HawkEye 360 Inc., a leading commercial provider of space-based radio frequency (RF) data and analytics, announced the transition from Amazon Web Services (AWS) commercial services to AWS GovCloud (US) as a foundational element of their overall approach to protecting sensitive and controlled data. AWS GovCloud (US) is designed to host sensitive data, regulate workloads, and address stringent U.S. government security and compliance requirements. "Our AWS GovCloud (US) migration is a significant milestone for HawkEye 360. The move to AWS GovCloud (US) facilitates the development and delivery of export-controlled products and services and demonstrates to our customers the security and integrity of our data and analytics," said HawkEye 360 Vice President of Engineering, Chris Gregory. AWS GovCloud (US) is FedRAMP compliant and provides a robust platform for HawkEye 360 to develop and deliver products as well as streamlines the assessment process that demonstrates to the US government that HawkEye 360 is meeting the applicable requirements of the Defense Federal Acquisition Regulation Supplement (DFARS), as well as the Cybersecurity Maturity Model Certification (CMMC) Level 2 and NIST-800-171.

Any overreliance on foreign inputs in drug supply chains could leave the U.S. open to dire shortages in the event of conflict or natural catastrophe. The White House has flagged the potential disruption of the pharmaceutical supply chain as a national-security issue, saying these drugs are essential to the health and prosperity of the country. "The ever-changing threat environment, both natural and man-made, gives rise to numerous unforeseen challenges, such as to the pharmaceutical supply chain," said Jennifer Foley, a deputy director in DHS's science and technology directorate. Quantifind Inc., a company that normally does risk screening for financial institutions, will do the work, looking into supply chains for the Cross-Border Threat Screening and Supply Chain Defense Center of Excellence, a government-backed research center connected with Texas A&M University. Our Morning Risk Report features insights and news on governance, risk and compliance.

Elon Musk's neuroscience startup Neuralink Corp. is expected to give a progress report on its brain-implant technology in a highly anticipated streamed event Wednesday night. In a tweet last week, the company teased a demo for the event, which begins at 9 p.m. New York time, with a short video that slowly spelled out the message "please join us for a show and tell." Some outside researchers said the video may indicate that a Neuralink device has been used to decode brain signals to type words on a screen, although they speculated that it would most likely be through a monkey or a wearable device. Neuralink has been testing its implant technology on nonhuman primates for several years, including in April 2021, when the company released a video showing that a monkey implanted with two Neuralink devices could play a videogame called Pong as the device translated its brain activity into commands with the help of machine-learning software. Other researchers have managed to use a brain-computer interface to enable monkeys to produce words on a computer screen.

The U.S. Army is a large organization with many stakeholders, moving parts, and money inflows and outflows. One could say it's not too different from a large private sector company. It faces many of the same stressors of a large private sector company as well, including deciding how to allocate its budget. In recent years, the Army turned to artificial intelligence (AI) to help tackle budget allocation. The challenge was to find a solution for contracting officers trying to accurately predict which contracts were most likely to end up underspending their funding.

For AI to flourish in healthcare, the industry must focus on the "algorithmically underserved," said John D. Halamka, M.D., M.S., president of Mayo Clinic Platform, at the HLTH 2022 conference this month in Las Vegas. Giving visibility to the algorithmically underserved -- individuals who do not generate enough data/are not well represented enough in health data sets for AI to make a determination -- is just one requirement to overcome the prospect of AI bias in healthcare. And identifying and fixing sources of AI bias must be a focus area for an industry that's striving for ethical and equitable AI development, shared Halamka. Dr. John Halamka is President of Mayo Clinic Platform, and a founding member of the Coalition for ... [ ] Health AI For example, what if there was a national registry that hosted all the metadata needed to power the responsible development of algorithms for use in healthcare? Building this kind of standardization into the relatively black box nature of AI development is among the priorities of The Coalition for Health AI (CHAI), which launched earlier this year.

Artificial intelligence (AI), continues to be a huge growth driver for companies of all sizes going through digital transformation, with IDC predicting that AI spending will reach $500B by 2023. AI is helping organizations identify new business models, increase revenue, and gain a competitive advantage. But along with these opportunities, AI also brings immense responsibilities. AI systems and pipelines are complex. They have many areas of potential failure, from issues with the underlying data to errors in the tools themselves to outcomes that adversely affect people and processes.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. A U.S. Marine veteran gave Los Angeles Lakers fans something to cheer about at the Crypto.com Arena Monday night before the Indiana Pacers won the game on a buzzer beater. Annika Hutsler, who had a leg amputated in 2019, was seated in the arena crowd.

Meaningful uncertainty quantification in computer vision requires reasoning about semantic information -- say, the hair color of the person in a photo or the location of a car on the street. To this end, recent breakthroughs in generative modeling allow us to represent semantic information in disentangled latent spaces, but providing uncertainties on the semantic latent variables has remained challenging. In this work, we provide principled uncertainty intervals that are guaranteed to contain the true semantic factors for any underlying generative model. The method does the following: (1) it uses quantile regression to output a heuristic uncertainty interval for each element in the latent space (2) calibrates these uncertainties such that they contain the true value of the latent for a new, unseen input. The endpoints of these calibrated intervals can then be propagated through the generator to produce interpretable uncertainty visualizations for each semantic factor. This technique reliably communicates semantically meaningful, principled, and instance-adaptive uncertainty in inverse problems like image super-resolution and image completion.

Aerosol-cloud interactions (ACI) include various effects that result from aerosols entering a cloud, and affecting cloud properties. In general, an increase in aerosol concentration results in smaller droplet sizes which leads to larger, brighter, longer-lasting clouds that reflect more sunlight and cool the Earth. The strength of the effect is however heterogeneous, meaning it depends on the surrounding environment, making ACI one of the most uncertain effects in our current climate models. In our work, we use causal machine learning to estimate ACI from satellite observations by reframing the problem as a treatment (aerosol) and outcome (change in droplet radius). We predict the causal effect of aerosol on clouds with uncertainty bounds depending on the unknown factors that may be influencing the impact of aerosol. Of the three climate models evaluated, we find that only one plausibly recreates the trend, lending more credence to its estimate cooling due to ACI.

Minesweeper is an interesting single player game based on logic, memory and guessing. Solving Minesweeper has been shown to be an NP-hard task. Deterministic solvers are the best known approach for solving Minesweeper. This project proposes a neural network based learner for solving Minesweeper. To choose the best learner, different architectures and configurations of neural networks were trained on hundreds of thousands of games. Surprisingly, the proposed neural network based learner has shown to be a very good approximation function for solving Minesweeper. The neural network learner competes well with the CSP solvers, especially in Beginner and Intermediate modes of the game. It was also observed that despite having high success rates, the best neural learner was considerably slower than the best deterministic solver. This report also discusses the overheads and limitations faced while creating highly successful neural networks for Minesweeper.