Update efficient_ai.qmd

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efficient_ai.qmd

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 # Efficient AI
 
+Efficiency in artificial intelligence (AI) is not simply a luxury; it is a necessity. In this chapter, we dive into the key concepts that underpin efficiency in AI systems. The computational demands placed on neural networks can be daunting, even for the most minimal of systems. For AI to be seamlessly integrated into everyday devices and essential systems, it must perform optimally within the constraints of limited resources, all while maintaining its efficacy. The pursuit of efficiency guarantees that AI models are streamlined, rapid, and sustainable, thereby widening their applicability across a diverse array of platforms and scenarios.
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 ## Learning Objectives
 
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 ## Introduction
 
-In this chapter, we dive into the concepts that govern efficiency in AI systems. It is of paramount importance, especially in the context of embedded TinyML systems. The computational demands of neural networks can be overwhelming, even in the smallest of systems. Efficiency in AI isn't just a luxury---it's a necessity. For AI to truly be integrated into everyday devices and critical systems, it must operate within the constraints of limited resources without compromising its effectiveness. The drive for efficiency ensures that AI models are lean, fast, and sustainable, making their application viable across a broader range of platforms and scenarios. 
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 Training models can consume a significant amount of energy, sometimes equivalent to the carbon footprint of sizable industrial processes. We will cover some of these sustainability details in the [AI Sustainability](./sustainable_ai.qmd) chapter. On the deployment side, if these models are not optimized for efficiency, they can quickly drain device batteries, demand excessive memory, or fall short of real-time processing needs. Through this introduction, our objective is to elucidate the nuances of efficiency, setting the groundwork for a comprehensive exploration in the subsequent chapters. 
 
 ## The Need for Efficient AI
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 Together, these form a holistic framework for efficient AI. But the journey doesn't end here. Achieving optimally efficient intelligence requires continued research and innovation. As models become more sophisticated, datasets grow larger, and applications diversify into specialized domains, efficiency must evolve in lockstep. Measuring real-world impact would need nuanced benchmarks and standardized metrics beyond simplistic accuracy figures. 
 
-Moreover, efficient AI expands beyond technological optimization but also encompasses costs, environmental impact, and ethical considerations for the broader societal good. As AI permeates across industries and daily lives, a comprehensive outlook on efficiency underpins its sustainable and responsible progress. The subsequent chapters will build upon these foundational concepts, providing actionable insights and hands-on best practices for developing and deploying efficient AI solutions.
+Moreover, efficient AI expands beyond technological optimization but also encompasses costs, environmental impact, and ethical considerations for the broader societal good. As AI permeates across industries and daily lives, a comprehensive outlook on efficiency underpins its sustainable and responsible progress. The subsequent chapters will build upon these foundational concepts, providing actionable insights and hands-on best practices for developing and deploying efficient AI solutions.