We are in a transformative era, and advances in Artificial Intelligence (AI), especially the foundational models, are constantly in the news. AI has been an integral part of many applications that rely on automation for service delivery, and one of them is mission-critical public safety applications. The problem with AI-oriented mission-critical applications is the humanin-the-loop system and the lack of adaptability to dynamic conditions while maintaining situational awareness. Agentic AI (AAI) has gained a lot of attention recently due to its ability to analyze textual data through a contextual lens while quickly adapting to conditions. In this context, this paper proposes an AAI framework for mission-critical applications. We propose a novel framework with a multi-layer architecture to realize the AAI. We also present a detailed implementation of AAI layer that bridges the gap between network infrastructure and missioncritical applications. Our preliminary analysis shows that the AAI reduces initial response time by 5.6 minutes on average, while alert generation time is reduced by 15.6 seconds on average and resource allocation is improved by up to 13.4%. We also show that the AAI methods improve the number of concurrent operations by 40, which reduces the recovery time by up to 5.2 minutes. Finally, we highlight some of the issues and challenges that need to be considered when implementing AAI frameworks.

The development of satellite constellation applications is rapidly advancing due to increasing user demands, reduced operational costs, and technological advancements. However, a significant gap in the existing literature concerns reconfiguration and retasking issues within satellite constellations, which is the primary focus of our research. In this work, we critically assess the impact of satellite failures on constellation performance and the associated task requirements. To facilitate this analysis, we introduce a system modeling approach for GPS satellite constellations, enabling an investigation into performance dynamics and task distribution strategies, particularly in scenarios where satellite failures occur during mission-critical operations. Additionally, we introduce reinforcement learning (RL) techniques, specifically Q-learning, Policy Gradient, Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), for managing satellite constellations, addressing the challenges posed by reconfiguration and retasking following satellite failures. Our results demonstrate that DQN and PPO achieve effective outcomes in terms of average rewards, task completion rates, and response times.

Computer vision and intelligent video solutions now have the potential to revolutionize industries, improve operations, and enhance the quality of life for citizens. And with recent advancements in artificial intelligence and machine learning, it's easier and more accessible for businesses to unleash these solutions and compete in today's fast-paced, data-driven world. But developing and deploying computer vision and intelligent video applications at the edge requires strategic partnerships within the ecosystem. Intel systems integrators and IoT solution aggregators, for example, provide critical expertise and technologies necessary for success. You can see this for yourself at the International Security Conference & Exposition, ISC West, taking place March 28 to 31 at the Venetian Expo in Las Vegas.

EIZO has released an article explaining the benefits NVIDIA's Ampere brings to its embedded systems to support both traditional render applications as well as GPGPU operations like target detection using both traditional and AI/ML methods. Autonomous vehicles such as UAVs must have the highest efficiency computing systems to ensure accurate vision and perception in SWaP (Size, Weight, and Power)-restricted environments while not sacrificing performance. High-Performance Computing (HPC) systems running at the edge require the utmost powerful GPGPU processing hardware per watt to assure advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms can execute on the expected workflows. Most importantly, lives depend on the systems used in mission-critical environments. The computer systems completing the sensor analysis and mathematical calculations require extreme accuracy to ensure missions are safe and successful.

Just 10 years ago, most application development testing strategies focused on unit testing for validating business logic, manual test cases to certify user experiences, and separate load testing scripts to confirm performance and scalability. The development and release of features were relatively slow compared to today's development capabilities built on cloud infrastructure, microservice architectures, continuous integration and continuous delivery (CI/CD) automations, and continuous testing capabilities. Furthermore, many applications are developed today by configuring software as a service (SaaS) or building low-code and no-code applications that also require testing the underlying business flows and processes. Agile development teams in devops organizations aim to reduce feature cycle time, increase delivery frequencies, and ensure high-quality user experiences. The question is, how can they reduce risks and shift-left testing without creating new testing complexities, deployment bottlenecks, security gaps, or significant cost increases?

