This document defines a set of metadata to support the audit trail of the transformation of a geometry definition, while it is distributed and shared in supply-chains, to ensure the traceability of geometric model data. It also defines a set of web services based on the utilisation of these metadata. The following are within the scope of this document:(1) metadata definitions for geometry transformation audit trail:(2) syntax for storing these metadata in geometry data sets in various formats;(3) conformance level for implementers and business processes; and(4) definitions of web services to query the geometric model data set and its associated metadata.The following are outside the scope of this document:(1) service specifications for CAD operations;(2) specifications of a cybersecurity infrastructure to enable web services;(3) the technical implementation of a STEP geometry services client or server;(4) any geometric model definition;(5) any product and manufacturing information (PMI) definition; and(6) archiving.
This document establishes terminology for AI and describes concepts in the field of AI. This document can be used in the development of other standards and in support of communications among diverse, interested parties or stakeholders. This document is applicable to all types of organizations (e.g. commercial enterprises, government agencies, not-for-profit organizations).
The standard establishes requirements for blockchain based digital identity systems. The standard addresses the following attributes of the system, including but not limited to, digital identity definition, distributed digital identity creation, distributed digital identity authentication, distributed digital identity note (refers to identity credentials such as identity card, work card, member card), data or asset circulation protocols.
This standard defines a model for ethical considerations and practices in the design, creation and use of empathic technology, incorporating systems that have the capacity to identify, quantify, respond to, or simulate affective states, such as emotions and cognitive states. This includes coverage of 'affective computing', 'emotion Artificial Intelligence' and related fields.
This document is the first of a family of standards. ISO/IEC 8632 provides a file format suitable for the storage and retrieval of picture description information. The file format consists of an ordered set of elements that may be used to describe pictures in a way that is compatible between systems of different architectures, compatible with devices of differing capabilities and design, and meaningful to application constituencies. This picture description includes the capability for describing static images. The elements specified provide for the representation of a wide range of pictures on a wide range of graphical devices. The elements are organized into groups that delimit major structures (metafiles, pictures, and application structures), that specify the representations used within the metafile, that control the display of the picture, that perform basic drawing actions, that control the attributes of the basic drawing actions, that allow application-specific structuring to be overlaid on the graphical content, and that provide access to non-standard device capabilities. The metafile is defined in such a way that, in addition to sequential access to the whole metafile, random access to individual pictures and individual context-independent application structures is well-defined. Applications which require random access to pictures and/or context-independent application structures within pictures may, within the metafile, define directories to these pictures and/or context-independent application structures. The metafile may then be opened and randomly accessed without interpreting the entire metafile. In addition to a functional specification, two standard encodings of the metafile syntax are specified. These encodings address the needs of applications that require small metafile size plus minimum effort to generate and interpret, and maximum flexibility for a human reader or editor of the metafile. This part of ISO/IEC 8632 describes the format using an abstract syntax. The remaining parts of ISO 8632 specify standardized encodings that conform to this syntax: ISO/IEC 8632-3 specifies a binary encoding; ISO/IEC 8632-4 specifies a clear text encoding.
The impact of artificial intelligence or autonomous and intelligent systems (A/IS) on humans is measured by this standard. The positive outcome of A/IS on human well-being is the overall intent of this standard. Scientifically valid well-being indices currently in use and based on a stakeholder engagement process ground this standard. Product development guidance, identification of areas for improvement, risk management, performance assessment, and the identification of intended and unintended users, uses and impacts on human well-being of A/IS are the intents of this standard.
Guidance to developers of IEEE 1232 - conformant applications is provided in this guide. A simple doorbell is used as an example system under test to illustrate how the static model constructs of Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE) are used to form a diagnostic reasoner knowledge base. Each of AI-ESTATE's knowledge base types is discussed in conceptual terms, and how those concepts are represented in exchange files is shown. Also, some of the nuanced aspects of diagnostic knowledge bases in AI-ESTATE are clarified. An example reasoner session is provided to illustrate the use of AI-ESTATE services.
A framework of knowledge graphs is proposed in this standard. The knowledge graph conceptual model, construction and integration process of knowledge graphs, main activities in the processes, and stakeholders of knowledge graphs are described in detail. This standard can be applied in various organizations that plan, design, develop, implement, and apply knowledge and in organizations that develop support technologies, tools, and services to knowledge graphs.
Artificial intelligence (AI) computing differs from generic computing in terms of device formation, operators, and usage. AI server systems, including AI server, cluster, and high-performance computing (HPC) infrastructures are designed specifically for this purpose. The performance of these infrastructures is important to users not only on generic models but also on the ones for specific domains. Formal methods for the performance benchmarking for AI server systems are provided in this standard, including approaches for test, metrics, and measure. In addition, the technical requirements for benchmarking tools are discussed.
The AI development interface, AI model interoperable representation, coding format, and model encapsulated format for efficient AI model inference, storage, distribution, and management are discussed in this standard.
Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the architectural framework and application guidelines for federated machine learning, including description and definition of federated machine learning; the categories federated machine learning and the application scenarios to which each category applies; performance evaluation of federated machine learning; and associated regulatory requirements.