Standards Database

This standard establishes a comprehensive framework for mitigating security risks, privacy leaking in the development, deployment, and use of generative pretrained AI models. The framework encompasses requirements concerning security risks, functional and non-functional requirements, and evaluation metrics such as transparency,…

This standard provides definitions, terms, frameworks, and general requirements for systems that apply pre-trained large language models (LLM) in the smart home industry. It covers foundational platforms, model capabilities, scenario applications, and safety requirements. © Copyright 2024 IEEE – All…

This standard specifies a domain-specific architecture for Large Language Models (LLMs) Management Platforms that includes: functional, service, and deployment requirements. © Copyright 2024 IEEE – All rights reserved.

This recommended practice follows a safety by design approach by providing recommendations for developing, deploying, and maintaining generative artificial intelligence models with adequate safeguards against child sexual abuse. The document provides a framework that expands child safety to the entire…

The recommended practice outlines a process model that leverages artificial intelligence imaging and data processing technologies for improved emissions information reporting. It provides textile producers with comprehensive data on fabrics, including their origins and metadata. This approach enables stakeholders to…

This recommended practice provides a holistic framework for AI foundation models. The framework allows sharing of the common computing resources, data and infrastructure in the usage(s) and application(s) of AI Foundation models. The detailed technical requirements for the holistic framework…

This standard provides: 1. Agent Components: The building blocks and architectural elements that constitute an educational Large Language Model (LLM) agent. 2. Agent Interoperability Protocols: A communication protocol for interaction between different LLM agents and other components in the educational…

This standard defines a comprehensive framework for federated machine learning of semantic information agents. It targets two primary layers: • Information Protocol Layer: This layer focuses on binary compression and protocol-level data structures for federated information exchange, with an emphasis…

This standard addresses the evaluation of safety, explainability, and stability of algorithms implementing autonomous driving levels, as defined by SAE J3016. The standard specifies the minimum requirements for experimental setups, data flow charts, algorithm pipelines, and automated quantitative evaluation metrics…

This standard defines and provides criteria to measure the capabilities of foundation models. The standard focuses on measurable and objective capabilities such as perception-oriented capabilities, cognitive capabilities, and learning capabilities. The standard provides guidelines for evaluating these capabilities, drawing inspiration…

This recommended practice provides an overview of Artificial Intelligence Generated Content (AIGC) technologies, levels of those AICG technologies and defines the following: • Recommended rules for levels of AIGC technologies; • A detailed recommended practice for levels of AIGC technologies;…

This standard addresses Artificial Intelligence (AI) risks such as malicious use, AI race, organizational risks, and rogue AI’s. The standard defines the relevant terminology, potential applications, and a risk abatement strategy for the behavior of AI models. The standard does…

This standard establishes a comprehensive set of criteria for the evaluation of Large Language Models (LLMs) and extends to multimodal models. Primarily, the standard provides a multi-dimensional evaluation framework for the users, teams, and enterprises who evaluate or use LLM…

This recommended practice provides a data processing framework for training large language models, refining the relevant terms and definitions. The recommended practice also provides data processing processes, methods, characteristics and performance evaluation during the pre-training and fine-tuning stages. © Copyright…

For the software engineering life cycle empowered by Generative Pre-trained Transformer (GPT) this recommended practice specifies: • a description and definitions, • the competency in each stage applying GPT, • levels of intelligence empowered by GPT, • intelligence level evaluation…