Just a couple of years ago, most AI tools still felt experimental. Impressive enough to demo at a meeting but rarely integrated into daily workflows. Things have changed quickly. In 2026, artificial intelligence has woven itself into how we draft, code, design, research, manage teams, and communicate. The tools are no longer experimental. They’re operational.…
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Artificial intelligence (AI) and machine learning (ML) are continuing to become more mainstream and you’ll find the technology in everything from your smartphone apps and computer programs to smart tech and appliances, and automobiles (think self-driving cars). These technologies are no longer confined to scientific computing and statistical research but have, for the most part, become a…
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Machine learning (ML) and artificial intelligence (AI) are no longer limited to high-end servers or cloud platforms. Thanks to new developments in integrated circuits (IC) and software technology, it’s possible to implement ML algorithms and deep learning neural networks on tiny controllers and microcomputers. And these embedded devices installed at edges must no longer rely…
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Artificial intelligence crossed a critical threshold in 2026. AI is no longer confined to research labs or narrow pilot programs. It sits at the strategic core of nearly every major industry, with global AI spending projected to exceed $2 trillion in 2026 alone. That is a 36% year-over-year increase. Enterprise adoption has reached 88%, though…
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The artificial intelligence (AI) subscription market in 2026 has become one of the most saturated categories in consumer software. Nearly every major AI assistant now starts at around $20 per month for its flagship individual plan, making it challenging for users to understand which platform actually offers the best value for their needs. ChatGPT Plus,…
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Edge AI refers to deploying artificial intelligence (AI) algorithms and models directly on local devices like sensors, smartphones, or Internet-of-Things (IoT) devices. This approach includes edge computing, which processes data closer to where it’s generated rather than sending it to a centralized Cloud for analysis. According to a report by Gartner, by the end of…
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The demand for artificial Intelligence (AI) chips is increasing due to their suitability in certain AI applications. This development mirrors the rise of Graphic Processing Units (GPUs) for 3D applications years ago. However, traditional CPUs and even GPUs often fall short in handling the unique demands of AI workloads, prompting the emergence of AI-specific chips.…
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In the previous article, we discussed the different types of AI chips. Among these, ASICs play a significant role. Compared to GPUs, ASICs are faster and more power-efficient, thanks to their customized architectures designed for specific AI tasks or algorithms. One common misconception about AI ASICs is that they’re used solely for inference applications. However,…
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Machine learning (ML) is becoming increasingly important for microcontrollers because it enables smart and autonomous decision-making in embedded systems. The many Internet of Things (IoT) applications, often called “smart devices”, only become intelligent thanks to ML. Microcontrollers are commonly used in edge computing devices where data is processed locally rather than being sent…
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Artificial Intelligence (AI) is no longer confined to data centers. Today, AI is widely used and implemented in edge devices, smartphones, and embedded systems. This has been made possible through hardware and software acceleration methods that work together within embedded systems. It’s now feasible to run small machine-learning models on low-power, resource-constrained microcontroller units without…
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