CYC is a large-scale investment in knowledge infrastructure, built over a person-century since 1984. It contains approximately 10^5 general concepts and 10^6 commonsense axioms, with millions more inferred. The project aimed to create a universal schema of knowledge that could standardize and improve information retrieval, integration, and consistency checking. CYC is viewed as an expert system with a domain spanning all everyday objects and actions, providing fundamental knowledge that is not typically found in textbooks or encyclopedias. The project avoided natural language understanding and machine learning, instead manually crafting a million axioms to create a critical mass of knowledge. This allowed for further knowledge collection through NLU and ML. CYC handles complex concepts like causality, time, space, and intention, and represents them in a way that enables efficient reasoning. It uses a first-order predicate calculus with second-order extensions, allowing for more expressive and flexible knowledge representation. CYC's knowledge is organized into contexts, which are explicit and allow for different assumptions and assertions. This approach enables the system to handle ambiguity and context-specific information. The project also faced challenges in representing complex relationships and has developed methods for browsing and editing large knowledge bases. CYC has various applications, including information retrieval, word processing, and simulations. It can help in dynamically linking heterogeneous information sources, enabling more accurate and context-sensitive information retrieval. It also supports content-checking, helping to identify inconsistencies and violations of common sense. CYC can improve speech recognition and natural language understanding by providing a semantic backbone for checking the sanity of understood sentences. CYC is essential for applications that require commonsense knowledge, as it provides a comprehensive and structured representation of knowledge that is not found in traditional repositories. The project has faced challenges in creating a scalable and efficient knowledge representation system, but its approach has led to significant advancements in AI and knowledge engineering. The future of CYC lies in its ability to integrate with other technologies, such as neural networks and decision theory, to create a more powerful and flexible system.CYC is a large-scale investment in knowledge infrastructure, built over a person-century since 1984. It contains approximately 10^5 general concepts and 10^6 commonsense axioms, with millions more inferred. The project aimed to create a universal schema of knowledge that could standardize and improve information retrieval, integration, and consistency checking. CYC is viewed as an expert system with a domain spanning all everyday objects and actions, providing fundamental knowledge that is not typically found in textbooks or encyclopedias. The project avoided natural language understanding and machine learning, instead manually crafting a million axioms to create a critical mass of knowledge. This allowed for further knowledge collection through NLU and ML. CYC handles complex concepts like causality, time, space, and intention, and represents them in a way that enables efficient reasoning. It uses a first-order predicate calculus with second-order extensions, allowing for more expressive and flexible knowledge representation. CYC's knowledge is organized into contexts, which are explicit and allow for different assumptions and assertions. This approach enables the system to handle ambiguity and context-specific information. The project also faced challenges in representing complex relationships and has developed methods for browsing and editing large knowledge bases. CYC has various applications, including information retrieval, word processing, and simulations. It can help in dynamically linking heterogeneous information sources, enabling more accurate and context-sensitive information retrieval. It also supports content-checking, helping to identify inconsistencies and violations of common sense. CYC can improve speech recognition and natural language understanding by providing a semantic backbone for checking the sanity of understood sentences. CYC is essential for applications that require commonsense knowledge, as it provides a comprehensive and structured representation of knowledge that is not found in traditional repositories. The project has faced challenges in creating a scalable and efficient knowledge representation system, but its approach has led to significant advancements in AI and knowledge engineering. The future of CYC lies in its ability to integrate with other technologies, such as neural networks and decision theory, to create a more powerful and flexible system.