Artificial Intelligence Glossary 📚
A
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.
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.
