Perspectives on AI - Environmental, Social, and Technology Impact

Explore a comprehensive tutorial that delves into the environmental, social, and technological impacts of Artificial Intelligence on modern networks. Learn essential AI terminology and understand why current AI systems demand massive processing and electrical power. Discover how hyperscalers and new AI companies are addressing the challenge of creating Gigawatt-scale facilities and their strategies for securing reliable, clean energy sources. Gain insights into the taxonomy of AI, machine learning, and neural networks, while understanding AI's position in the Gartner Hype Cycle. Master the fundamentals of Large Language Models, GPU dominance, and the distinction between training and inference. Examine emerging AI technologies including non-tensor models, spiking neural networks, neuromorphic processors, and agentic AI. Investigate real-world power consumption challenges faced by countries like Singapore, Ireland, and Memphis, and analyze how major tech companies including Amazon, Google, Meta, Microsoft, Oracle, and xAI are tackling power supply challenges. Consider the limitations of renewable energy sources and explore the renewed interest in nuclear power as a potential solution for AI's growing energy demands.