Artificial Intelligence
Artificial Intelligence
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Technical committeeTypeAcronymISO/IEC TR 24029-1:2021CommitteePublished year2021KeywordsDescription
This document provides background about existing methods to assess the robustness of neural networks.
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Technical committeeTypeAcronymISO/IEC TR 24028:2020CommitteePublished year2020KeywordsDescription
This document surveys topics related to trustworthiness in AI systems, including the following:
- approaches to establish trust in AI systems through transparency, explainability, controllability, etc.;
- engineering pitfalls and typical associated threats and risks to AI systems, along with possible mitigation techniques and methods; and
- approaches to assess and achieve availability, resiliency, reliability, accuracy, safety, security and privacy of AI systems.
The specification of levels of trustworthiness for AI systems is out of the scope of this document. -
Technical committeeTypeAcronymISO/IEC TR 20547-5:2018CommitteePublished year2018KeywordsDescription
ISO/IEC TR 20547-5:2018 describes big data relevant standards, both in existence and under development, along with priorities for future big data standards development based on gap analysis.
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Technical committeeTypeAcronymISO/IEC TR 20547-2:2018CommitteePublished year2018KeywordsDescription
ISO/IEC TR 20547-2:2018 provides examples of big data use cases with application domains and technical considerations derived from the contributed use cases.
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Technical committeeTypeAcronymISO/IEC TR 20547-1:2020CommitteePublished year2020KeywordsDescription
This document describes the framework of the big data reference architecture and the process for how a user of the document can apply it to their particular problem domain.
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Technical committeeTypeAcronymISO/IEC 20547-3:2020CommitteePublished year2020KeywordsDescription
This document specifies the big data reference architecture (BDRA). The reference architecture includes concepts and architectural views.
The reference architecture specified in this document defines two architectural viewpoints:
- a user view defining roles/sub-roles, their relationships, and types of activities within a big data ecosystem;
- a functional view defining the architectural layers and the classes of functional components within those layers that implement the activities of the roles/sub-roles within the user view.
The BDRA is intended to:
- provide a common language for the various stakeholders;
- encourage adherence to common standards, specifications, and patterns;
- provide consistency of implementation of technology to solve similar problem sets;
- facilitate the understanding of the operational intricacies in big data;
- illustrate and understand the various big data components, processes, and systems, in the context of an overall big data conceptual model;
- provide a technical reference for government departments, agencies and other consumers to understand, discuss, categorize and compare big data solutions; and
- facilitate the analysis of candidate standards for interoperability, portability, reusability, and extendibility. -
Technical committeeTypeAcronymISO/IEC 20546:2019CommitteePublished year2019KeywordsDescription
This document provides a set of terms and definitions needed to promote improved communication and understanding of this area. It provides a terminological foundation for big data-related standards.
This document provides a conceptual overview of the field of big data, its relationship to other technical areas and standards efforts, and the concepts ascribed to big data that are not new to big data. -
Technical committeeTypeAcronymIEEE 7010-2020CommitteePublished year2020KeywordsDescription
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.
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Technical committeeTypeAcronymIEEE 62529-2012 - IEC 62529:2012€CommitteePublished year2012KeywordsDescription
Adoption of IEEE Std 1641-2010. This standard provides the means to define and describe signals used in testing. It also provides a set of common basic signals, built upon formal mathematical specifications so that signals can be combined to form complex signals usable across all test platforms.
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Technical committeeTypeAcronymIEEE 62243-2010CommitteePublished year2010KeywordsDescription
Data interchange and standard software services for test and diagnostic environments are defined by Artificial Intelligence Exchange and Service Tie to All Test Environments (AIESTATE). The purpose of AI-ESTATE is to standardize interfaces for functional elements of an intelligent diagnostic reasoner and representations of diagnostic knowledge and data for use by such diagnostic reasoners. Formal information models are defined to form the basis for a format to facilitate exchange of persistent diagnostic information between two reasoners and also to provide a formal typing system for diagnostic services. The services to control a diagnostic reasoned are defined by this standard.