Core AI concepts
The foundation layer of the AIGP vocabulary: what AI is, what it runs on, and its classic forms. Artificial intelligence is defined as machine-based systems that infer from inputs how to generate outputs.
This first cluster is the bedrock of the AIGP vocabulary: what AI is, what it runs on, and its classic forms. Artificial intelligence itself is defined as machine-based systems that, for given objectives, infer from inputs how to generate outputs - predictions, content, recommendations or decisions - that influence real or virtual environments.
- Capability spectrum: an Algorithm is a fixed recipe; Autonomy is how far a system acts without human intervention; Agentic AI sits at the high-autonomy end, chaining tools across end-to-end workflows.
- Hypothetical horizon: Artificial general intelligence (AGI) would match or exceed human cognition across any task - still hypothetical, unlike today's narrow AI.
- Heritage forms: the Expert system (knowledge base + if-then inference engine), the Turing test, and Robotics predate modern ML.
- Application fields: Natural language processing (NLP), Computer vision and the Chatbot are the classic AI application areas.
- Compute - the GPUs and processing power - is both a cost driver and a policy lever for regulating frontier models.
The exam reuses the AI definition almost verbatim: AI infers from inputs how to generate outputs (predictions, content, recommendations or decisions) that influence real or virtual environments. Note it says infer, not just compute.
Key terms - quick answers
What is “Artificial intelligence”?
What is “Artificial general intelligence (AGI)”?
What is “Algorithm”?
What is “Autonomy”?
Sources and study method
This independent lesson uses active recall, spaced retrieval and scenario practice. Read the full study method.