NextComputing

What is Cognitive Computing in AI?

Unlike traditional AI that focuses strictly on automation, cognitive computing mimics the human brain’s ability to reason, learn, and process complex data.

Key Highlights:
🔹 How it works: Uses natural language processing, machine learning, and pattern recognition to solve ambiguous problems.
🔹 Real-world applications: Transformed decision-making across healthcare diagnostics, financial fraud detection, and personalized customer experiences.
🔹 The Big Benefit: Enhances human intelligence rather than replacing it—enabling faster, data-driven decisions in unstructured environments.

Read the full breakdown here:

What Is Cognitive Computing in AI? Definition, Applications, and Benefits - NextComputing - Purpose-Built Computer Solutions Learn exactly what cognitive computing is, how it relates to AI, and how it is different from AI. Get expert-level knowledge today.

Edge AI isn't just about moving compute closer to data—it's about enabling split-second decisions where cloud latency isn't an option.

From bandwidth optimization and local threat detection to autonomous resource management, learn how bringing AI to the network edge is transforming real-time performance and resilience:

https://solutions.nextcomputing.com/ai/role-of-ai-in-edge-networking-key-applications-and-benefits-explained/

Role of AI in Edge Networking: Key Applications and Benefits Explained - NextComputing - Purpose-Built Computer Solutions The role of AI in edge networking is to enable real-time, intelligent data processing and decision-making closer to data sources such as IoT devices, smartphones, gateways, routers, and local servers, reducing latency and improving reliability and security. By deploying AI algorithms at the edge, ne...

Why wait for the cloud when milliseconds count? ⚡

Moving artificial intelligence to the "edge"—processing data directly on local devices instead of distant cloud servers—is transforming real-time applications.

🔹 Ultra-low latency: Reaction times drop from seconds to milliseconds.
🔹 Lower bandwidth costs: Process raw data locally; send only key insights upstream.
🔹 Stronger privacy & uptime: Keep sensitive data on-site and maintain reliability even offline.

From autonomous driving to industrial IoT and smart video surveillance, Edge AI makes immediate decision-making possible.

🔗 Read the full breakdown: https://solutions.nextcomputing.com/ai/how-can-edge-ai-reduce-latency-benefits-and-real-time-performance-explained/

How Can Edge AI Reduce Latency? Benefits and Real-Time Performance Explained - NextComputing - Purpose-Built Computer Solutions Edge AI reduces latency by performing data processing and AI inference directly on local edge devices, such as IoT sensors or smart cameras, rather than transmitting data to remote cloud servers. This localized computation minimizes network transmission delays, enabling faster real-time decision-mak...

How is Edge AI reshaping the video surveillance landscape?

Moving AI analysis from distant cloud servers directly onto edge hardware is transforming security systems into proactive, real-time response tools.

Key benefits outlined in our latest breakdown:
• Faster Alerting: Analyze video at the source to cut latency and respond immediately to threats or safety incidents.
• Bandwidth & Storage Savings: Process data locally and send only actionable metadata or critical clips instead of continuous raw video streams.
• Enhanced Privacy & Security: Sensitive footage stays on-site, reducing exposure and aligning with compliance standards like HIPAA.
• Fewer False Alarms: Differentiate between people, vehicles, and environmental motion to keep alerts relevant.

Read the full article to learn how Edge AI hardware and deep learning algorithms are driving the future of smart surveillance:
https://solutions.nextcomputing.com/blog/what-does-edge-ai-mean-in-video-surveillance-explanation-benefits-applications/

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What is AMD Ryzen Threadripper?

Built for demanding workloads like 3D rendering, video encoding, and AI tasks, AMD Ryzen Threadripper CPUs deliver high core counts and massive processing power for high-performance workstations.

🔗 Read the full overview & benchmarks:

What Is AMD Ryzen Threadripper? Performance Review & Benchmarks - NextComputing - Purpose-Built Computer Solutions AMD Ryzen Threadripper is a high-end desktop (HEDT) processor line designed primarily for professionals and enthusiasts who require extreme multi-threaded performance for tasks like high-volume data throughput, running simulations, video editing, and 3D rendering. Is it for gaming? If you’ve ever ...

Ready to level up your live streams? High-quality broadcasts require more than just hitting “Go Live.”

