Join us for Pop Summit 2025: Digital Signage Strategies —
Register NowLost in the AI Lingo?
Here’s your cheat sheet to bots, buzzwords & brainy tech, made simple for internal comms pros.
Agentic AI – AI systems that can autonomously plan, decide, and act toward specific goals, using memory, reasoning, and tools—often with minimal human oversight.
Algorithm – A set of instructions or rules that a computer follows to solve a problem or perform a task.
Artificial Intelligence (AI) – Technology that enables machines to perform tasks that typically require human intelligence, such as learning, reasoning, and decision-making.
Automation – The use of technology to perform tasks without human intervention, often to increase speed, accuracy, and efficiency.
Bias in AI – When AI makes unfair or skewed decisions due to flaws in the data it was trained on.
Big Data – Extremely large datasets that are analyzed by AI to identify patterns, trends, or insights.
Chatbot – An AI program designed to mimic human conversation via text or voice.
Computer Vision – A field of AI that enables machines to interpret and understand visual information like photos and videos.
Deep Learning – A subset of machine learning that uses layered neural networks to model complex patterns in large amounts of data.
Data Labeling – The process of tagging data (like images or text) with relevant categories so AI can learn from it.
Edge AI – AI that processes data on local devices (like phones or sensors) instead of relying on cloud servers.
Ethical AI – The practice of designing and using AI systems in ways that are fair, transparent, accountable, and aligned with human values.
Explainable AI (XAI) – AI systems designed to make their decisions and behavior understandable to humans.
Fine-Tuning – Adjusting a pre-trained AI model using more specific data to improve its performance on a particular task.
Foundation Model – A large, general-purpose AI model (like a large language model) that can be adapted for many different tasks.
GenAI (Generative AI) – AI models that can create new content—text, images, music—based on patterns learned from existing data.
Generative AI – AI that produces original content by analyzing and replicating learned patterns.
Hallucination (in AI) – When an AI confidently produces information that is false, misleading, or made-up.
Human-in-the-Loop – An approach where humans are involved in training, monitoring, or correcting AI to ensure better outcomes.
Inference – The stage when an AI model uses what it has learned to make predictions or decisions.
Intelligent Agent – An autonomous entity that perceives its environment and takes actions to achieve specific goals.
Jupyter Notebook – A tool often used by data scientists to write and test Python code, especially for AI and ML tasks.
Knowledge Graph – A structured representation of information that helps AI understand relationships between concepts or entities.
Large Language Model (LLM) – A type of AI trained on vast text data to understand and generate human-like language (e.g., ChatGPT).
Labelled Data – Data that includes tags or categories used to train supervised AI models.
Machine Learning (ML) – A branch of AI where machines learn from data to improve their performance over time without being explicitly programmed.
Model – A trained algorithm that can analyze data and make predictions or decisions.
Natural Language Processing (NLP) – The ability of computers to understand, interpret, and generate human language.
Neural Network – A type of AI model inspired by the human brain, made up of layers of nodes (neurons) that process data.
Overfitting – When an AI model performs well on training data but fails to generalize to new, unseen data.
Prompt Engineering – Crafting inputs (prompts) in a way that helps an AI model produce better or more accurate responses.
Pre-training – The initial training phase where an AI model learns general patterns from large amounts of data.
Quantization – A technique that reduces the size of AI models to make them faster and more efficient, especially on mobile or edge devices.
Reinforcement Learning – A method where an AI learns through trial and error, receiving rewards or penalties based on its actions.
Retrieval-Augmented Generation (RAG) – A method where AI looks up external information (e.g., from documents) before generating a response.
Supervised Learning – A machine learning approach where models are trained on labeled examples.
Synthetic Data – Artificially generated data used to train or test AI models when real data is unavailable or sensitive.
Training Data – The dataset used to teach an AI model how to recognize patterns or make decisions.
Turing Test – A test of a machine's ability to exhibit behavior indistinguishable from a human.
Unsupervised Learning – A machine learning approach where models find patterns in data without labels or explicit instruction.
Underfitting – When a model is too simple to capture patterns in data, resulting in poor performance.
Vector Embedding – A way to represent words or concepts as numerical vectors so AI can compare and understand their meaning.
Weights – The numerical values in a neural network that determine how data is processed and influence model predictions.
Explainability (XAI) – How understandable an AI’s decisions are to humans. Critical in high-stakes applications like healthcare or finance.
YOLO (You Only Look Once) – A popular real-time object detection algorithm used in computer vision.
Zero-Shot Learning – When an AI performs a task without having seen any labeled examples during training, based on generalization from related data.