Module 1 - Resource: AI Glossary for Business Leaders

This glossary provides clear, non-technical definitions of key AI terms that business leaders should understand.

Algorithm: A set of rules or instructions given to an AI system to help it learn from data and make decisions.

Artificial Intelligence (AI): Computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

Bias: Systematic errors in AI systems that can lead to unfair outcomes for certain groups or individuals, often reflecting historical biases in training data.

Computer Vision: AI technology that enables computers to derive meaningful information from digital images, videos, and other visual inputs.

Data Drift: When the properties of the input data change over time, causing degradation in AI model performance.

Deep Learning: A subset of machine learning using neural networks with multiple layers (deep neural networks) to analyze various factors of data.

Explainable AI (XAI): AI systems designed to make their operations and outputs understandable to humans.

Feature: An individual measurable property or characteristic of a phenomenon being observed, used as input to an AI model.

Foundation Model: Large AI models trained on broad data that can be adapted for various downstream tasks (e.g., GPT, BERT).

Generative AI: AI systems that can create new content, such as text, images, audio, or video, based on patterns learned from existing data.

Machine Learning (ML): A subset of AI that enables a system to automatically learn and improve from experience without being explicitly programmed.

Natural Language Processing (NLP): Technology that helps computers understand, interpret, and generate human language.

Neural Network: Computing system inspired by biological neural networks, consisting of interconnected nodes (neurons) that process and transmit information.

Overfitting: When an AI model learns the training data too well, including its noise and outliers, limiting its ability to generalize to new data.

Precision: The proportion of positive identifications that were actually correct (a measure of model quality).

Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.

Recommendation System: AI systems that suggest relevant items to users based on various factors like past behavior, preferences, and contextual information.

Reinforcement Learning: A type of machine learning where an agent learns by interacting with its environment and receiving rewards or penalties.

Supervised Learning: Training an AI model on labeled data, where the correct outputs are provided during training.

Training Data: The dataset used to train an AI model, teaching it to make predictions or decisions.

Transfer Learning: A technique where a model developed for one task is reused as the starting point for a model on a second task, reducing training time and data requirements.

Unsupervised Learning: Training an AI model with unlabeled data to find patterns and relationships without predefined outputs.