AI-specific Archives - Page 20 of 53

This document specifies requirements and provides guidance for establishing, implementing, maintaining and continually improving the quality for data used in…

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Last Updated: 18 Jul 2024

ITU-T Y.3179

Architectural framework for machine learning model serving in future networks including IMT-2020

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Internet of things Smart cities Telecom and network-based services AI-specific

Interoperability System architecture Interface and architecture

This Recommendation provides an architectural framework for machine learning (ML) models serving in future networks including IMT-2020, i.e., preparing and…

Published

Last Updated: 18 Jul 2024

GB/T 40691

Artificial intelligence – Affective computing user interface – Model

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Horizontal AI-specific Human-computer interaction Interface and architecture See more

This document gives a general model and interaction model based on the user interface of emotion computing, describing emotion representation,…

Published

Last Updated: 18 Jul 2024

Content Type

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 5: Data quality governance

Artificial intelligence — Functional safety and AI systems

Artificial Intelligence (AI) – Assessment of the robustness of neural networks – Part 1: Overview

Artificial intelligence — Quality evaluation guidelines for AI systems

Artificial intelligence (AI) — Assessment of the robustness of neural networks — Part 2: Methodology for the use of formal methods

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 4: Data quality process framework

Artificial Intelligence – Overview of AI tasks and functionalities related to natural language processing

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1: Overview, terminology, and examples

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 3: Data quality management requirements and guidelines

Architectural framework for machine learning model serving in future networks including IMT-2020

Artificial intelligence – Affective computing user interface – Model

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