{"id":415390,"date":"2024-10-20T06:04:55","date_gmt":"2024-10-20T06:04:55","guid":{"rendered":"https:\/\/pdfstandards.shop\/product\/uncategorized\/bs-iso-iec-229892022\/"},"modified":"2024-10-26T11:18:31","modified_gmt":"2024-10-26T11:18:31","slug":"bs-iso-iec-229892022","status":"publish","type":"product","link":"https:\/\/pdfstandards.shop\/product\/publishers\/bsi\/bs-iso-iec-229892022\/","title":{"rendered":"BS ISO\/IEC 22989:2022"},"content":{"rendered":"
PDF Pages<\/th>\n | PDF Title<\/th>\n<\/tr>\n | ||||||
---|---|---|---|---|---|---|---|
2<\/td>\n | National foreword <\/td>\n<\/tr>\n | ||||||
8<\/td>\n | Foreword <\/td>\n<\/tr>\n | ||||||
9<\/td>\n | Introduction <\/td>\n<\/tr>\n | ||||||
11<\/td>\n | 1 Scope 2 Normative references 3 Terms and definitions 3.1 Terms related to AI <\/td>\n<\/tr>\n | ||||||
16<\/td>\n | 3.2 Terms related to data <\/td>\n<\/tr>\n | ||||||
18<\/td>\n | 3.3 Terms related to machine learning <\/td>\n<\/tr>\n | ||||||
20<\/td>\n | 3.4 Terms related to neural networks <\/td>\n<\/tr>\n | ||||||
21<\/td>\n | 3.5 Terms related to trustworthiness <\/td>\n<\/tr>\n | ||||||
23<\/td>\n | 3.6 Terms related to natural language processing <\/td>\n<\/tr>\n | ||||||
26<\/td>\n | 3.7 Terms related to computer vision 4 Abbreviated terms <\/td>\n<\/tr>\n | ||||||
27<\/td>\n | 5 AI concepts 5.1 General 5.2 From strong and weak AI to general and narrow AI 5.3 Agent <\/td>\n<\/tr>\n | ||||||
28<\/td>\n | 5.4 Knowledge <\/td>\n<\/tr>\n | ||||||
29<\/td>\n | 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 <\/td>\n<\/tr>\n | ||||||
30<\/td>\n | 5.10 Data <\/td>\n<\/tr>\n | ||||||
31<\/td>\n | 5.11 Machine learning concepts 5.11.1 Supervised machine learning 5.11.2 Unsupervised machine learning <\/td>\n<\/tr>\n | ||||||
32<\/td>\n | 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 <\/td>\n<\/tr>\n | ||||||
33<\/td>\n | 5.11.9 Retraining <\/td>\n<\/tr>\n | ||||||
34<\/td>\n | 5.12 Examples of machine learning algorithms 5.12.1 Neural networks <\/td>\n<\/tr>\n | ||||||
35<\/td>\n | 5.12.2 Bayesian networks 5.12.3 Decision trees 5.12.4 Support vector machine <\/td>\n<\/tr>\n | ||||||
36<\/td>\n | 5.13 Autonomy, heteronomy and automation <\/td>\n<\/tr>\n | ||||||
37<\/td>\n | 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 <\/td>\n<\/tr>\n | ||||||
38<\/td>\n | 5.15 Trustworthiness 5.15.1 General 5.15.2 AI robustness <\/td>\n<\/tr>\n | ||||||
39<\/td>\n | 5.15.3 AI reliability 5.15.4 AI resilience 5.15.5 AI controllability 5.15.6 AI explainability <\/td>\n<\/tr>\n | ||||||
40<\/td>\n | 5.15.7 AI predictability 5.15.8 AI transparency 5.15.9 AI bias and fairness <\/td>\n<\/tr>\n | ||||||
41<\/td>\n | 5.16 AI verification and validation 5.17 Jurisdictional issues <\/td>\n<\/tr>\n | ||||||
42<\/td>\n | 5.18 Societal impact 5.19 AI stakeholder roles 5.19.1 General <\/td>\n<\/tr>\n | ||||||
43<\/td>\n | 5.19.2 AI provider 5.19.3 AI producer <\/td>\n<\/tr>\n | ||||||
44<\/td>\n | 5.19.4 AI customer 5.19.5 AI partner 5.19.6 AI subject <\/td>\n<\/tr>\n | ||||||
45<\/td>\n | 5.19.7 Relevant authorities 6 AI system life cycle 6.1 AI system life cycle model <\/td>\n<\/tr>\n | ||||||
47<\/td>\n | 6.2 AI system life cycle stages and processes 6.2.1 General 6.2.2 Inception <\/td>\n<\/tr>\n | ||||||
48<\/td>\n | 6.2.3 Design and development <\/td>\n<\/tr>\n | ||||||
49<\/td>\n | 6.2.4 Verification and Validation 6.2.5 Deployment 6.2.6 Operation and monitoring <\/td>\n<\/tr>\n | ||||||
50<\/td>\n | 6.2.7 Continuous validation 6.2.8 Re-evaluation 6.2.9 Retirement 7 AI system functional overview 7.1 General <\/td>\n<\/tr>\n | ||||||
51<\/td>\n | 7.2 Data and information 7.3 Knowledge and learning <\/td>\n<\/tr>\n | ||||||
52<\/td>\n | 7.4 From predictions to actions 7.4.1 General 7.4.2 Prediction <\/td>\n<\/tr>\n | ||||||
53<\/td>\n | 7.4.3 Decision 7.4.4 Action 8 AI ecosystem 8.1 General <\/td>\n<\/tr>\n | ||||||
55<\/td>\n | 8.2 AI systems 8.3 AI function 8.4 Machine learning 8.4.1 General <\/td>\n<\/tr>\n | ||||||
56<\/td>\n | 8.5 Engineering 8.5.1 General 8.5.2 Expert systems 8.5.3 Logic programming 8.6 Big data and data sources \u2014 cloud and edge computing 8.6.1 Big data and data sources <\/td>\n<\/tr>\n | ||||||
58<\/td>\n | 8.6.2 Cloud and edge computing <\/td>\n<\/tr>\n | ||||||
60<\/td>\n | 8.7 Resource pools 8.7.1 General 8.7.2 Application-specific integrated circuit <\/td>\n<\/tr>\n | ||||||
61<\/td>\n | 9 Fields of AI 9.1 Computer vision and image recognition 9.2 Natural language processing 9.2.1 General <\/td>\n<\/tr>\n | ||||||
62<\/td>\n | 9.2.2 Natural language processing components <\/td>\n<\/tr>\n | ||||||
64<\/td>\n | 9.3 Data mining 9.4 Planning 10 Applications of AI systems 10.1 General <\/td>\n<\/tr>\n | ||||||
65<\/td>\n | 10.2 Fraud detection 10.3 Automated vehicles <\/td>\n<\/tr>\n | ||||||
66<\/td>\n | 10.4 Predictive maintenance <\/td>\n<\/tr>\n | ||||||
67<\/td>\n | Annex A (informative) Mapping of the AI system life cycle with the OECD\u2019s definition of an AI system life cycle <\/td>\n<\/tr>\n | ||||||
69<\/td>\n | Bibliography <\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":" Information technology. Artificial intelligence. Artificial intelligence concepts and terminology<\/b><\/p>\n |