Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence. They allow us to integrate in a natural way expert knowledge, physical modeling, heterogeneous and correlated data and quantities of interest. For exactly this reason, multiple sources of model uncertainty are inherent within the modular structure of the graphical model. In this paper we develop information-theoretic, robust uncertainty quantification methods and non-parametric stress tests for directed graphical models to assess the effect and the propagation through the graph of multi-sourced model uncertainties to quantities of interest. These methods allow us to rank the different sources of uncertainty and correct the graphical model by targeting its most impactful components with respect to the quantities of interest. Thus, from a machine learning perspective, we provide a mathematically rigorous approach to correctability that guarantees a systematic selection for improvement of components of a graphical model while controlling potential new errors created in the process in other parts of the model. We demonstrate our methods in two physico-chemical examples, namely quantum scale-informed chemical kinetics and materials screening to improve the efficiency of fuel cells.

Students these days spend a lot of time on the internet. Be it for an assignment, on social media, or for playing video games, they spend a lot of time on their gadgets. The world has become more interactive through the web and one can meet thousands of strangers or expose oneself to cyberattacks- one of the most dreadful crimes of our age. The impact of such crimes can be scarring for young people. One cannot simply lock them up in a room with no wi-fi. While there are certain games for students that can be played without the support of the internet, most of the popular ones are multiplayer and cannot function with a web connection.

Quantum metrology: Quantum measurements involve highly accurate manipulation of particles to identify subtle changes in information. Quantum metrology could enable new types of radars, cameras, and other systems, which when applied in defense and national security use cases, might offer better ways to detect things like stealth aircrafts via quantum radar, or underground facilities via quantum gravimetry. It can also provide new types of location detection that does not depend on GPS signals--which can be easily tampered with. Cryptography: An essential aspect of cryptography is random number generation. To break it down: pseudo random number generators (PRNGs) and true random number generators (TRNGs).

Get email updates with the day's biggest stories China has built a shark drone to help it spy on and hunt down enemy ships and submarines. The stealthy sea robot can move at speeds of six knots and will help conduct reconnaissance as well as search and destroy missions for the country's military. Developed independently by Beijing-based Boya Gongdao Robot Technology, the unmanned device was unveiled at the 7th China Military Intelligent Technology Expo on Monday. And it has already been deployed for use by the forces. Most such drones can be fired out of a sub's torpedo tube, but it is unclear how the Robo-Shark will be launched. It's other functions include search and rescue, battlefield surveillance, hydrological survey, communications relay and underwater tracking missions, the Global Times reports.

These are exciting times for the artificial intelligence community. Interest in the field is growing at an accelerating pace, registration at academic and professional machine learning courses is soaring, attendance in AI conferences is at an all-time high, and AI algorithms have become a vital component of many applications we use every day. But as with any field going through the hype cycle, AI is surrounded by a saturation of information, much of which is misleading or of little value. I can tell that from my inbox. Every day, I receive several pitches that claim company X has solved problem Y with "advanced AI techniques," or that AI can now solve problem Z. A few years ago, I might have opened and read these emails with interest.

The Defense Threat Reduction Agency wants information on companies, universities and other organizations working on artificial intelligence, machine learning and data science technologies that could help counter weapons of mass destruction and other emerging threats. DTRA intends to use AI, ML and data science tools to improve decision-making and situational awareness for countering WMD and supporting deterrence missions, automate the identification of CWMD and deterrence objects and activities and facilitate information delivery to meet warfighter operational needs, according to a request for information posted Friday. The technology interest areas outlined in the RFI include AI-enhanced modeling and simulation, natural language processing, computer vision, high performance computing and multiagent systems. The agency is seeking information on data analytics, cloud platforms for data transfer and harmonization, data storage and accessibility, automated data labeling and other data-related capabilities. DTRA has asked interested stakeholders to share information on other specific interest areas, including the detection of spectral emissions, sensor data integration, human/computer interface and extraction of actionable information from noisy data.

AWS just announced the General Availability of Amazon HealthLake, a HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud at petabyte scale. We believe that the combination of the innovation trends in healthcare (such as reimbursement models around data-driven evidence), standardization around interoperability (such as federal and global incentives and mandates in adopting the Fast Healthcare Interoperability Resources standard, or FHIR), and the advancement of scientific methods (such as with deep learning) enable our healthcare and life sciences (HCLS) customers to improve clinical and research efforts. Over the past decade, we've witnessed a digital transformation with healthcare organizations capturing huge volumes of patient information in electronic medical records (EMRs) every day, making the medical record a source of big data containing information regarding sociodemographics, medical conditions, genetics, and treatments. Making sense of all this data provides the biggest opportunity to transform care by tailoring disease treatment and prevention to individuals and populations. This so-called precision medicine takes into account the individual variability in genes, environment, and lifestyle for each individual.

WASHINGTON: In a clear sign of the fundamental importance of ethics and human control to the coming age of artificial intelligence in the US military, Defense Secretary Lloyd Austin declared his department will "do it the right way," even as competitors like China use AI to better monitor and suppress their citizens. "In the AI realm, as in many others, we understand that China is our pacing challenge. We're going to compete to win, but we're going to do it the right way," Austin told a day-long conference of the National Security Commission on Artificial Intelligence (NACAI). "So our use of AI must reinforce our democratic values, protect our rights, ensure our safety, and defend our privacy. Of course, we understand the pressures and the tensions. And we know that evaluations of the legal and ethical implications of novel tech can take time."

Scientists hunting for elusive gravitational waves across the universe may be able to supercharge their discoveries with a new tool: artificial intelligence. Gravitational waves are ripples in spacetime, created when a massive object is accelerated or disturbed, such as when a black hole and a neutron star collide. Theorized by Albert Einstein, their existence was confirmed in 2015 with the first gravitational wave discovery by researchers using LIGO (the advanced Laser Interferometer Gravitational-Wave Observatory). Now, just six years later, there have been at least 50 gravitational wave events detected. However, while scientists continue to detect gravitational waves, some think that, by using artificial intelligence (AI), researchers could spot these signals much faster and, therefore, more often.

There's bipartisan agreement in Washington that the US government should do more to support development of artificial intelligence technology. The Trump administration redirected research funding towards AI programs; President Biden's science advisor Eric Lander said of AI last month that "America's economic prosperity hinges on foundational investments in our technological leadership." At the same time, parts of the US government are working to place limits on algorithms to prevent discrimination, injustice, or waste. The White House, lawmakers from both parties, and federal agencies including the Department of Defense and the National Institute for Standards and Technology are all working on bills or projects to constrain potential downsides of AI. Biden's Office of Science and Technology Policy is working on addressing the risks of discrimination caused by algorithms.