AI Definitions and Acronyms

AI Definitions and Acronyms commonly used across all the areas.

LLM (Large Language Model) — A type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content.

AI (Artificial Intelligence) — The ability of a machine or computer program to learn and think.

GenAI (Generative AI) — A type of artificial intelligence (AI) that can create new content.

Neural Network — A method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain.

Training — Teaches AI systems to interpret, perceive, and learn from data.

AI Model — A program that analyzes datasets to find patterns and make predictions.

GAN (Generative Adversarial Network) — GANs are generative models: they create new data instances that resemble your training data.

Transformers Model — A transformer model is a neural network that learns context and thus meaning by tracking relationships.

Machine Learning (ML) — A branch of artificial intelligence (AI) that allows computers to learn from data and improve without being explicitly programmed.

Computer Vision — A field of artificial intelligence (AI) that uses digital systems to interpret visual data.

Deep Learning — a machine learning technique that teaches computers to process data in a way that mimics the human brain.

Robotics — A branch of engineering and computer science that involves the design, construction, and operation of robots.

Priming — A strategy that introduces a new topic to students in a way that facilitates their academic learning because they know what they can expect.

Prompt Engineering — The process where you guide generative artificial intelligence (generative AI) solutions to generate desired outputs.

AI Agent — Autonomous entities that act upon an environment using sensors and actuators to achieve their goals.

Multimodal LLM — A new AI paradigm, in which various data types (image, text, speech, numerical data) are combined with multiple intelligence processing algorithms to achieve higher performances.

Model Weights — the learned traits that determine the strength of a connection (or signal) between any two of the neurons that make up the content of the network.

Machine Learning Pipeline — The end-to-end construct that orchestrates the flow of data into, and output from, a machine learning model (or set of multiple models).

API (Application Programmable Interface) — A set of defined rules that enable different applications to communicate with each other.

Inference — The process of running live data through a trained AI model to make a prediction or solve a task.

Artificial General Intelligence (AGI) — An artificial general intelligence is a hypothetical type of intelligent agent that can learn and work on general tasks that belong to human beings.

AI Tokens — Tokens are the basic units of text or code that an LLM AI uses to process and generate language.

Contextual AI — A type of artificial intelligence that interprets the context of a situation or query.