. Given the news scrapers run at regularly scheduled two-hour intervals for all news organization, this suggests that Fox News updates its RSS feed with new articles far more often than others, and the Atlantic updates its RSS feed far less frequently than others.

Gaussian processes are powerful models for probabilistic machine learning, but are limited in application by their $O(N^3)$ inference complexity. We propose a method for deriving parametric families of kernel functions with compact spatial support, which yield naturally sparse kernel matrices and enable fast Gaussian process inference via sparse linear algebra. These families generalize known compactly-supported kernel functions, such as the Wendland polynomials. The parameters of this family of kernels can be learned from data using maximum likelihood estimation. Alternatively, we can quickly compute compact approximations of a target kernel using convex optimization. We demonstrate that these approximations incur minimal error over the exact models when modeling data drawn directly from a target GP, and can out-perform the traditional GP kernels on real-world signal reconstruction tasks, while exhibiting sub-quadratic inference complexity.

As he departs, the Department of Defense's top artificial intelligence official says the foundation is set for the Joint Artificial Intelligence Center--now it has to deliver. "The foundational elements are now in place. What we have to do in the course of the next one to two years is deliver. This is about delivery first and foremost," said Lt. Gen. Jack Shanahan at a virtual Mitchell Institute event June 4. "What we have to do is show that we're making a difference."

As he departs, the Defense Department's top artificial intelligence official says the foundation is set for the Joint Artificial Intelligence Center -- but now it must deliver. "The foundational elements are now in place. What we have to do in the course of the next one to two years is deliver. This is about delivery first and foremost," Lt. Gen. Jack Shanahan said during a virtual Mitchell Institute event June 4. "What we have to do is show that we're making a difference."