ISO/IEC 22989:2022
Information technology — Artificial intelligence — Artificial intelligence concepts and terminology
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).
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Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
3.1 Terms related to AI
3.2 Terms related to data
3.3 Terms related to machine learning
3.4 Terms related to neural networks
3.5 Terms related to trustworthiness
3.6 Terms related to natural language processing
3.7 Terms related to computer vision
4 Abbreviated terms
5 AI concepts
5.1 General
5.2 From strong and weak AI to general and narrow AI
5.3 Agent
5.4 Knowledge
5.5 Cognition and cognitive computing
5.6 Semantic computing
5.7 Soft computing
5.8 Genetic algorithms
5.9 Symbolic and subsymbolic approaches for AI
5.10 Data
5.11 Machine learning concepts
5.11.1 Supervised machine learning
5.11.2 Unsupervised machine learning
5.11.3 Semi-supervised machine learning
5.11.4 Reinforcement learning
5.11.5 Transfer learning
5.11.6 Training data
5.11.7 Trained model
5.11.8 Validation and test data
5.11.9 Retraining
5.12 Examples of machine learning algorithms
5.12.1 Neural networks
5.12.2 Bayesian networks
5.12.3 Decision trees
5.12.4 Support vector machine
5.13 Autonomy, heteronomy and automation
5.14 Internet of things and cyber-physical systems
5.14.1 General
5.14.2 Internet of things
5.14.3 Cyber-physical systems
5.15 Trustworthiness
5.15.1 General
5.15.2 AI robustness
5.15.3 AI reliability
5.15.4 AI resilience
5.15.5 AI controllability
5.15.6 AI explainability
5.15.7 AI predictability
5.15.8 AI transparency
5.15.9 AI bias and fairness
5.16 AI verification and validation
5.17 Jurisdictional issues
5.18 Societal impact
5.19 AI stakeholder roles
5.19.1 General
5.19.2 AI provider
5.19.3 AI producer
5.19.4 AI customer
5.19.5 AI partner
5.19.6 AI subject
5.19.7 Relevant authorities
6 AI system life cycle
6.1 AI system life cycle model
6.2 AI system life cycle stages and processes
6.2.1 General
6.2.2 Inception
6.2.3 Design and development
6.2.4 Verification and Validation
6.2.5 Deployment
6.2.6 Operation and monitoring
6.2.7 Continuous validation
6.2.8 Re-evaluation
6.2.9 Retirement
7 AI system functional overview
7.1 General
7.2 Data and information
7.3 Knowledge and learning
7.4 From predictions to actions
7.4.1 General
7.4.2 Prediction
7.4.3 Decision
7.4.4 Action
8 AI ecosystem
8.1 General
8.2 AI systems
8.3 AI function
8.4 Machine learning
8.4.1 General
8.5 Engineering
8.5.1 General
8.5.2 Expert systems
8.5.3 Logic programming
8.6 Big data and data sources — cloud and edge computing
8.6.1 Big data and data sources
8.6.2 Cloud and edge computing
8.7 Resource pools
8.7.1 General
8.7.2 Application-specific integrated circuit
9 Fields of AI
9.1 Computer vision and image recognition
9.2 Natural language processing
9.2.1 General
9.2.2 Natural language processing components
9.3 Data mining
9.4 Planning
10 Applications of AI systems
10.1 General
10.2 Fraud detection
10.3 Automated vehicles
10.4 Predictive maintenance
Annex A (informative) Mapping of the AI system life cycle with the OECD’s definition of an AI system life cycle
Bibliography
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Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
3.1 Terms related to AI
3.2 Terms related to data
3.3 Terms related to machine learning
3.4 Terms related to neural networks
3.5 Terms related to trustworthiness
3.6 Terms related to natural language processing
3.7 Terms related to computer vision
4 Abbreviated terms
5 AI concepts
5.1 General
5.2 From strong and weak AI to general and narrow AI
5.3 Agent
5.4 Knowledge
5.5 Cognition and cognitive computing
5.6 Semantic computing
5.7 Soft computing
5.8 Genetic algorithms
5.9 Symbolic and subsymbolic approaches for AI
5.10 Data
5.11 Machine learning concepts
5.11.1 Supervised machine learning
5.11.2 Unsupervised machine learning
5.11.3 Semi-supervised machine learning
5.11.4 Reinforcement learning
5.11.5 Transfer learning
5.11.6 Training data
5.11.7 Trained model
5.11.8 Validation and test data
5.11.9 Retraining
5.12 Examples of machine learning algorithms
5.12.1 Neural networks
5.12.2 Bayesian networks
5.12.3 Decision trees
5.12.4 Support vector machine
5.13 Autonomy, heteronomy and automation
5.14 Internet of things and cyber-physical systems
5.14.1 General
5.14.2 Internet of things
5.14.3 Cyber-physical systems
5.15 Trustworthiness
5.15.1 General
5.15.2 AI robustness
5.15.3 AI reliability
5.15.4 AI resilience
5.15.5 AI controllability
5.15.6 AI explainability
5.15.7 AI predictability
5.15.8 AI transparency
5.15.9 AI bias and fairness
5.16 AI verification and validation
5.17 Jurisdictional issues
5.18 Societal impact
5.19 AI stakeholder roles
5.19.1 General
5.19.2 AI provider
5.19.3 AI producer
5.19.4 AI customer
5.19.5 AI partner
5.19.6 AI subject
5.19.7 Relevant authorities
6 AI system life cycle
6.1 AI system life cycle model
6.2 AI system life cycle stages and processes
6.2.1 General
6.2.2 Inception
6.2.3 Design and development
6.2.4 Verification and Validation
6.2.5 Deployment
6.2.6 Operation and monitoring
6.2.7 Continuous validation
6.2.8 Re-evaluation
6.2.9 Retirement
7 AI system functional overview
7.1 General
7.2 Data and information
7.3 Knowledge and learning
7.4 From predictions to actions
7.4.1 General
7.4.2 Prediction
7.4.3 Decision
7.4.4 Action
8 AI ecosystem
8.1 General
8.2 AI systems
8.3 AI function
8.4 Machine learning
8.4.1 General
8.5 Engineering
8.5.1 General
8.5.2 Expert systems
8.5.3 Logic programming
8.6 Big data and data sources — cloud and edge computing
8.6.1 Big data and data sources
8.6.2 Cloud and edge computing
8.7 Resource pools
8.7.1 General
8.7.2 Application-specific integrated circuit
9 Fields of AI
9.1 Computer vision and image recognition
9.2 Natural language processing
9.2.1 General
9.2.2 Natural language processing components
9.3 Data mining
9.4 Planning
10 Applications of AI systems
10.1 General
10.2 Fraud detection
10.3 Automated vehicles
10.4 Predictive maintenance
Annex A (informative) Mapping of the AI system life cycle with the OECD’s definition of an AI system life cycle
Bibliography