On both the big and the small screen, we see AI-driven robotic overlords slowly but surely and ruthlessly taking over the human race. The so-called "robot AI insurgency" is the latest in a long line of human-robot conflicts in recent decades. New technologies are often driven by public fears, but the surest outcome of an artificial intelligence takeover is that it takes place in real life. While the US Department of Homeland Security's Office of Science and Technology Policy has only amazing things to say about the AI revolution, there are many AI experts who are openly speaking out against the negative effects that AI can have and calling on researchers to examine the societal impact of artificial intelligence. The recent publication of a report by the National Institute of Standards and Technologies (NIST) is particularly noteworthy.

Few things in the world of computing are receiving more buzz these days than Artificial Intelligence (AI), Machine Learning (ML), the Internet of Things (IoT), and Cloud Computing. Where once these were considered to be bleeding-edge technologies that only the most forward-thinking IT organizations were using, the adoption rates of these solutions are increasing exponentially, and more organizations are incorporating these once bleeding-edge technologies into their IT strategies by the day (so much so that I actually heard a commercial regarding IoT on sports talk radio the other day, but that's another story). This article will address how the need to defend the rapidly expanding web will fundamentally change both the security tools we use as well as the data center environment itself. As IoT becomes more-and-more established, the number of devices and other assets connected to the internet grows exponentially. This inevitably increases the potential attack surface for ransomware campaigns and other types of cyber-attack.

Disruptive technologies drive doctrinal and operational changes for modern militaries. Particularly in the last 120 years, industrial and technological advancements have revolutionized warfare. In the last century, the United States military has developed a military machine that is centered on a guiding command-and-control principle: centralized control, decentralized execution. However, the next decade will bring significant advancements in autonomous decision-making and artificial intelligence, chauffeuring in resilient, distributed command and control and dislodging the human operator from mission command. While artificial intelligence and autonomous systems are already broadly employed in health, transportation and digital services, true autonomy in military systems is still in development.

Injecting malicious backdoors into deep neural networks is easier than previously thought, a new study by researchers at Texas A&M University shows. There's growing concern about the security implications of deep learning algorithms, which are becoming an integral part of applications across different sectors. Vulnerabilities in deep neural networks (DNN), the main technology behind deep learning, has become a growing area of interest in recent years. Trojan attacks are hidden triggers embedded in neural networks that can cause an AI model to act erratically at the whim of a malicious actor. For instance, an attacker can fool the image processor of a self-driving car into bypassing a stop sign or mistaking it for a speed limit sign.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Self-guiding drone swarms, course-correcting ammunition, AI-driven sensor networks, self-healing armor and armies of armed robots are all things expected to characterize warfare for decades to come. That is the fundamental question now being taken up by an elite unit of scientists with Army Futures Command, a task force of sorts looking at global technological trends, areas of basic research and changing global conditions as indicators of what kinds of unanticipated warfare weapons, tactics and scenarios will emerge in coming decades. "There is a tech forecasting cell at the Army Research Lab ... not only from a U.S. Tech base but worldwide which looks at where the investments] are being made and where we are going to be in technology in 10 to 15 years," Gen. John Murray, Commanding General of Army Futures Command, told The National Interest in an interview several months ago.

If the US is going to build a quantum internet and otherwise claim technical supremacy, it's going to need appropriate funding -- and that might be forthcoming. As the Wall Street Journal reports, The White House has proposed a 2021 non-defense budget that includes a roughly 30 percent increase in spending on AI and quantum computing. It would spend about $1.5 billion on AI work (versus $1.12 billion in 2020) and $699 million on quantum technology (versus $579 million). About $25 million of that spending would go toward the quantum internet plan. Other funds would go toward AI research institutes from agencies like the Agriculture Department and National Science Foundation. Other elements of the proposal aren't as specific, but emphasize the need for AI in healthcare (particularly during the pandemic) and "advanced manufacturing" for the next wave of quantum computing devices.

Tedrake, a robotics researcher at MIT, has taken part in the challenge, and describes how hard it is to even get a robot in a car. What do you think about RoboSimian and its smooth yet creepy way of exiting a vehicle? And how do you think "Clyde" compares to Boston Dynamics' Atlas? Let us know your thoughts in the comments!

In this tutorial, we're going to show you how to take open source data from the National Oceanic and Atmospheric Administration (NOAA), clean it, and forecast future temperatures using no-code machine learning methods. This particular data comes from the Harmful Algal BloomS Observation System (HABSOS). There are several interesting questions to ask of this data -- namely, what is the relationship between algal blooms and water temperature fluctuations. For this tutorial, we're going to start with a basic question: can we predict what temperatures will be over the next five months? There are a lot of approaches to this; what is shown below is just one approach.

Before artificial intelligence wields weapons in real-world battle, the Pentagon needs to test its AIs in virtual environments. That's why DARPA, the Pentagon's blue-sky projects agency, is developing an AI that can communicate with humans in games, as well as manage and understand the peculiarities of a human team in action. See, central to the US military's enthusiastic embrace of artificial intelligence is the idea that AI and robotics can never replace human troops, but they can work with them in a human-machine team that is much […]

The cybersecurity landscape is looking at higher than ever threat levels, data volumes quadrupling every 36 months, computing power and data transfer speeds increasing just as fast, and a diversity of IoT devices ushering in a new era of automation. To get a grip on this, more organizations are exploring how AI can help. The Next-generation security operations center (SOC) incorporates automation and orchestration -- automation applied to both defense operations and threat hunting incorporating AI and machine learning, and orchestration managing how multiple sets of tools and platforms work together. "AI and ML are not only used in a next-generation SOC to enhance detection and prevention activities, but also, increasingly, to augment incident response actions such as containment actions, ticket creation, and user engagement to triage and/or validate a suspicious action," stated John Harrison, Director, Cybersecurity Center of Excellence for Criterion, in an article he wrote for Nextgov. "The applications of AI and ML reduce the time spent on each alert and improve the Mean Time to Detect as well as the Mean Time to Repair."