AI Glossary

AI Agent

An AI system that can independently perform tasks and make decisions to achieve specific goals, often interacting with other systems or the real world. Unlike simple AI that just responds to questions, an agent can plan actions, use tools, and work autonomously - like a digital assistant that can book appointments, send emails, and manage your calendar without constant supervision.

Algorithm

A set of step-by-step instructions that tells a computer how to solve a problem or complete a task. Think of it like a recipe that the computer follows to process information and produce results.

Artificial General Intelligence (AGI)

A theoretical type of AI that would have human-level intelligence across all domains, able to understand, learn, and apply knowledge as flexibly as humans do. Current AI systems are "narrow" - good at specific tasks but not generally intelligent like humans.

Artificial Intelligence (AI)

Computer systems designed to perform tasks that typically require human intelligence, such as recognizing speech, making decisions, or understanding images. Think of it as teaching computers to "think" and solve problems like humans do, though in very different ways.

Bias

When an AI system produces unfair or prejudiced results because the data it learned from contained human prejudices or wasn't representative of all groups. This can lead to discriminatory outcomes in hiring, lending, or other important decisions.

Chain of Thought

A method where AI systems break down complex problems into step-by-step reasoning, showing their "thinking process" before reaching a conclusion. It's like showing your work on a math problem - the AI explains each logical step it takes, which helps produce more accurate answers and allows humans to follow its reasoning.

Computer Vision

AI technology that enables computers to identify and understand visual information from images or videos. It's what allows your phone to recognize faces in photos or helps self-driving cars identify stop signs and pedestrians.

Deep Learning

A more advanced form of machine learning that uses artificial neural networks with multiple layers to process information. It mimics how the human brain processes information through interconnected neurons, allowing computers to recognize complex patterns in data like images, speech, or text.

Fine-tuning

The process of taking a pre-trained AI model and giving it additional specialized training for a specific task or domain. It's like taking a generally educated person and giving them specialized training to become an expert in a particular field.

Generative AI

AI systems that can create new content such as text, images, music, or code based on prompts or examples. Instead of just analyzing existing information, these systems can produce original creative works that didn't exist before.

Hallucination

When an AI system generates information that sounds plausible but is actually false or made up. This happens because AI systems predict what seems most likely to come next, rather than checking if information is factually correct.

Large Language Model (LLM)

A type of AI system trained on vast amounts of text data to understand and generate human-like language. These models can write essays, answer questions, and hold conversations by predicting what words should come next based on patterns learned from text.

Machine Learning

A method of teaching computers to learn patterns and make predictions from data without being explicitly programmed for every situation. It's like showing a child thousands of pictures of cats and dogs until they can recognize the difference on their own.

Model

The mathematical representation that an AI system creates after learning from data. Think of it as the "brain" of the AI that contains all the patterns and knowledge it has learned, which it uses to make predictions or decisions about new information.

Natural Language Processing (NLP)

The ability of computers to understand, interpret, and generate human language in a meaningful way. This technology powers chatbots, voice assistants, and translation services, allowing machines to communicate with us using everyday language.

Neural Network

A computer system inspired by how neurons work in the human brain. It consists of interconnected nodes that process and pass information to each other, allowing the system to learn from examples and make decisions based on patterns it discovers.

Overfitting

When an AI system becomes too specialized on its training examples and performs poorly on new, unseen data. It's like a student who memorizes specific practice problems but can't solve similar problems with slightly different numbers.

Predictive Analytics

The use of AI and statistical techniques to analyze historical data and make informed predictions about future events or trends. It's like being a weather forecaster for business - using past patterns in sales, customer behavior, or market conditions to predict what's likely to happen next, helping organizations make better decisions and prepare for the future.

Prompt

The input or instruction you give to an AI system to tell it what you want it to do. It's like asking a question or giving directions to the AI, such as "Write a poem about spring" or "Explain photosynthesis in simple terms."

Reinforcement Learning

A learning method where an AI system learns through trial and error by receiving rewards for good actions and penalties for bad ones. It's similar to how you might train a pet with treats, or how we learn to play games by discovering which moves lead to winning.

Supervised Learning

A type of machine learning where the AI system learns from examples that include both the input and the correct answer. It's like learning with a teacher who shows you problems and their solutions until you can solve similar problems on your own.

Training Data

The collection of examples used to teach an AI system how to perform a specific task. Just like students need textbooks and practice problems to learn, AI systems need large amounts of example data to understand patterns and make accurate predictions.

Unsupervised Learning

A type of machine learning where the AI system finds hidden patterns in data without being given the "right answers." It's like being given a box of mixed puzzle pieces and figuring out how they group together without seeing the final picture.

Weights

The numerical values within an AI model that determine how strongly different pieces of information influence the final output. Think of them as the "strength settings" on connections in the AI's brain - they get adjusted during training to help the model make better predictions, similar to how our brain strengthens certain neural pathways through learning and experience.