Jeannette M. Wing's article discusses the importance of computational thinking in various fields and its educational implications. Computational thinking is defined as a problem-solving approach that draws on fundamental concepts of computing, including abstraction, automation, and human behavior. The essence of computational thinking lies in abstraction, which allows for the creation of symbolic representations that are more general than physical dimensions. This abstraction process introduces layers, enabling the development of complex systems through well-defined interfaces. Computational thinking is influencing research across disciplines, from statistics and biology to economics and the humanities. It is transforming fields by enabling the automation of abstractions and the handling of large datasets. The future of computational thinking promises deeper insights through more sophisticated abstractions, allowing for the modeling and analysis of complex systems at multiple scales. The article also addresses the educational challenge of making computational thinking accessible to everyone. It emphasizes the need for early and effective teaching methods, integrating computational concepts with practical tools like computers. The vision is to make computational thinking an integral part of childhood education, fostering a deeper understanding and application of these skills. Additionally, the article highlights the interplay between scientific questions, technological innovation, and societal demands in driving the field of computing. It discusses technological trends such as multi-core architecture, nanocomputing, and the rise of virtual worlds, as well as societal expectations for availability, reliability, and accessibility. The scientific challenges underlying these advancements are also explored, including questions about computability, intelligence, and the nature of information. Overall, the article underscores the importance of computational thinking in both academic and practical contexts, emphasizing its potential to drive innovation and solve complex problems.Jeannette M. Wing's article discusses the importance of computational thinking in various fields and its educational implications. Computational thinking is defined as a problem-solving approach that draws on fundamental concepts of computing, including abstraction, automation, and human behavior. The essence of computational thinking lies in abstraction, which allows for the creation of symbolic representations that are more general than physical dimensions. This abstraction process introduces layers, enabling the development of complex systems through well-defined interfaces. Computational thinking is influencing research across disciplines, from statistics and biology to economics and the humanities. It is transforming fields by enabling the automation of abstractions and the handling of large datasets. The future of computational thinking promises deeper insights through more sophisticated abstractions, allowing for the modeling and analysis of complex systems at multiple scales. The article also addresses the educational challenge of making computational thinking accessible to everyone. It emphasizes the need for early and effective teaching methods, integrating computational concepts with practical tools like computers. The vision is to make computational thinking an integral part of childhood education, fostering a deeper understanding and application of these skills. Additionally, the article highlights the interplay between scientific questions, technological innovation, and societal demands in driving the field of computing. It discusses technological trends such as multi-core architecture, nanocomputing, and the rise of virtual worlds, as well as societal expectations for availability, reliability, and accessibility. The scientific challenges underlying these advancements are also explored, including questions about computability, intelligence, and the nature of information. Overall, the article underscores the importance of computational thinking in both academic and practical contexts, emphasizing its potential to drive innovation and solve complex problems.