Generative Artificial Intelligence (GenAI): What is Generative AI?

A

Agent
An AI system that can autonomously perform tasks or make decisions, often interacting with its environment or other systems.

AI (Artificial Intelligence)
The field of computer science focused on creating systems that can perform tasks typically requiring human intelligence, such as reasoning, learning, and problem-solving.

AI Model
A mathematical or computational structure trained on data to perform specific tasks like classification, prediction, or generation. Examples include GPT-4 and Stable Diffusion.

AI Tool
A software application that uses AI models to perform tasks such as summarizing text, generating images, or analyzing data. Examples include Chat GPT and Canva's Magic Design.

Anthropomorphism
The habit of assigning human-like qualities to AI. While AI systems can imitate human emotions or speech, they don’t possess feelings or consciousness. We might interact with various AI models as if they were colleagues or thought partners, but in reality, they serve as tools for learning and resource development.

Augmentation
The use of AI to enhance human capabilities or existing systems, such as AI-assisted writing or decision-making.

B

Bias
Bias in AI models refers to output errors caused by skewed training data. Such bias can cause models to produce inaccurate, offensive, or misleading predictions. Biased AI models arise when algorithms prioritize irrelevant or misleading data traits over meaningful patterns.

F

Fine-tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to specialize it for a particular task or domain.

G

Generative AI
A type of AI that creates new content—such as text, images, or music—based on patterns learned from training data.

Grounding
The process of ensuring that AI-generated content is based on factual, verifiable information, often by linking to trusted sources.

GPT (Generative Pre-trained Transformer)
A family of large language models developed by OpenAI, designed to generate coherent and contextually relevant text based on input prompts.

H

Hallucinations
When an AI model generates information that sounds plausible but is false or misleading. A common issue in generative AI systems.

L

LLMs (Large Language Models)
Advanced AI models trained on vast amounts of text data to understand and generate human-like language.

M

ML (Machine Learning)
A subset of AI that enables systems to learn from data and improve their performance over time without being explicitly programmed.

N

Neural Network
A series of algorithms that mimic the operations of a human brain to recognize relationships in data.

NLP (Natural Language Processing)
A field of AI focused on enabling machines to understand, interpret, and generate human language.

P

Prompt
The input or question given to an AI model to generate a response. Effective prompting is key to getting useful outputs.

R

RAG (Retrieval-Augmented Generation)
A technique that combines information retrieval with text generation, allowing AI to pull in external knowledge to improve accuracy and relevance.

T
Tokens
The smallest units of data processed by language models. In text-based AI, a token might be a word, part of a word, or even punctuation. Models like GPT break input into tokens to understand and generate language. For example, the sentence “AI is powerful” might be split into three tokens: “AI”, “is”, and “powerful”.

V
Vectors
Numerical representations of data used by AI models to understand and process information. In language models, words or tokens are converted into vectors—arrays of numbers—that capture meaning, context, and relationships. These vectors allow models to perform tasks like similarity comparison, classification, and generation.