| 🏠 Back to Exam Syllabus | 📺 RooCloud on YouTube | 🌐 RooCloud Practice Exams |
Types of AI Explained: Reactive, Limited Memory, ANI, AGI, and ASI
This opening episode of the ISACA Advanced in AI Audit (AAIA) exam prep series unpacks the building blocks of automated intelligence — how the broad concepts of AI, machine learning and deep learning relate, what the functional types and capability levels actually mean, and the technological models you’ll meet in the field as an auditor.
What this episode covers
- How Artificial Intelligence, Machine Learning, and Deep Learning nest inside one another.
- The four functional types of AI — reactive machines, limited memory, theory of mind, and self-aware AI.
- The three capability levels — Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI).
- Generative models and the Generative Adversarial Network (GAN) architecture.
- Agentic AI — how it differs from standard automation, and the main areas where it shows up in the enterprise.
- Predictive models and where they appear in business decisions.
- The auditor’s lens for assessing privacy risk, asking the right vendor questions, and designing control frameworks for each model type.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the four functional types of AI?
The four functional types are reactive machines, limited memory, theory of mind, and self-aware AI. Reactive machines respond to inputs with no memory, limited-memory systems use recent observational data to complete a task, theory of mind understands human emotions and adapts to them, and self-aware AI possesses its own consciousness and does not yet exist.
What is the difference between ANI, AGI, and ASI?
Artificial Narrow Intelligence (ANI) is weak intelligence that operates only within a specific domain and covers all current AI. Artificial General Intelligence (AGI) could perform any intellectual task a human can and would need to master seven traits including sensory perception, fine motor skills, and creativity. Artificial Super Intelligence (ASI) is purely theoretical and would transcend human capability entirely.
How do AI, machine learning, and deep learning relate to each other?
They sit inside one another like nesting dolls. AI is the broadest category of systems that mimic human thinking, machine learning is a subset that learns patterns from historical data instead of fixed rules, and deep learning is an advanced form of ML that processes information in increasingly abstract layers.
What are GANs in generative AI?
A Generative Adversarial Network (GAN) uses two competing neural networks. A generator creates fake data while a discriminator tries to catch the fakes. They train against each other until the generator becomes so good the discriminator can no longer tell the difference, which is how highly realistic synthetic video and audio are produced.
Why do auditors need to understand the types of AI?
Classifying a system by its functional type and capability level lets an auditor judge what a tool can and cannot do, decide whether it poses a severe data privacy risk or is just a basic rule engine, ask the right questions during vendor assessments, and design appropriate control frameworks for the enterprise.
📚 Master the ISACA AAIA Exam!
Ready to test your knowledge? Access chapter-specific Multiple Choice Questions (MCQs) and full-length practice exams for the ISACA AAIA certification at RooCloud.com. Solve the chapter-wise questions to reinforce this lesson before moving to the next episode.
Reference: This article is based on concepts discussed in Types of AI Explained.