# Artificial Intelligence (AI)

> YogoQ Core AI-readable term handoff. Preview, read-only, Reviewed/Verified only.

- Canonical URL: https://core.yogoq.com/en-US/core/artificial-intelligence
- Locale: en-US
- Content tier: db_backed
- Quality: reviewed
- Publication status: published_reviewed
- Schema version: core-reviewed-term-ai-handoff-v2
- Compatible with: core-reviewed-term-ai-handoff-v1
- Content hash: 06cba93fbf176ed4ccfb81a6f58b68a6f90668f97746d14b35e0c454366992be
- Trust policy: core-trust-policy-v1-2026-06-22

## Short Definition

Artificial intelligence (AI) concerns machine-based systems that infer from inputs how to generate outputs such as predictions, content, recommendations, or decisions. AI capability does not by itself establish accuracy…

## 一言でいうと

Artificial intelligence (AI) concerns machine-based systems that infer from inputs how to generate outputs such as predictions, content, recommendations, or decisions. AI capability does not by itself establish accuracy, safety, autonomy, or fitness for a use.

## 計算の考え方

AI is a technology category, so it has no universal formula. Evaluation must use task-specific performance plus risk and operational measures. Task performance | Accuracy, precision, recall, calibration, or domain-specific utility on representative data Impact | Severity and distribution of beneficial and harmful outcomes, including affected groups Operations | Latency, availability, drift, override rate, incidents, and cost under real use

- Task performance | Accuracy, precision, recall, calibration, or domain-specific utility on representative data
- Impact | Severity and distribution of beneficial and harmful outcomes, including affected groups
- Operations | Latency, availability, drift, override rate, incidents, and cost under real use

## 含めるもの / 含めないもの

Define the system, use context, affected people, and lifecycle rather than labeling a model alone. Include | Machine-learning and knowledge-based systems that infer outputs influencing physical or virtual environments Exclude | Every deterministic rules engine, ordinary database query, or fixed automation merely because it is complex State explicitly | Objective, inputs, model and version, outputs, human role, deployment context, affected parties, monitoring, and retirement

- Include | Machine-learning and knowledge-based systems that infer outputs influencing physical or virtual environments
- Exclude | Every deterministic rules engine, ordinary database query, or fixed automation merely because it is complex
- State explicitly | Objective, inputs, model and version, outputs, human role, deployment context, affected parties, monitoring, and retirement

## 意味

An AI system combines one or more models with data, software, interfaces, and operational processes to produce outputs for explicit or implicit objectives. Systems vary in autonomy and in whether they adapt after deployment; responsibility for selecting, deploying, and overseeing them remains with people and organizations.

## 役立つ場面

Clarifies whether AI adds enough value over simpler software or process change. Sets evidence and oversight proportional to the consequence of error. Assigns accountability across provider, deployer, operator, reviewer, and affected-user channels.

- Clarifies whether AI adds enough value over simpler software or process change.
- Sets evidence and oversight proportional to the consequence of error.
- Assigns accountability across provider, deployer, operator, reviewer, and affected-user channels.

## 使い方のポイント

- AI is broader than machine learning, while generative AI is a narrower family within AI.
- A model is a component; the deployed AI system includes data, interfaces, people, and operations.
- Benchmark performance does not guarantee fitness in a new context.
- Human oversight must be designed as a functioning control, not a label.
- After deployment, monitor context, impact, anomalies, overrides, and stop conditions as well as performance.

## 何が数字を動かすか

Results depend on problem framing, data, model design, evaluation coverage, system integration, human interaction, and post-deployment change. Representative data and tests determine which conditions are actually covered. Deployment context can make the same model acceptable in one use and unsafe in another. Human oversight needs authority, information, time, and an effective fallback. Monitoring detects drift, misuse, changing populations, and unexpected impact.

- Representative data and tests determine which conditions are actually covered.
- Deployment context can make the same model acceptable in one use and unsafe in another.
- Human oversight needs authority, information, time, and an effective fallback.
- Monitoring detects drift, misuse, changing populations, and unexpected impact.

## 判断するときの注意点

Do not move from a compelling demonstration to consequential use without contextual evaluation. Training data can encode gaps or biases that aggregate accuracy hides. Confident outputs may still be incorrect, unsafe, or unsupported. Automation can magnify harm through scale, feedback loops, and over-reliance. Monitoring and incident response must continue after launch.

