Artificial Intelligence I ISO/IEC 22989:2022 Training – Part 4 | Clause 5.5 to 5.10 Explained

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📘 ISO/IEC 22989:2022 Training – AI Concepts (Clause 5.5–5.10)

In this detailed session of our ISO/IEC 22989:2022 series, we explore advanced AI concepts covering Cognition, Semantic Computing, Soft Computing, Genetic Algorithms, Symbolic vs Subsymbolic AI, and Data Foundations within the ML lifecycle.

This part strengthens conceptual clarity for AI governance, system design, and standardization alignment.

🔹 Cognition & Cognitive Computing

Artificial Intelligence draws heavily from cognitive science. This section explains:

Knowledge acquisition and processing

Reasoning based on exclusive and shared experiences

Learning and perception mechanisms

Core cognitive functions:
Attention, memory, knowledge formation, judgment, evaluation, reasoning, computation, problem solving, decision making, and language comprehension & production

We also discuss AI subdisciplines that replicate or simulate cognitive behavior in computational systems.

🔹 Semantic Computing

Semantic computing focuses on:

Matching and representing meaning in information

Retrieving, managing, manipulating, and creating content

Reducing uncertainty through structured representation

Logical reasoning by combining prior knowledge with learned information

This is foundational for knowledge graphs, intelligent search, and contextual AI systems.

🔹 Soft Computing

Unlike rigid computational models, soft computing:

Tolerates imprecision, uncertainty, and partial truth

Uses techniques such as:

Fuzzy systems

Evolutionary algorithms

Swarm intelligence

Neural network systems

Soft computing enhances adaptability and robustness in real-world AI applications.

🔹 Genetic Algorithms

Inspired by biological evolution, genetic algorithms:

Simulate natural selection and mutation

Represent solutions as gene sets (often binary: 0s and 1s)

Evaluate performance through fitness calculation

Improve solutions via:

Ranking

Crossover of chromosomes

Mutation

Continue until defined termination criteria are met

Select the fittest chromosomes as optimal or near-optimal solutions

These are powerful for optimization problems in complex environments.

🔹 Symbolic vs Subsymbolic AI

We explore the two foundational AI paradigms:

✔ Symbolic AI

Explicit knowledge encoding

Logical models and formal representations

Clear syntax–semantics distinction

Rule-based reasoning

✔ Subsymbolic AI

Connectionist paradigm

Implicit knowledge encoding

Statistical and machine learning approaches

Deep neural networks

We also examine Hybrid AI, which integrates both paradigms for enhanced capability.

🔹 Data in AI & Machine Learning Lifecycle

Data is central to AI system development and testing. This session covers:

Structured and unstructured data forms

The ML lifecycle stages:

Data acquisition

Exploratory data analysis (EDA)

Data annotation

Filtering & normalization

De-identification

Quality checking

Data sampling

Understanding data governance and lifecycle integrity is critical for trustworthy AI systems aligned with ISO/IEC standards.

🎯 Who Should Watch?

AI professionals & researchers

Data scientists & ML engineers

AI governance & compliance leaders

ISO standard practitioners

Technology risk & assurance professionals

This training strengthens your conceptual foundation under ISO/IEC 22989:2022 AI terminology and concepts framework, enabling better alignment with emerging AI governance and assurance standards.

Stay tuned for the next part of the series. 🚀

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