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BS ISO/IEC 22989:2022

BS ISO/IEC 22989:2022

$215.11

Information technology. Artificial intelligence. Artificial intelligence concepts and terminology

Published By Publication Date Number of Pages
BSI 2022 72
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PDF Catalog

PDF Pages PDF Title
2 National foreword
8 Foreword
9 Introduction
11 1 Scope
2 Normative references
3 Terms and definitions
3.1 Terms related to AI
16 3.2 Terms related to data
18 3.3 Terms related to machine learning
20 3.4 Terms related to neural networks
21 3.5 Terms related to trustworthiness
23 3.6 Terms related to natural language processing
26 3.7 Terms related to computer vision
4 Abbreviated terms
27 5 AI concepts
5.1 General
5.2 From strong and weak AI to general and narrow AI
5.3 Agent
28 5.4 Knowledge
29 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
30 5.10 Data
31 5.11 Machine learning concepts
5.11.1 Supervised machine learning
5.11.2 Unsupervised machine learning
32 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
33 5.11.9 Retraining
34 5.12 Examples of machine learning algorithms
5.12.1 Neural networks
35 5.12.2 Bayesian networks
5.12.3 Decision trees
5.12.4 Support vector machine
36 5.13 Autonomy, heteronomy and automation
37 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
38 5.15 Trustworthiness
5.15.1 General
5.15.2 AI robustness
39 5.15.3 AI reliability
5.15.4 AI resilience
5.15.5 AI controllability
5.15.6 AI explainability
40 5.15.7 AI predictability
5.15.8 AI transparency
5.15.9 AI bias and fairness
41 5.16 AI verification and validation
5.17 Jurisdictional issues
42 5.18 Societal impact
5.19 AI stakeholder roles
5.19.1 General
43 5.19.2 AI provider
5.19.3 AI producer
44 5.19.4 AI customer
5.19.5 AI partner
5.19.6 AI subject
45 5.19.7 Relevant authorities
6 AI system life cycle
6.1 AI system life cycle model
47 6.2 AI system life cycle stages and processes
6.2.1 General
6.2.2 Inception
48 6.2.3 Design and development
49 6.2.4 Verification and Validation
6.2.5 Deployment
6.2.6 Operation and monitoring
50 6.2.7 Continuous validation
6.2.8 Re-evaluation
6.2.9 Retirement
7 AI system functional overview
7.1 General
51 7.2 Data and information
7.3 Knowledge and learning
52 7.4 From predictions to actions
7.4.1 General
7.4.2 Prediction
53 7.4.3 Decision
7.4.4 Action
8 AI ecosystem
8.1 General
55 8.2 AI systems
8.3 AI function
8.4 Machine learning
8.4.1 General
56 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
58 8.6.2 Cloud and edge computing
60 8.7 Resource pools
8.7.1 General
8.7.2 Application-specific integrated circuit
61 9 Fields of AI
9.1 Computer vision and image recognition
9.2 Natural language processing
9.2.1 General
62 9.2.2 Natural language processing components
64 9.3 Data mining
9.4 Planning
10 Applications of AI systems
10.1 General
65 10.2 Fraud detection
10.3 Automated vehicles
66 10.4 Predictive maintenance
67 Annex A (informative) Mapping of the AI system life cycle with the OECD’s definition of an AI system life cycle
69 Bibliography

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BS ISO/IEC 22989:2022
$215.11