Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction.
Bias
Systematic errors in AI outputs result from prejudiced training data or flawed algorithms, leading to unfair outcomes.
Chain-of-Thought Prompting
A technique in prompting language models to generate intermediate reasoning steps to improve problem-solving accuracy.
Context Window
The amount of text or data an AI model can consider at once when processing or generating language.
Deep Learning
A subset of machine learning where neural networks learn from data, enabling the modeling of complex patterns.
Diffusion Model
A type of generative model that learns to generate data by reversing a diffusion process, often used in image and audio generation.
Emergent Behavior
Unexpected capabilities or behaviors that arise in AI systems as they scale in complexity and data.
Foundation Model
Large-scale models trained on broad data that can be adapted to various downstream tasks, serving as a base for specialized applications.
Generative Adversarial Network (GAN)
A class of AI models consisting of two neural networks—the generator and the discriminator—that work together to produce realistic synthetic data.
Generative AI
A type of machine learning that generates content, such as text, images, music, and videos, by learning patterns from existing data.
Hallucination
In AI, this refers to instances where models generate information that appears plausible but is incorrect or nonsensical.
Large Language Model (LLM)
A type of AI model trained on vast amounts of text data to understand and generate human-like language.
Machine Learning (ML)
A field within AI where algorithms learn from data to make decisions or predictions without explicit programming.
Natural Language Processing (NLP)
A branch of AI that enables machines to understand, interpret, and generate human language.
Neural Networks
Computational models inspired by the human brain's interconnected neuron structure used in machine learning to recognize patterns and solve complex problems.
Reinforcement Learning
An area of machine learning where agents learn to make decisions by performing actions and receiving feedback in the form of rewards or penalties.
Synthetic Media
Content generated or manipulated by AI, including images, videos, and audio, often indistinguishable from real media.
Token
A unit of text (such as a word or subword) used in the context of language models and NLP tasks.
Transformer Model
A type of neural network architecture particularly effective for processing sequential data, such as language, and foundational to many state-of-the-art language models.
References
Ruiz, P., & Fusco, J. (n.d.). AI glossary. CIRCLS. Retrieved January 27, 2025, from https://circls.org/educatorcircls/ai-glossary
Coursera. (n.d.). Generative AI glossary for business leaders. Retrieved January 27, 2025, from https://www.coursera.org/resources/ai-terms
National Institute of Standards and Technology (NIST). (n.d.). Glossary of AI terms. AI Risk Management Framework Knowledge Base. Retrieved January 27, 2025, from https://airc.nist.gov/AI_RMF_Knowledge_Base/Glossary
Pasick, A. (2023, March 27). AI glossary. The New York Times. Retrieved January 27, 2025, from https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html
Khan, I. (2025, January 19). ChatGPT glossary: 49 AI terms everyone should know. CNET. Retrieved January 27, 2025, from https://www.cnet.com/tech/services-and-software/chatgpt-glossary-49-ai-terms-everyone-should-know