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Artificial Intelligence:
Key AI Terms Glossary

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Key AI Terms Glossary

The following terms are common in articles and resources written about AI. Some of these words may be used in other fields or contexts. Please note that all definitions in this glossary are written within the context of AI.

Autonomous AI

Artificial intelligence systems capable of operating and performing tasks without constant human control. These systems can analyze data, learn from it, and execute actions based on their programming and algorithms.


The presence of systematic and undesired preferences or imbalances in the output generated by an AI model. Bias can emerge in various forms, such as in the content, language, or perspectives generated by the AI system.


The abrupt shifts in quality, coherence, or relevance often observed in AI generated content, particularly in writing. It refers to the inconsistencies in style, tone, or factual accuracy that can occur within a short span. Identifying burstiness helps distinguish AI-generated content from human-created content.

Generative AI

AI systems that can generate new content, such as text, images, or music. It involves developing algorithms and models that can understand patterns in existing data and use that understanding to generate novel output.

Generative Model

An AI model designed to generate new data that resembles the patterns and characteristics of the training data it has been exposed to.


Misinformation or made-up information based on a pattern that the AI model has learned as part of its training. For example, the model could create references that do not actually exist.

Heat Map

A visual representation that highlights important elements in the output generated by an AI model. It helps understand where the model focuses and assists in evaluating and improving the generated content.

Large Language Models

Components of artificial intelligence developed based on the training of vast datasets of documents from various sources. The computer program analyzes data input and maps out words in the dataset. It next tries to predict which words are positioned before or after other words using predictive patterns of most likely combinations.


The generated content produced by a generative AI system. It can be text, images, audio, music, video, or other data the model is designed to produce.


A measure used to assess the coherence and consistency of AI-generated text. Higher perplexity values suggest the content is more likely to be AI-generated due to unusual patterns or inconsistencies. Content identification systems use perplexity to identify AI-generated content.

Positional Encoding

A technique that assigns a number to each word during training that is used to show the position (or order) of words in a sequence.


In generative AI, probabilistic means that the models incorporate probability, which is used to estimate the likelihood of different outcomes and generate outputs that align with the learned probabilities.


The initial input text or instructions given to a model to generate new content based on that starting point. It provides context and guides the model's output. The prompt can be a few words or sentences that set the tone or specify the desired content.


The capability to possess consciousness, self-awareness, and subjective experiences. Achieving true sentience in AI systems is a topic of scientific exploration and philosophical debate.


Discrete units used to represent meaningful components of text, such as words or phrases. Breaking down text into these units allows AI models to process and analyze language at a granular level, enabling tasks like language generation. 

Training Data

Training data refers to the set of examples or input data used to train a generative AI model. It consists of a collection of representative samples the model learns from to generate new content or make predictions.


A type of model (or robot) that can simultaneously work on several tasks and sequentially build output. The transformer gives AI models the ability to process and learn from data so they can interpret context and place words together to form a cohesive sentence structure