Standard
Track updates
AS ISO/IEC 22989:2023
[Current]Information technology — Artificial intelligence — Artificial intelligence concepts and terminology
AS ISO/IEC 22989:2023 identically adopts ISO/IEC 22989:2022, which establishes terminology for AI and describes concepts in the field of AI.
Published: 25/08/2023
Pages: 60
Table of contents
Cited references
Content history
Table of contents
Header
About this publication
Preface
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
5.11.9.1 General
5.11.9.2 Continuous learning
5.12 Examples of machine learning algorithms
5.12.1 Neural networks
5.12.1.1 General
5.12.1.2 Feed forward neural network
5.12.1.3 Recurrent neural network
5.12.1.3.1 General
5.12.1.3.2 Long short-term memory network
5.12.1.4 Convolutional neural network
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
5.19.2.1 General
5.19.2.2 AI platform provider
5.19.2.3 AI service or product provider
5.19.3 AI producer
5.19.3.1 General
5.19.3.2 AI developer
5.19.4 AI customer
5.19.4.1 General
5.19.4.2 AI users
5.19.5 AI partner
5.19.5.1 General
5.19.5.2 AI system integrator
5.19.5.3 Data provider
5.19.5.4 AI auditor
5.19.5.5 AI evaluator
5.19.6 AI subject
5.19.6.1 General
5.19.6.2 Data subject
5.19.6.3 Other subjects
5.19.7 Relevant authorities
5.19.7.1 General
5.19.7.2 Policy makers
5.19.7.3 Regulators
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
9.2.2.1 General
9.2.2.2 Machine translation
9.2.2.3 Speech synthesis
9.2.2.4 Speech recognition
9.2.2.5 Question answering
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
Bibliography
Cited references in this standard
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