- Training data can encode gaps or biases that aggregate accuracy hides.
- Confident outputs may still be incorrect, unsafe, or unsupported.
- Automation can magnify harm through scale, feedback loops, and over-reliance.
- Monitoring and incident response must continue after launch.

## よくある誤解 / 落とし穴

- AI means a system understands like a person. Output quality does not establish human-like comprehension.
- All AI learns continuously. Many deployed systems remain fixed until deliberately updated.
- Human approval automatically makes a system safe. Oversight fails without context, authority, and time.

## 最小例

A support team evaluates an AI system that routes incoming cases. It compares the system with rules-based routing on representative languages and issue types, measures recall for urgent cases, reviews error impact by customer group, and sets a confidence threshold for human triage. The rollout begins in one queue with logging, override, fallback, and incident procedures. Expansion depends on observed benefit and bounded harm, not demonstration quality alone.

## 似ている言葉との違い

AI system | Infers outputs for objectives with varying autonomy Machine learning | Techniques that learn patterns from data; a subset used in many AI systems Generative AI | AI that produces content such as text, images, audio, or code Traditional automation | Executes explicitly specified rules without the same inference behavior Model | Core inference component inside a broader operational system

- AI system | Infers outputs for objectives with varying autonomy
- Machine learning | Techniques that learn patterns from data; a subset used in many AI systems
- Generative AI | AI that produces content such as text, images, audio, or code
- Traditional automation | Executes explicitly specified rules without the same inference behavior
- Model | Core inference component inside a broader operational system

## 一緒に見る指標

Combine usefulness, reliability, impact, and governance evidence. Task utility | Whether outputs improve the intended decision or workflow Robustness and calibration | Behavior under variation and confidence quality Group and impact analysis | Who benefits, who bears errors, and how severely Override and incident rates | Whether human controls and escalation work Drift and cost | Sustainability under changing data and load

- Task utility | Whether outputs improve the intended decision or workflow
- Robustness and calibration | Behavior under variation and confidence quality
- Group and impact analysis | Who benefits, who bears errors, and how severely
- Override and incident rates | Whether human controls and escalation work
- Drift and cost | Sustainability under changing data and load

## Aliases

- Artificial Intelligence (AI) (display_name, en-US)
- AI (abbreviation)
- アーティフィシャル・インテリジェンス (katakana, en-US)
- 人工知能（AI） (localized_title, ja-JP)
- Artificial Intelligence (AI) (english_name, en-US)

## Relations

- Risk: related (https://core.yogoq.com/en-US/core/risk)

## RAG Chunks

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- core:chunk:artificial-intelligence:en-US:meaning:de33f6932dfa8ac1
- core:chunk:artificial-intelligence:en-US:usage:23c7e1f402e0abc2
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- core:chunk:artificial-intelligence:en-US:misunderstandings:79ca7686ea5be390
- core:chunk:artificial-intelligence:en-US:misunderstandings:fcab4c9a8bd880cd
- core:chunk:artificial-intelligence:en-US:examples:0c4fb2f9a1852a6f
- core:chunk:artificial-intelligence:en-US:comparisons:e54ccdbf016164a5
- core:chunk:artificial-intelligence:en-US:related_metrics:94c983ed49011d59
- core:chunk:artificial-intelligence:en-US:faq:cddae92f8bcff8c4
- core:chunk:artificial-intelligence:en-US:faq:253fd38a6dc7dee6
- core:chunk:artificial-intelligence:en-US:faq:6756c56c76a11fce

## FAQ

### Is machine learning the same as AI?

No. Machine learning is an important set of techniques used in many AI systems, while AI also includes knowledge-based and other approaches.

### Does AI always make autonomous decisions?

No. Systems vary from decision support to automated action, and their level of autonomy must be stated.

### When is a simpler rule preferable?

Use a simpler method when it meets the objective with lower error, cost, opacity, or governance burden.

## Sources

- NIST CSRC: artificial intelligence definition - https://csrc.nist.gov/glossary/term/artificial_intelligence
- NIST AI 100-3: trustworthy AI glossary - https://www.nist.gov/publications/language-trustworthy-ai-depth-glossary-terms
- NIST AI RMF 1.0 - https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10

## Limitations

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- Public pages support general understanding and practical context; they are not professional advice for individual cases.
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- Even when AI-assisted drafting or audit is used, publication relies on quality gates and human-readable evidence.