One way we can boil down how generative AI is used in cybersecurity is to divide it into the following six categories (ones that can also be considered for broader machine learning/AI use cases as well): How is Generative AI Used in Cybersecurity Compared to its Other, More Popular Uses? Generative AI can be used in cybersecurity to enhance real-time … Read More
What Are AI Workloads? Types, Resources, Algos
AI workloads are specialized computing tasks designed to develop, train, and deploy artificial intelligence models, ranging from lightweight CPU-based tasks to large-scale data processing and high-performance computing. An AI workload is any job, big or small, that a system must execute to build, run, or maintain an AI model. Artificial intelligence isn’t just one simple thing—it’s a mix of many … Read More
How AI Is Used in Cybersecurity, it’s Role and Applications
AI is used in cybersecurity for threat detection, incident response automation, biometric and identity security, vulnerability management, behavioral pattern analyzation, and for malware and phishing detection. AI solutions can help identify anomalies faster and often more accurately than traditional methods, depending on data quality and deployment context. By leveraging machine learning algorithms, AI helps organizations proactively defend against evolving cyber threats while reducing false … Read More
How Edge Computing Impacts Real-Time AI: Exploring Benefits and Challenges
Edge computing significantly impacts real-time AI by moving data processing, analysis, and decision-making from centralized cloud servers to the “edge” of the network—physically closer to where data is generated (sensors, cameras, IoT devices). Edge computing impacts latency, privacy, bandwidth, and reliability. Edge computing significantly enhances real-time AI by processing data locally near the source, reducing latency and enabling immediate decision-making … Read More
Is AI Data Science? Exploring the Technology and Trends
AI and data science are closely related, but they are not the same thing. Think of them as overlapping fields within the broader area of computing and analytics. Artificial Intelligence Artificial Intelligence focuses on building systems that can perform tasks that typically require human intelligence, such as recognizing patterns, understanding language, generating content, or making predictions. Examples of AI include: … Read More
Why Ampere Processors are Optimal for AI
Ampere® processors are well-suited for high-volume data workflows such as AI inference and training. They feature single-threaded design, high core counts, and native support for FP16 via Arm SIMD instructions, which can improve throughput in AI workloads, reducing contention caused by simultaneous multi-threading (SMT) found in other processors, enabling efficient parallel processing. Additionally, they are cost-effective for CPU-based inference and … Read More
Which Edge Hardware Works Best for AI Workloads
The best edge computing solution for AI workloads depends on the complexity and scale of your tasks: microcontrollers and smart sensors excel at minimal, low-power processing; compact edge devices like NVIDIA Jetson deliver strong acceleration for computer vision and machine learning; while portable edge workstations provide high-performance computing for complex models in harsh environments. For most mid-to-high complexity AI applications, … Read More
Edge Computing and AI Inference with Ampere Cloud Native Processors
NextComputing’s Ampere Edge appliances and Fly Away Kits take full advantage of Ampere’s AI-friendly CPU’s, making AI inference faster, cheaper, and more energy-efficient in the cloud and at the edge. Ampere’s white paper, AI Inference with Ampere Cloud Native Processors (PDF), explains how and why their CPU’s are superior for AI inferencing. AI consists of two critical components: training and … Read More
How AI Enhances Cybersecurity: Defensive Measures, Tools, & Challenges
AI enhances cybersecurity efforts by improving threat detection, automating responses and repetitive security tasks, uncovering trends, and analyzing vast amounts of security data faster than humans using traditional methods. AI dramatically improves cybersecurity efforts with it’s speed, adaptation, and analytical abilities. Key applications include identifying phishing attempts, managing vulnerabilities through behavioral analytics, protecting endpoints against malware, password protection and authentication, … Read More
Will AI Replace Cybersecurity?
No, AI won’t entirely replace cybersecurity, but it will fundamentally alter it. AI is transforming cybersecurity by automating many routine tasks like vulnerability detection and threat analysis, significantly enhancing efficiency. However, it will likely not fully replace cybersecurity professionals anytime soon, as human expertise remains essential for complex decision-making, contextual understanding, ethical and contextual judgement, and responding to novel threats that … Read More