In their latest guide, NextComputing breaks down the essential gear needed to offer seamless, professional streaming.

• Core Processing: Smooth 1080p/4K streaming relies on hardware encoding (GPUs) or a dedicated streaming PC to prevent lagging, dropped frames, and system crashes during heavy multitasking.

• Video Sources: While webcams are fine for at-home streaming, professional users who do on-site streaming for events need professional hardware like a DSLR/mirrorless camera and a DeckLink capture card for smooth visuals.

• Audio Quality: Audio makes or breaks a stream! Use proper mic placement, pop filters, and consider XLR microphones paired with an interface/mixer for precise gain control and lower noise.

• Lighting & Backdrop: Softboxes or ring lights positioned slightly above eye level reduce harsh shadows, while acoustic treatment and clean backdrops (or green screens) give your studio a polished look.

• Test Before You Broadcast: Always run private test streams to verify audio levels (-12 dB to -6 dB), frame rate stability, audio/video sync, and upload speeds (aim for at least 10+ Mbps on Ethernet).

• Choose the Right Platform: Match your goals to the platform—Twitch for real-time interactive gaming, YouTube Live for long-term search discoverability, or Restream to simulcast everywhere at once.

Read the full guide here: https://solutions.nextcomputing.com/combined-solution/what-equipment-is-needed-for-live-streaming-essential-gear-and-setup-guide/

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Is your infrastructure ready for the edge?

Deploying AI locally means lower latency, increased security, and faster decision-making. We've compiled the must-have hardware and software specs to get you started on the right foot.

Learn more:

What’s Needed to Deploy AI at the Edge: Essential Hardware and Software Requirements - NextComputing - Purpose-Built Computer Solutions Deploying AI at the edge requires compatible hardware such as low-power microcontrollers or edge TPUs capable of local data processing, optimized AI models tailored for constrained environments using techniques like quantization and pruning, data sources like cameras or medical devices, and software...

How do you scale AI across thousands of physical stores without breaking the bank—or the network?

Modern retail edge AI infrastructure is shifting the heavy lifting away from distant cloud servers and placing it right on the shop floor. By deploying localized hyperconverged infrastructure (HCI), rugged edge nodes, and specialized accelerators, retailers are achieving ultra-low latency, bandwidth savings, and offline resilience.

NextComputing knows that to run these intensive AI models and handle massive datasets efficiently—especially out in the field or at the network edge—choosing the right high-performance hardware architecture is just as critical as choosing your software tools.

Infrastructure Used for Retail Edge AI: Hardware, Software, and Technology Explained - NextComputing - Purpose-Built Computer Solutions Retail edge AI infrastructure typically consists of localized computing hardware such as hyperconverged edge platforms, integrated with AI-capable devices like IoT sensors, cameras, and specialized low-power AI processors, along with orchestration and containerization, storage, and networking. (Lear...

Guesswork or gut feelings? There’s a smarter way to make critical business decisions.

A/B testing is the gold standard for data-driven choices, but pairing it with AI takes your optimization to the next level. From automating hypotheses to predicting high-impact wins and dynamically managing traffic with Multi-Armed Bandits, AI makes testing faster, sharper, and more precise.

Stop losing conversions while you wait for statistical significance. Let data—and intelligent automation—speak clearly.

Read our latest guide on how to integrate AI into your A/B testing framework:

How to Use AI for A/B Testing in Data Science - NextComputing - Purpose-Built Computer Solutions What if you made all your critical business decisions based on guesswork or gut feelings, how often would you get it right? In data science, A/B testing offers a smarter way: testing two versions of something to see which one works better. But what if this process could be faster, sharper, and smart...

As cyber threats grow more sophisticated, modern banks are moving past traditional security methods and turning to AI as an always-on sentinel to protect customer data and assets. By using machine learning models, financial institutions can detect anomalies, block fraud, and neutralize zero-day threats like deepfakes and advanced phishing in real time. AI also helps automate incident response to isolate breaches instantly, monitors for insider risks through behavioral analytics, and simplifies regulatory compliance—giving banks a proactive, fast-evolving shield against cybercriminals.

Read more at https://solutions.nextcomputing.com/ai/how-is-ai-used-in-cybersecurity-for-banks/