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The seven defined terms
ISO/IEC DIS 42102 defines seven terms of its own and imports the rest from ISO/IEC 22989:2022. Every one of the seven has a page here with the meaning in our own words, what it settles, the evidence to keep and a test to apply to your own documents.
The seven are the load-bearing words of the framework: algorithm, AI system capabilities, capability, conversational learning, emergent behaviour, matrix and hybrid AI. Capability is the unit on one axis, matrix is the shape of the whole, and hybrid AI plus conversational learning mark where one method label stops being enough.
These definitions are public and can be read before the full draft is available. They are also the part most likely to be used loosely in an organisation, so each page includes a test that catches the common misuse rather than only restating the meaning.
| Code | Term | Meaning in our words |
|---|---|---|
| 3.1 | algorithm | A finite, ordered set of instructions that accomplishes a task or solves a problem. The draft imports the idea from the older data processing vocabulary and adapts it to the AI context. |
| 3.2 | AI system capabilities | The collective term for what an AI system can do: the abilities and functionalities attributed to it. The entry is a synonym pointer, so the operative meaning sits in the entry for capability. |
| 3.3 | capability | The ability of a system to carry out a task. It is a property of the system, stated as what it can do, not as how it does it. |
| 3.4 | conversational learning | A hybrid method in which a system improves through interactive dialogue with users or other agents, taking in new knowledge during the conversation rather than only through a separate training cycle. |
| 3.5 | emergent behaviour | Observable behaviour that appears when one or more AI systems coordinate several capabilities of their components across more than one knowledge domain. The note names factual, conceptual, procedural and metacognitive domains. |
| 3.6 | matrix | A two-dimensional picture that sets the methods of AI systems against their capabilities. It is the shape of the description, not a score and not a maturity model. |
| 3.7 | hybrid AI | A method that combines mechanisms from different families: learning approaches, knowledge forms, neural designs, symbolic reasoning or statistical models. |
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