ISO/IEC TR 24372:2021
(Main)Information technology — Artificial intelligence (AI) — Overview of computational approaches for AI systems
Information technology — Artificial intelligence (AI) — Overview of computational approaches for AI systems
This document provides an overview of the state of the art of computational approaches for AI systems, by describing: a) main computational characteristics of AI systems; b) main algorithms and approaches used in AI systems, referencing use cases contained in ISO/IEC TR 24030.
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General Information
Standards Content (Sample)
TECHNICAL ISO/IEC TR
REPORT 24372
First edition
2021-12
Information technology — Artificial
intelligence (AI) — Overview of
computational approaches for AI
systems
Reference number
© ISO/IEC 2021
© ISO/IEC 2021
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© ISO/IEC 2021 – All rights reserved
Contents Page
Foreword .v
Introduction . vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Abbreviated terms . 2
5 General . 3
6 Main characteristics of AI systems . .5
6.1 General . 5
6.2 Typical characteristics of AI systems . 6
6.2.1 Adaptable . 6
6.2.2 Constructive . 6
6.2.3 Coordinated . 6
6.2.4 Dynamic . 6
6.2.5 Explainable . 6
6.2.6 Discriminative or generative . 6
6.2.7 Introspective . . 6
6.2.8 Trained or trainable. 7
6.2.9 Accommodating various data . 7
6.3 Computational characteristics of AI systems . 7
6.3.1 Data-based or knowledge-based . 7
6.3.2 Infrastructure-based. 7
6.3.3 Algorithm-dependent . 8
6.3.4 Multi-step or end-to-end learning-based . 9
7 Types of AI computational approaches . 9
7.1 General . 9
7.2 Knowledge-driven approaches . 10
7.3 Data-driven approaches . 10
8 Selected algorithms and approaches used in AI systems .11
8.1 General . 11
8.2 Knowledge engineering and representation . 11
8.2.1 General . 11
8.2.2 Ontology .12
8.2.3 Knowledge graph .12
8.2.4 Semantic web . . 14
8.3 Logic and reasoning . 14
8.3.1 General . 14
8.3.2 Inductive reasoning . 15
8.3.3 Deductive inference .15
8.3.4 Hypothetical reasoning . 16
8.3.5 Bayesian inference . 17
8.4 Machine learning . 18
8.4.1 General . 18
8.4.2 Decision tree . 18
8.4.3 Random forest . 19
8.4.4 Linear regression .20
8.4.5 Logistic regression . 21
8.4.6 K-nearest neighbour . 21
8.4.7 Naïve Bayes . 22
8.4.8 Feedforward neural network. 22
8.4.9 Recurrent neural network . 23
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© ISO/IEC 2021 – All rights reserved
8.4.10 Long short-term memory network. 24
8.4.11 Convolutional neural network . 25
8.4.12 Generative adversarial network . 26
8.4.13 Transfer learning . 27
8.4.14 Bidirectional encoder representations from transformers . 27
8.4.15 XLNet .28
8.5 Metaheuristics .29
8.5.1 General .29
8.5.2 Genetic algorithms .29
Bibliography .31
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© ISO/IEC 2021 – All rights reserved
Foreword
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accordance with the editorial rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives or
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This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 42, Artificial intelligence.
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