Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power gri ds, and transportation systems. Similar to other information systems, a significant threat to indust rial control systems is the attack from cyberspace--the offensive maneuvers launched by "anon ymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusio n detection systems that serve as the first line of defense by monitoring and reporting potenti ally malicious activities. Classical machine-learning-based intrusion detection methods usua lly generate prediction models by learning modest-sized training samples all at once. Such approac h is not always applicable to industrial control systems, as industrial control systems must proces s continuous control commands with limited computational resources in a nonstop way. To satisf y such requirements, we propose using online learning to learn prediction models from the control ling data stream. W e introduce several state-of-the-art online learning algorithms categorical ly, and illustrate their efficacies on two typically used testbeds--power system and gas pipeline. Fur ther, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance pro blem that is pervasive in industrial intrusion detection systems. Our experimental results ind icate that the proposed algorithm can achieve an overall improvement in the detection rate of cybe rattacks in industrial control systems. Modern industrial control systems are microprocessor-equ ipped devices and associated communication networks used to monitor and operate physica l equipment in the industrial environment.

San Francisco (CNN Business)As wildfire season raged in California this fall, a startup a few states away used artificial intelligence to pinpoint the location of blazes there within minutes -- in some cases far faster than these fires might otherwise be noticed by firefighters or civilians. Santa Fe-based Descartes Labs, which uses AI to analyze satellite imagery, launched its US wildfire detector in July. The company's AI software pores over images coming in roughly every few minutes from two different US government weather satellites, in search of any changes -- the presence of smoke, a shift in thermal infrared data showing hot spots -- that could indicate a fire has ignited. Descartes is testing its detector by sending alerts to select forestry officials in its home state of New Mexico and told CNN Business its wildfire detector has spotted about 6,200 total thus far. The company says it can often detect these fires when they're just about 10 acres in size.

The past decade has seen some remarkable gains in the manufacturing industry. AI and big data have created new machine capabilities and new job opportunities for highly skilled workers. The ease of communication on a global scale has made collaborating with suppliers, producers and product development firms around the world faster and simpler than ever before. At the same time, U.S. manufacturers are facing a worrisome long-term skills shortage, while the trade war with China has made the short-term outlook for domestic companies uncertain. I've seen these changes and developments firsthand during my 37 years as an executive for global manufacturing operations -- most recently for a product design, development and manufacturing firm.

In the not too distant future, we can expect to see our skies filled with unmanned aerial vehicles (UAVs) delivering packages, maybe even people, from location to location. In such a world, there will also be a digital twin for each UAV in the fleet: a virtual model that will follow the UAV through its existence, evolving with time. "It's essential that UAVs monitor their structural health," said Karen Willcox, director of the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin (UT Austin) and an expert in computational aerospace engineering. "And it's essential that they make good decisions that result in good behavior." An invited speaker at the 2019 International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), Willcox shared the details of a project--supported primarily by the U.S. Air Force program in Dynamic Data-Driven Application Systems (DDDAS)--to develop a predictive digital twin for a custom-built UAV.

This week, astronauts on the International Space Station got a new helper that goes by the nickname'Simon.' More formally known as Crew Interactive Mobile Companion 2 (CIMON 2), Simon was developed in a joint project by IBM's Watson team, Airbus, and the German Aerospace Center. The Crew Interactive Mobile Companion 2 (CIMON 2, pronounced'Simon,' pictured above) will help astronauts conduct experiments and talk through their feelings The robotic helper was delivered to the ISS on SpaceX's Dragon capsule launched from Cape Canaveral this week. Last year, an earlier model of CIMON was sent to the ISS, but the new version has been updated with AI enhancements that IBM says will make it more'emotionally intelligent.' 'The overall goal is to really create a true companion,' IBM's Matthias Biniok told ABC. 'The relationship between an astronaut and CIMON is really important.'

Cybersecurity is one of the many uses of artificial intelligence. Going by a recent report by Norton, the global cost to recover from a typical data breach is USD 3.86 million. Studies also conclude that it takes a whole 196 days to recover from any data breach. As such, it makes sense for companies to use AI to avoid both financial losses and waste of time. That said, this article highlights how AI can help in cybersecurity.

Our technical report provides an overview of the relevant parts of an ML lifecycle--selecting the right problem, the right data, and the right math and summarizing the model output for consumption--as well as questions that relate to those areas of focus. As the federally funded research and development center (FFRDC) known for AI engineering, and with its long experience in cybersecurity, the SEI has the expertise to advise you--the decision makers adopting these tools--on evaluating the adequacy of ML tools applied to cybersecurity. To that end, we structured the report around the questions you should ask about ML tools. We chose this framing, rather than proposing a detailed guide of how to build an ML system in cybersecurity, because we want to enable you to learn what a good tool looks like. When decision makers have difficulty identifying a good tool, the market will usually stop providing them.

The Age of Autonomy is upon us. Truly autonomous devices are quickly replacing those that are merely automated. This is a natural evolution of IoT due to autonomy becoming a necessity to handle the volume, velocity and veracity of real-time data being generated. The centralized solution architectures and services in our enterprise data centers today are not viable for the industry use cases that the Age of Autonomy brings so fundamental changes must be made. In this technical panel hosted by Kristof Kloeckner, experts in autonomy from IBM, The International Center of Automotive Research (ICAR), Vapor, Here, and W8Less will be discussing how the combination of Edge Computing, 5G and Micro-Positioning are quickly making the Age of Autonomy a reality.

The U.S. Department of Veterans Affairs has launched the National Artificial Intelligence Institute, with the aim of boosting the health and wellness of veterans through advanced AI and machine learning technologies. WHY IT MATTERS The institute will incorporate feedback from veterans and other partners across federal agencies, industry, nonprofits and academia, said VA officials. The goal is to "prioritize and realize" AI research and development that can help veterans and others. NAII is a joint initiative between VA's Office of Research and Development and Secretary's Center for Strategic Partnerships. Professionals there will design, execute and collaborate on strategies that build on the American AI Initiative and the National AI R&D Strategic Plan.

The Department of Veterans Affairs has opened a new artificial intelligence institute to pursue research and inform national strategy. The National Artificial Intelligence Institute, a joint initiative of the VA's office of research and development and the VA secretary's center for strategic partnerships, will work with public and private partners to carry out AI research and development projects, including efforts to apply AI to identify veterans at high risk for suicide or to help reduce patient wait times. The institute will also collaborate with federal agencies on national AI strategy. That includes building upon the American AI Initiative, the national AI strategy President Donald Trump established through an executive order in February. The American AI Initiative's goal is to promote AI innovation in numerous sectors, including healthcare.