AI and Superintelligence: A Plain-English Glossary

Artificial intelligence is already used in business, but superintelligence remains a debated, hypothetical concept. This glossary explains the terms in plain English and separates current capabilities from questions about future systems. Here, SI means superintelligence: a hypothetical AI capability that would substantially exceed the strongest human abilities across a broad range of cognitive tasks. It is not a synonym for today’s generative AI tools.

Core AI terms

• Artificial intelligence (AI): Software that performs tasks associated with human reasoning, perception, language or decision-making. AI is a broad category, not one product.
• Superintelligence (SI): A hypothetical form of AI that would outperform humans across most or all important cognitive tasks. There is no generally accepted proof that current AI systems meet this definition.
• Artificial general intelligence (AGI): A debated term for AI with broad, flexible competence across many kinds of tasks. Definitions vary; AGI does not necessarily mean superintelligence.
• Machine learning (ML): A branch of AI in which a system learns patterns from examples or data rather than following only hand-written rules.
• Generative AI: AI that creates new material—such as text, images, audio or code—in response to instructions and context.
• Large language model (LLM): A model trained on large amounts of text to understand and generate language. LLMs can help with writing, research and software tasks, but their output needs review.
• AI agent: A software system that can pursue a defined goal through multiple steps, often by using tools or calling other systems. The word “agent” is used broadly; ask what actions it can actually take.
• Prompt: An instruction or question given to an AI system. Good prompts provide the goal, context, limits and desired format.
• Retrieval-augmented generation (RAG): A method that lets an AI model retrieve information from an approved source—such as company documents—before generating an answer.
• Hallucination: A plausible-sounding but unsupported or incorrect AI response. Important claims should be checked against reliable sources or authoritative records.
• Fine-tuning: Further training a model on selected examples to adjust its behavior for a task or style. It is different from simply giving a model documents to search.
• Evaluation (eval): A repeatable way to measure whether an AI system produces accurate, safe and useful results for the intended task.

Integration and delivery terms

• Systems integration (SI): Connecting applications, databases, devices or services so information moves reliably between them and a business workflow works end to end.
• Application programming interface (API): A defined way for one software system to request information or actions from another.
• ETL / ELT: Methods for moving and preparing data. ETL transforms data before loading it into a target system; ELT loads it first and transforms it there.
• Orchestration: Coordinating a sequence of systems or tasks—for example, retrieving records, checking rules and routing an item for human approval.
• Human in the loop: A person reviews, approves or corrects an AI-assisted decision or output.
• Guardrails: Rules and technical controls that limit what a system may do, what data it may use and when it must hand work to a person.
• Model drift: A drop in performance when the data, user behavior or real-world conditions change after a model is deployed.
• AI alignment: The challenge of making an AI system’s behavior reliably reflect its intended goals, constraints and human values.
• AI safety: Methods for identifying and reducing risks from AI systems, including errors, misuse, unintended actions and loss of meaningful oversight.
• Interpretability: Techniques for understanding how an AI system reaches or produces an output.
• Recursive self-improvement: A hypothetical process in which an AI system contributes to improving its own capabilities. It is a research and forecasting concept, not a normal feature of current business AI tools.
• Data governance: The policies, roles and controls that establish who may use data, how it is protected, and how its quality and origin are managed.

Questions worth asking

When a vendor or project team uses a term, ask what it means in this particular project. What data will the system use? What decisions can it make? What will a person review? How will success and errors be measured? What happens when the system is unavailable?

Claims about AGI or superintelligence should be treated carefully: ask for a precise definition, reproducible evidence and an independent way to evaluate the claim. Strong performance on selected tasks does not by itself establish superintelligence.

A shared vocabulary helps teams make better decisions. It does not replace a clear mission, good data, security review or human accountability.

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