Glossary of AI Monitoring Terms

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With artificial intelligence becoming an increasingly popular topic, its terminology can start to blur together. Some terms may sound overly technical, and others are being tossed around without much explanation. Yet many of these concepts are more approachable than they might initially seem. Understanding terms related to AI can help you to better understand the discourse around AI monitoring and governance.

Algorithm: A set of instructions that tells a computer how to solve a problem. Think of it like a recipe: If you follow the steps, you'll get a specific outcome. AI systems rely on algorithms to make predictions, recognize patterns, or generate responses.

Anomaly Detection: A monitoring technique that spots unusual or suspicious behavior, like sudden output changes, unexpected user interactions, or spikes in errors. It helps identify bugs, hacks, or degradation of a model's performance over time due to changes in the real-world data on which it was trained.

API (Application Programming Interface): A tool that lets different computer programs talk to each other. AI models often run through APIs, allowing apps, websites, or services to use them.

Artificial Intelligence (AI): A broad term for computer systems designed to perform tasks that typically require human intelligence, such as learning, planning, recognizing patterns, and making decisions. It doesn't mean the computer is "alive"; it simply follows complex mathematical and data processes.

Bias: Unfair or unbalanced behavior in an AI system, often caused by biased data. For example, if a model is trained mostly on one group of people, it may perform poorly for others. AI monitoring tools identify bias to help models remain fair and accurate.

Data Pipeline: A series of connected steps that collect, process, and move data where it needs to go. Reliable pipelines are crucial for developing accurate AI models and ensuring effective monitoring.

Deep Learning: A type of machine learning inspired by the human brain. It uses layered networks, called neural networks, to process vast amounts of data. This approach powers technologies such as voice assistants, image recognition, and large language models.

Explainability: A characteristic that helps humans understand why an AI model made a certain prediction. Without explainability, AI can feel like a "black box," a complex system or device whose internal workings can't be seen and understood.

Feedback Loop: When the output of an AI model influences the next set of data the model receives. Sometimes, this is helpful; other times, it can reinforce mistakes or bias, which is why AI monitoring is necessary.

Guardrails: Rules or safety controls built into an AI system to prevent risky behavior, such as producing harmful content, exposing personal information, or responding to dangerous prompts

Hallucination: When an AI system confidently gives an answer that is factually wrong or wholly made up. It's not being sneaky; it's predicting text the way it thinks humans would, even if the information isn't real.

Inference: The moment an AI model uses what it has learned to make a prediction or generate an answer. If training is like studying for a test, inference is taking the test.

Large Language Model (LLM): A type of AI model trained to understand and generate human-like text. LLMs learn from massive amounts of written data, which enables them to perform tasks such as answering questions, writing stories, and summarizing documents.

Latency: The amount of time it takes for an AI system to respond to a query. High latency means slow answers; low latency means fast answers. Monitoring keeps track of latency to ensure good performance.

Machine Learning (ML): A branch of AI in which computers learn from data instead of being explicitly programmed. The more data an AI model sees, the better it becomes at spotting patterns, similar to how people learn from experience.

Model Drift: A slow decrease in AI model accuracy over time. This often happens because the world changes but the model's training data doesn't. For example, a shopping recommendation model may become outdated as trends shift.

Monitoring: A continuous process that checks how an AI model performs in real-world conditions. Monitoring looks for unusual behavior, errors, hallucinations, security threats, and signs that accuracy is declining.

Neural Network: A collection of connected nodes that work together to analyze information. Each "neuron" performs a simple calculation, but millions of them together can recognize faces, translate languages, or detect spam.

Observability: The practice of watching an AI system's behavior to ensure that it stays reliable, safe, and accurate. Observability tools track data quality, model accuracy, latency, and risks, allowing teams to identify issues before they become bigger problems.

Prompt: The input you give an AI model, like a question or instruction. The quality of the prompt often determines the quality of the response.

Prompt Injection: A security issue in which someone intentionally manipulates an AI's instructions to make it act differently than intended. It's similar to tricking a person by giving them misleading directions, except the target is an AI system.

Scalability: The ability of an AI system to handle an increasing amount of work without breaking down or slowing dramatically.

Toxicity Detection: A type of monitoring that identifies harmful or inappropriate language in AI output. It helps prevent offensive or abusive responses from slipping through.

Training Data: The information used to teach an AI model. Good training data helps a model learn useful patterns; flawed or incomplete data can lead to mistakes.

Versioning: The practice of keeping track of different versions of an AI model as it gets updated. It helps teams understand what changed and quickly roll back the changes if a new version performs worse.

Glossary of AI Monitoring Terms

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