ISO 37100:2016 defines terms relating to sustainable development in communities, smart community infrastructure and related subjects.
As accelerating improvements in city services and quality of life is fundamental to the definition of a smart city ISO 37120 is intended to provide a complete set of indicators to measure progress towards a smart city.
Avatar Representation and Animation (ARA) is a Technical Specification being developed to provide data format specifications enabling a party to represent and animate an avatar transmitted by another independent party. The goal is represented by the following use case: Avatar-Based Videoconference: avatars representing humans with a high degree of accuracy participate in a videoconference. A virtual secretary (VS) represented as an avatar displaying PS creates an online summary of the meeting with a quality enhanced by the VS’s ability to understand the PS of the avatar it converses with.
This document is the first of a planned series of technical documents designed to facilitate interoperability between Metaverse Instances.
Technical Report - MPAI Metaverse Model - Functionality Profiles is the second of a planned series of technical metaverse interoperability technical documents.
This document provides guidance for members of the governing body of an organization to enable and govern the use of Artificial Intelligence (AI), in order to ensure its effective, efficient and acceptable use within the organization. This document also provides guidance to a wider community, including: executive managers; external businesses or technical specialists, such as legal or accounting specialists, retail or industrial associations, or professional bodies; public authorities and policymakers; internal and external service providers (including consultants); assessors and auditors. This document is applicable to the governance of current and future uses of AI as well as the implications of such use for the organization itself. This document is applicable to any organization, including public and private companies, government entities and not-for-profit organizations. This document is applicable to an organization of any size irrespective of their dependence on data or information technologies.
This document defines a reference architecture of Knowledge Engineering (KE) in Artificial Intelligence (AI). The reference architecture describes KE roles, activities, constructional layers, components and their relationships among themselves and other systems from systemic user and functional views. This document also provides a common KE vocabulary by defining KE terms.
This document defines a set of processes and associated terminology for describing the life cycle of AI systems. This document forms the foundation of a detailed AI system life cycle specification. It is based on ISO/IEC/IEEE 15288 and ISO/IEC/IEEE 12207 with substitutes for and additions of AI specific processes, whose foundation is based on ISO/IEC 22989 and ISO/IEC 23053. Selected sets of these processes can be applied throughout the life cycle for managing and performing the stages of an AI system's life cycle. This document provides processes that support the definition, control and improvement of the AI system life cycle processes used within an organization or a project. Organizations and projects can use these processes when developing or acquiring AI systems. When an element of an AI system is traditional software or a traditional system, the software life cycle processes in ISO/IEC/IEEE 12207 and the system life cycle processes in ISO/IEC/IEEE 15288 may be used to implement that element.
This document provides a set of guidelines for identifying the context, opportunities, and processes for developing and applying AI applications. It can be used by ISO, IEC, and JTC1 Technical Committees and Sub-Committees to build on this work in developing standards for AI applications in their areas of interest. The guidelines provide a macro level view of the AI application context, the stakeholders and their roles, relationship to the life cycle of the system, and common AI application characteristics. The guidelines will reference but not duplicate or overlap other AI-related standards to build details.
This Technical Report describes general considerations to be taken for measurement of user’s Quality of Experience (QoE) on multimedia conferencing services.
This standard specifies the terminology, definitions, and taxonomy for metaverse and related terms. This standard defines categories and levels of metaverse to provide clarification for metaverse users and a roadmap for metaverse developers.
ISO/TS 19158:2012 provides a framework for quality assurance specific to geographic information. It is based upon the quality principles and quality evaluation procedures of geographic information identified in ISO 19157 and the general quality management principles defined in ISO 9000. The framework defined in ISO/TS 19158:2012 enables a customer to satisfy itself that its suppliers, both internal and external, are capable of delivering geographic information to the required quality. Fundamental to the framework is the assurance of the supplier's ability to understand and meet the quality requirements. Through the quality assurance framework both the customer and the supplier are able to consider the quality required at the earliest opportunity in the production/update process. Principles and responsibilities of the relationship between the customer and the supplier that facilitate the framework are provided. The responsibility for the quality assessment procedure is shared between the customer and the supplier. ISO/TS 19158:2012 is applicable to customers and suppliers of all geographic information where the quality of the product may be impacted upon by the supplier's processes in any of the following scenarios:1) there is an agreement or legislation for the supply of data acquisition services,2) data acquisition services are being tendered for, and3) one or more suppliers exist in the supply chain.ISO/TS 19158:2012 is not applicable for the supply of legacy datasets or ?off the shelf' products where there is no further data production or update activity to manage.