Understanding Artificial Intelligence Concepts and Terminology with ISO/IEC 22989:2022

Course details

As the use cases and implementation of AI tools continue growing, there are risks of misinterpreting intent and purpose across an organization. Having an aligned understanding of strategies and objectives related to AI first begins with establishing a common organizational taxonomy. In this course, instructor Lyron Andrews shows you how the standard language of ISO/IEC 22989 concepts and terminology can establish a common language for AI within your organization. Lyron also demonstrates how this document can be used to develop other standards and support communications among diverse interested parties or stakeholders.

Learning objectives
Define the various branches of AI and establish a common organizational taxonomy.
Compare and differentiate the key characteristics and applications of different AI branches.
Classify AI branches to enable proper selection and use case adoption within an organization.
Utilize the common taxonomy provided in ISO/IEC 22989 to align AI business goals and understanding.
Explain how a common AI taxonomy can support effective communication and collaboration among diverse stakeholders.

Concepts

0. Introduction

01 - The benefits of a common AI language

1. AI Concepts - Clauses 5-5.10

02 - Overview of AI concepts (Clause 5.1)
03 - Strong, weak, general, and narrow AI (Clause 5-5.2)
04 - AI agents (Clause 5.3)
05 - AI knowledge (Clause 5.4)
06 - Cognitive, semantic, and soft computing (Clause 5.5-5.7)
07 - Genetic symbolic and subsymbolic algorithms (Clause 5.8-5.9)
08 - Data (Clause 5.10)

2. Machine Learning Concepts - Clauses 5.11-5.12.4

09 - Supervised, unsupervised, and semi-supervised (Clause 5.11-5.11.3)
10 - Additional training processes (Clause 5.11.4-5.11.8)
11 - Retraining ML models (Clause 5.11.9)
12 - Machine learning algorithms, part one (Clause 5.12-5.12.2)
13 - Machine learning algorithms, part two (Clause 5.12.1.3-5.12.1.3.2)
14 - Machine learning algorithms, part three (Clause 5.12.1.4 5.12.2)
15 - Machine learning algorithms, part four (Clause 5.12.3 5.12.4)

3. AI Technology and Administrative Controls

16 - Autonomy, heteronomy, and automation (Clause 5.13)
17 - Internet of things and CPS (Clause 5.14)
18 - Trustworthiness, verification, and validation (Clause 5.15-5.16)
19 - Jurisdictional issues and societal impact (Clause 5.17-5.18)
20 - AI stakeholder roles (Clause 5.19-5.19.7)

4. AI System Lifecycle and Functional Overview - Clause 6-7.4

21 - AI system lifecycle overview (Clause 6.1)
22 - Lifecycle stages and processes (Clause 6.2)
23 - AI data, information, knowledge, and learning (Clause 7-7.3)
24 - AI predictions to actions (Clause 7.4-7.4.4)

5. AI Ecosystem - Clause 8-8.7

25 - AI ecosystem and function with ML (Clause 8-8.4)
26 - AI engineering (Clause 8.5-8.5.3)
27 - Big data (Clause 8.6-8.6.1)
28 - Cloud and edge computing (Clause 8.6.2)
29 - Resource pools and ASIC (Clause 8.7-8.7.2)

6. Fields and Application of AI - Clause 9-10.4

30 - Computer vision and image recognition (Clause 9.1)
31 - Natural language processing (Clause 9.2-9.2.1)
32 - Data mining and planning (Clause 9.3-9.4)
33 - Application of AI systems (Clause 10-10.4)

Conclusion

34 - Continue your AI journey

Understanding Artificial Intelligence Concepts and Terminology with ISO/IEC 22989:2022

Authors

Lyron Andrews