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How McDonald’s Is Using Edge AI to Redefine Fast Food Efficiency
Introduction While cloud-based AI has driven much of the progress in machine learning and automation, the future increasingly lies at the edge — closer to where data is generated. One high-profile example is McDonald’s. By deploying Edge AI at its drive-thru systems, the fast-food giant is reshaping the customer experience, reducing latency, and optimizing operations…
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Why the AI Industry Is Moving Beyond GPUs: Hardware Bottlenecks and the Rise of Alternatives
Introduction: Are GPUs Still the Future of AI? For over a decade, Graphics Processing Units (GPUs) have been the driving force behind artificial intelligence advancements. From training large language models to accelerating inference tasks, GPUs—especially those from NVIDIA—have dominated the AI hardware landscape. However, as models grow larger and global tensions reshape technology access, the…
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Semiconductor Geopolitics in the Age of AI
Why Semiconductors Matter in the Age of AI Modern AI technologies—especially deep learning—require the ability to process massive amounts of data quickly and efficiently. This processing power is enabled by high-performance semiconductors such as GPUs (Graphics Processing Units). For instance, services like ChatGPT or image-generating AI can respond in real time thanks to thousands or…
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Reservoir Computing: Overview, Real-World Uses, and Challenges
What Is Reservoir Computing? Reservoir computing is a powerful machine learning technique designed for processing time-series data efficiently. It uses the dynamic behavior of recurrent systems to process time-series data efficiently.Unlike traditional recurrent neural networks (RNNs)—a class of neural networks designed for sequential data—where all the weights are trained through backpropagation, reservoir computing keeps the…