The convergence of edge computing with Artificial Intelligence (AI)
constitutes a watershed point in technological growth, with transformational
implications for a wide range of sectors. This chapter explores the mutually beneficial
interaction between edge computing and AI, as well as the inherent difficulties and
broad implications for the direction of computing in the future. With its decentralized
processing model, edge computing puts data analysis closer to the point of origin,
facilitating improved efficiency and real-time insights. Edge devices enable
sophisticated cognitive processes and autonomous decision-making through seamless
integration with AI algorithms. However, this integration creates architectural
challenges that call for creative solutions to handle privacy and security issues and
strike a balance between processing power and other resources. The chapter examines
how edge computing-enabled AI can revolutionize manufacturing, transportation,
healthcare, and smart cities. It shows how AI can change predictive maintenance,
individualized healthcare monitoring, and urban infrastructure optimization. New
advances offer avenues for collaborative applications and decentralized AI training.
Federated learning models, the spread of edge AI chips and algorithms, and the
democratization of AI capabilities are some of these themes. In summary, this chapter
provides direction on navigating the rapidly evolving area of artificial intelligence (AI)
facilitated by edge computing, encouraging collaboration and innovation to fully
achieve AI's transformative potential and address digital age concerns.
Keywords: Cognitive edge devices, Decentralized processing, Edge AI chips, Federated learning, Predictive maintenance, Real-time analytics.