Making AI more'humble' could not only help improve AI decision making, but could also help inspire ... [ ] more trust in the technology as a whole, and open the door for more useful and mission-critical applications in the future. AI is notoriously difficult to explain, and some deep learning algorithms can be too complex for even their creators to understand their reasoning. This makes it hard to trust what AI is doing, and even harder to find mistakes before it's too late. Having an algorithm stop partway through its reasoning to check with a human-in-the-loop could inspire more trust in AI, and open the door for the technology to be used in more sensitive and mission-critical applications. Injecting some'humility' into AI in this way could not only make AI more trustworthy and change how companies think about AI, but it could also help to demystify AI and reveal it as the logical and reliable technology that it is.

AI has already begun to automate many non-mission critical business processes, including aspects of customer service and human resources. As the technology advances, new opportunities continue to emerge, in particular AI's ability to automate the movement to, and management of mission-critical workloads on hybrid cloud environments. Many businesses--especially those in highly regulated industries such as telecom, financial services and healthcare--are hesitant to move mission-critical workloads to the cloud. In fact, data from multiple sources reveals that only 20 percent of all workloads have moved to the cloud. Businesses further along in their journey understand the benefits of cloud use and often have already turned to the cloud for non-mission critical workloads. The accelerated proliferation of mission-critical applications--combined with the fact that more than 70 percent of organizations using public cloud are working with multiple vendors--means companies must approach the migration of these applications to a hybrid cloud environment using a four-phased approach: advise, move, build and manage.

OverOps, the leading Continuous Reliability solution, announced the general availability (GA) of platform support for applications built on .NET Core and Microsoft .NET Framework. This expanded language compatibility builds on existing OverOps support for Java to cater to the wide variety of enterprises building mission-critical applications in C# and other .NET-based languages. With OverOps, engineering teams can better identify when rapid code changes introduce critical issues, and resolve them quickly to prevent negative impact on customer experience. "Historically, it's been challenging to identify and resolve critical issues in our code," said Torsten Sinnemann, Product Tech Lead at iQmetrix. "With the release of OverOps for .NET, we are looking forward to getting deeper access to the wealth of unknown information lurking within our applications."

San Jose, Calif., October 1, 2019 – Virtual Instruments, the leader in hybrid infrastructure management for mission-critical workloads, announced today its participation at HitachiNEXT 2019, taking place October 8-10 in Las Vegas, and NetApp INSIGHT 2019, taking place October 28-30 in Las Vegas. By holistically monitoring, analyzing and optimizing the performance, availability, capacity and efficiency of hybrid IT infrastructure within the context of the application, VirtualWisdom enables enterprises to take a modern, AIOps-empowered approach to infrastructure management. The latest version of VirtualWisdom applies real-time, AI-based analytics to help enterprises proactively manage the hybrid infrastructure supporting their mission-critical applications. Meanwhile, WorkloadWisdom analyzes production storage workloads, models workloads, creates what-if testing scenarios, and produces workload performance analytics, ultimately offering users better insight into how workload behavior affects storage system performance. NEXT 2019 is the largest annual event for Hitachi Vantara users, with a focus on sharing the best ideas to store, protect, enrich, activate and monetize data.

How can we trust the networks?" They answered: "We know that a network is quite reliable when its inputs come from its training set. But these critical systems will have inputs corresponding to new, often unanticipated situations. There are numerous examples where a network gives poor responses for untrained inputs." David Lorge Parnas followed up on this discussion in his Letter to the Editor (Feb. We wish to point out that machine learning-based systems, including commercial ones performing safety critical tasks, can fail not only under "unanticipated situations" (noted by Lewis and Denning) or "when it encounters data radically different from its training set" (noted by Parnas), but also under normal situations, even on data that is extremely similar to its training set. The Apollo self-driving team confirmed "it might happen" because the system was "deep learning trained." Now, after a further investigation, we have found that in 24 of these 27 failed tests, the 10 random points ...