Understanding Artificial Intelligence
Artificial Intelligence (AI) refers to the ability of a computer to perform tasks that typically require human intelligence. This includes understanding language, recognizing patterns, solving problems, learning from data, and making decisions. AI systems are built using algorithms—sets of rules or instructions that guide a computer’s behavior.
How AI Works
AI systems learn patterns from data (training), recognize those patterns in new situations (inference), and respond or act based on what they’ve learned. For example, a language model like ChatGPT was trained on billions of words and now uses that knowledge to generate new text in response to user prompts (questions)
At USC, AI is used most commonly with these tools:
- Zoom AI Companion
- Microsoft Copilot
- Google Gemini
- Google Notebook LM
Note: ChatGPT is not currently offered as an enterprise tool. Users of ChatGPT should remain mindful of USC’s data privacy and acceptable use policies, as well as responsible use guidelines, when using this technology. [See the USC Policies and Policy Governance website for details.]
AI Glossary
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Algorithm
A set of step-by-step instructions that a computer follows to perform a specific task or solve a problem. Algorithms are the building blocks of AI.
Artificial General Intelligence (AGI)
A theoretical type of AI that can understand, learn, and perform any intellectual task that a human being can do. Current AI systems are narrow, not general.
Artificial Narrow Intelligence (ANI)
AI that is designed to perform a specific task—like recognizing speech, identifying images, or generating text. Nearly all AI today falls into this category.
Artificial Superintelligence (ASI)
A hypothetical form of AI that surpasses human intelligence in all areas, including creativity, problem-solving, and social intelligence.
Chatbot
A computer program that simulates conversation with users, often powered by AI to understand and respond naturally to questions or commands.
Deep Learning (DL)
A subset of machine learning that uses neural networks with many layers (hence ‘deep’) to process data and make predictions or decisions.
Generative AI (GenAI)
AI that can create new content—such as text, images, music, or video—based on patterns it has learned from large datasets. Examples include ChatGPT and DALL·E.
GPT (Generative Pre-trained Transformer)
An acronym for the architecture behind ChatGPT. It’s a model trained on a vast amount of text data to generate human-like responses.
Large Language Model (LLM)
A type of generative AI trained on massive amounts of text to understand and generate human-like language. Examples include GPT models.
Machine Learning (ML)
A branch of AI where computers learn from data rather than being explicitly programmed. The system improves its performance as it processes more examples.
Model
A mathematical representation that helps an AI system make predictions or decisions. A model is ‘trained’ using data to recognize patterns or relationships.
Natural Language Processing (NLP)
A field of AI that enables computers to understand, interpret, and generate human language. Used in translation apps, chatbots, and voice assistants.
Neural Network (NN)
A computer system modeled after the human brain’s network of neurons. Neural networks process information in layers, helping AI recognize patterns in speech and images.
Prompt
The text or input given to an AI model to produce a response. Writing clear prompts is key to getting useful answers from generative AI systems.
Reinforcement Learning (RL)
A type of machine learning where an AI learns by trial and error—receiving rewards or penalties for the actions it takes.
Supervised Learning
A type of machine learning where the model is trained on labeled data (input paired with correct output) so it learns to predict the right answers.
Training Data
The information used to teach an AI model how to recognize patterns and make predictions.
Token
The smallest unit of data (a word, part of a word, or symbol) that an AI language model reads and processes to generate responses.
Transformer
A type of AI model architecture that allows for efficient processing of language and context—key to the success of modern LLMs like GPT.
Unsupervised Learning
Machine learning that uses unlabeled data. The AI finds hidden patterns or groupings on its own, without being told what the correct output is.
Suggestions & Edits
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