Artificial Intelligence Glossary 📑
A
Agent: A software program designed to perform specific, narrowly defined tasks on behalf of a user within predefined boundaries.
Agentic AI: Refers to AI systems that can autonomously pursue complex goals by planning, using tools, and making decisions across multiple steps without constant human guidance.
AGI (Artificial General Intelligence): A hypothetical AI capable of performing any intellectual task a human can. It remains a future goal of AI research.
AI (Artificial Intelligence): The field of computer science focused on creating machines that simulate human intelligence, such as learning, decision-making, and language understanding.
AI Alignment: The process of ensuring AI systems behave in accordance with human intentions and values.
AI Ethics: The study of ethical issues surrounding AI, including bias, transparency, and fairness.
AI Safety: The field concerned with ensuring AI systems do not pose risks to humans.
Algorithm: A step-by-step process or set of rules used for problem-solving and decision-making in AI and machine learning.
ANI (Artificial Narrow Intelligence): Also known as weak AI, it is designed for specific tasks, such as image recognition or language translation.
Anthropomorphism: The attribution of human traits, emotions, or intentions to AI systems.
Autonomous: Describes AI systems or robots that operate without human intervention, such as self-driving cars.
B
Backward Chaining: A reasoning method that starts with a goal and works backward to determine necessary conditions.
Bias: Unintended preferences or prejudices in AI systems, often due to imbalanced training data.
Big Data: Large and complex datasets that require AI and advanced analytics to process and extract insights.
Black Box (AI): A term for AI models, especially deep learning networks, whose internal decision-making process is difficult to interpret.
Bounding Box: A rectangular box used in computer vision to define the location of an object in an image.
C
Chatbot: An AI system designed to simulate conversation with users through text or voice.
Classification: A machine learning task that assigns data to predefined categories, such as spam detection in emails.
Clustering: An unsupervised learning technique that groups similar data points together.
Cognitive Computing: AI systems designed to mimic human thinking processes.
Computer Vision: A field of AI focused on enabling computers to interpret visual data, such as images and videos.
Convolutional Neural Networks: a specialized deep learning model, inspired by the visual cortex, excellent at recognizing patterns in grid-like data like images, audio, and video, by using layers of filters (kernels) to detect features (edges, shapes, textures) and progressively build complex representations, reducing data size with pooling, and then classifying through fully connected layers.
Corpus: A large collection of text used for training AI language models.
D
Data Mining: The process of discovering patterns and insights in large datasets using AI and statistical techniques.
Data Science: A field that combines AI, statistics, and domain expertise to analyze and interpret data.
Dataset: A structured collection of data used to train AI models.
Decision Tree: A model that splits data into branches based on decision rules, used for classification and regression tasks.
Deep Learning: A subset of machine learning that uses multi-layered neural networks to learn from large amounts of data.
E
Edge AI: AI processing performed on local devices rather than cloud servers to improve efficiency and privacy.
Emergent Behavior: Unexpected or unintended capabilities that arise as AI systems scale in complexity.
Expert System: An AI that applies predefined rules to make decisions in a specific domain.
Explainable AI (XAI): AI models designed to provide clear, human-understandable explanations for their decisions.
F
Feature: An individual attribute or measurable property used as input in machine learning models.
Feature Engineering: The process of transforming raw data into useful features for machine learning.
Federated Learning: A decentralized learning technique where models are trained across multiple devices without sharing raw data.
Forward Chaining: A reasoning approach that starts with known data and applies rules to derive conclusions.
G
GAN (Generative Adversarial Network): A type of AI model that generates realistic data by pitting two neural networks against each other.
Generative AI: AI systems that create new content, such as text, images, and music.
GPT (Generative Pre-trained Transformer): A family of AI language models capable of generating human-like text.
Gradient Descent: An optimization algorithm used to adjust model parameters to minimize error.
Guardrails (AI Safeguards): Constraints designed to prevent AI systems from producing harmful or unintended outputs.
H
Hallucination (AI): When an AI system generates false or misleading information while appearing confident.
Heuristic: A rule-of-thumb approach used to solve problems quickly when an optimal solution is impractical.
Hyperparameter: A setting in machine learning models that is manually tuned to improve performance.
I
Image Recognition: The ability of AI to identify and classify objects in images.
Intent (NLP): The purpose behind a user’s input in a chatbot or voice assistant.
K
Knowledge Graph: A structured representation of facts and their relationships, used to enhance AI’s ability to reason and retrieve information.
L
Label (Data Label): The correct output assigned to training examples in supervised learning.
Large Language Model (LLM): A powerful AI model trained on vast text data to generate human-like responses.
Linguistic Annotation: The process of marking text with grammatical or semantic information for AI training.
M
Machine Learning (ML): A subset of AI where algorithms learn patterns from data and make predictions or decisions.
Machine Translation: AI-driven translation of text or speech between languages.
Model: The result of training a machine learning algorithm, used to make predictions or classifications.
Multi-modal: refers to systems or models capable of processing, understanding, and generating information from multiple types of data modalities, such as text, images, audio, video, or other sensory inputs.
N
Natural Language Generation (NLG): AI technology that generates human-like text from structured data.
Natural Language Processing (NLP): The field of AI that enables machines to understand and process human language.
Neural Network: A computational model inspired by the human brain, used in deep learning.
O
Overfitting: A machine learning problem where a model learns noise in the training data instead of general patterns.
P
Parameter: A value learned by an AI model during training that affects its predictions.
Pattern Recognition: AI’s ability to detect patterns in data for classification or prediction.
Predictive Analytics: Using AI to forecast future outcomes based on past data.
Prompt (AI Prompt): An input given to AI models, like ChatGPT, to generate a response.
Q
Quantum Computing: A field of computing that uses quantum mechanics to process data in ways classical computers cannot.
R
Reactive AI: AI that responds to inputs without memory or learning from past experiences.
Regression: A type of machine learning used for predicting continuous values, such as stock prices.
Reinforcement Learning (RL): A machine learning approach where an agent learns by interacting with an environment and receiving rewards.
Robotics: The design and application of machines that can perform tasks autonomously, often using AI.
S
Sentiment Analysis: AI’s ability to determine the emotional tone of text, such as positive or negative sentiment.
Speech Recognition: AI’s ability to convert spoken words into text.
Supervised Learning: A type of machine learning where models learn from labeled training data.
T
Test Data: A dataset used to evaluate a trained model’s performance.
Token: A unit of text processed by AI, such as words or subwords.
Training Data: The dataset used to teach a machine learning model.
Transfer Learning: Reusing a pre-trained model on a new, related task to speed up learning.
Transformer (Model): A deep learning architecture that revolutionized NLP, used in models like GPT.
Turing Test: A test to determine if an AI can mimic human intelligence well enough to fool a human evaluator.
U
Unstructured Data: Data that lacks a predefined format, such as images, videos, or raw text.
Unsupervised Learning: A type of machine learning where AI discovers patterns in data without labels.
V
Validation Data: Data used to tune a machine learning model before final testing.
Variance (ML): A measure of how much a model’s predictions change based on different datasets.
W
Weak AI (Narrow AI): AI that is specialized for a specific task rather than general intelligence.
Z
Zero-Shot Learning: AI’s ability to make predictions for categories it has never seen before by relying on related knowledge.
