Skip to content

Concept Glossary: H

This page lists English glossary entries for this letter. Entry bodies are assembled from term source files.

hallucination

  • Meaning: Hallucination is an error in which a generative AI system produces unsupported or false content in a fluent and confident form. The key point is that hallucination is not limited to obviously strange output; a polished sentence can still be empty, outdated, or wrong when evidence is missing.
  • Why it matters: Hallucination is one of the central risks of generative AI because it separates fluency from correctness. Understanding it helps readers distinguish problems that can be reduced by better prompting from problems that require retrieval, source checking, review, or human confirmation.
  • Related concepts: confabulation, supporting evidence, human oversight, factuality, prompt
  • Core Section: P1-12.3
  • Appears in: P1-10.3

heuristic

  • Meaning: A heuristic is an experience-based rule that does not guarantee the best answer but helps search more promising candidates first. It is closer to a practical guide for limited time than to a proof that always returns the optimal result.
  • Why it matters: Many AI problems have too many paths or combinations to inspect exhaustively. Heuristics help readers distinguish a guaranteed solution method from a strategy that tries to find a useful answer quickly enough for the situation.
  • Related concepts: search, search space, computational limit
  • Core Section: P4-3.1
  • Appears in: P1-2.2, P1-7.2, P4-3.2, P4-4.1

hidden state

  • Meaning: Hidden state has two common meanings in this book. In recurrent neural networks, it is the internal state that carries information from earlier inputs into later computation. In computational-document execution, it can mean runtime values or settings that are not visible on the page but still affect later cells.
  • Why it matters: The same phrase can describe model memory or execution-environment state. Understanding the distinction helps readers explain why the same visible input may produce different results when unseen internal state has changed.
  • Related concepts: RNN, recurrent neural network, execution order, runtime state
  • Core Section: P1-11.2
  • Appears in: P2-10.3

human oversight

  • Meaning: Human oversight is a control structure that lets people review AI output, intervene, stop execution, reject results, or require changes when needed. It is stronger than a formal approval step that has no real power to alter the outcome.
  • Why it matters: In high-risk automation, responsibility requires meaningful intervention points. This concept helps readers distinguish the mere presence of a human from a design in which a human can actually inspect, stop, and correct the system.
  • Related concepts: safety, accountability, model validation, permission
  • Core Section: P1-15.1
  • Appears in: P1-10.3

hyperparameter

  • Meaning: A hyperparameter is a setting chosen before training that controls how a model is trained or shaped. Examples include update step size, number of layers, tree depth, and other values adjusted from outside the learned model parameters.
  • Why it matters: Hyperparameters are different from parameters learned from data. The distinction helps readers understand tuning, validation, and whether a performance change came from learned internal values or from the conditions under which training was run.
  • Related concepts: parameter, tuning, model validation
  • Core Section: P4-9.1
  • Appears in: P4-9.2, P4-9.3, P4-3.1, P4-3.2, P4-7.2, P4-8.1, P4-14.2, P4-16.2

hypothesis testing

  • Meaning: A statistical procedure for checking whether sample evidence is strong enough to reject a stated hypothesis under a chosen criterion.
  • Why it matters: It helps avoid turning noisy sample differences into overly strong conclusions.
  • Related concepts: Read together with the surrounding glossary entries and the linked Sections for this term.
  • Core Section: P2-5.5