The AI Revolution: Why the Technology of Today Won’t Power the AI of the Future

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Artificial Intelligence (AI) is undergoing a rapid transformation, driven by advancements in hardware and software. Today, AI relies heavily on high-performance computing (HPC), GPUs, TPUs, ASICs, and optimised software frameworks. However, as AI models become more complex, the limits of current technology become apparent. This raises an important question: will the AI infrastructure we rely […]

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Understanding Different Learning Methods in AI

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Artificial Intelligence (AI) has transformed the way we interact with technology, enabling automation, decision-making, and predictive analytics across various industries. At the core of AI development are different learning methodologies that dictate how models learn from data. In this blog, we will explore the key learning methods used in AI, their typical applications, how they […]

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Network Architecture Design for AI Workloads

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Artificial Intelligence (AI) workloads are exerting escalating pressures on network infrastructure. As models increase in complexity, efficient data transmission, minimal latency, and high bandwidth are crucial for seamless operations. The architecture of AI networks must be meticulously crafted to enhance performance in both training and inference tasks. This article examines InfiniBand and Open Ethernet topologies, […]

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Why RAG is Important vs Fine-Tuning and Training

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In the swiftly changing realm of artificial intelligence, companies are pursuing the most effective methods to optimise Large Language Models (LLMs) for their specific needs. Although conventional techniques like fine-tuning and comprehensive training are prevalent, Retrieval-Augmented Generation (RAG) is developing as a more efficient and pragmatic alternative. This essay will examine the significance of RAG, […]

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