AI Glossary
Every concept you need to navigate the world of Artificial Intelligence — from core fundamentals to cutting-edge research. Clear definitions, real-world context, expert-level depth.
Fundamentals
4 termsArtificial Intelligence (AI)
The capability of computer systems to perform tasks typically associated with human intelligence — including learning, reasoning, perception…
Narrow AI (Weak AI)
The only form of AI that currently exists. Systems designed and trained for a specific, limited task — such as playing chess, recognizing fa…
Artificial General Intelligence (AGI)
A theoretical form of AI with human-level cognitive flexibility — capable of understanding, learning, and solving any intellectual task that…
Artificial Superintelligence (ASI)
A hypothetical level of intelligence that would surpass human cognitive capabilities in virtually all domains — from scientific creativity t…
Machine Learning
10 termsMachine Learning (ML)
A subfield of AI that develops algorithms allowing machines to learn patterns from data and make predictions or decisions — without being ex…
Data Science
An interdisciplinary field combining statistics, programming, and domain expertise to extract knowledge and actionable insights from structu…
Supervised Learning
A Machine Learning paradigm where a model is trained on a labeled dataset — examples with known correct answers — so it can learn to make pr…
Unsupervised Learning
A Machine Learning paradigm that works with unlabeled data, discovering hidden patterns, structures, or groupings on its own — without prede…
Reinforcement Learning (RL)
A Machine Learning paradigm where an agent learns to make decisions by interacting with an environment, receiving rewards for good actions a…
Semi-supervised Learning
A learning paradigm that combines a small amount of labeled data with a large amount of unlabeled data during training, achieving better per…
Overfitting
A modeling failure where a machine learning model learns the training data too closely — memorizing noise and edge cases — and subsequently …
Cross-Validation
A model evaluation technique that divides data into multiple subsets, repeatedly training on some and testing on the remainder, to obtain a …
Feature Engineering
The process of selecting, transforming, or creating input variables (features) from raw data to make machine learning algorithms work more e…
Hyperparameter Tuning
The process of systematically optimizing the configuration settings of a machine learning algorithm — settings set before training, not lear…
Deep Learning
11 termsDeep Learning (DL)
A subfield of Machine Learning that uses artificial neural networks with many layers to learn extremely complex patterns directly from raw d…
Artificial Neural Network (ANN)
A computational model inspired by the human brain, composed of interconnected layers of nodes (neurons) that process information and learn c…
Activation Function
A mathematical function applied to each neuron's output that introduces non-linearity, enabling neural networks to learn complex, non-linear…
Backpropagation
The foundational algorithm for training neural networks — it efficiently computes the gradient of the loss function with respect to every we…
Gradient Descent
An iterative optimization algorithm that minimizes a loss function by updating model parameters in small steps in the direction opposite to …
Convolutional Neural Network (CNN)
A neural network architecture specialized for grid-structured data (especially images) that uses learned filters to detect local features — …
Recurrent Neural Network (RNN)
A neural network architecture designed for sequential data that maintains a hidden state — a form of memory — allowing it to incorporate con…
LSTM / GRU
Advanced Recurrent Neural Network variants that use gating mechanisms to selectively retain or forget information — overcoming the vanishing…
Transformer
A neural network architecture that processes entire sequences in parallel using self-attention mechanisms — eliminating recurrence and enabl…
Attention Mechanism
A technique that allows neural networks to dynamically focus on the most relevant parts of an input when producing each element of the outpu…
Dropout
A regularization technique that randomly deactivates a fraction of neurons during each training step, forcing the network to learn more robu…
Generative AI
9 termsGenerative AI
A branch of AI focused on models that generate new, original content — text, images, audio, code, video — that is statistically similar to t…
Large Language Model (LLM)
A Transformer-based deep learning model trained on massive text corpora — capable of understanding, generating, translating, summarizing, an…
Generative Adversarial Network (GAN)
A generative model architecture composed of two competing neural networks — a generator that creates synthetic data and a discriminator that…
Variational Autoencoder (VAE)
A generative model that learns to encode data into a structured, continuous latent space and decode it back — enabling generation of new, si…
ChatGPT
A conversational AI assistant developed by OpenAI, built on the GPT family of large language models and aligned with human preferences throu…
GPT (Generative Pre-trained Transformer)
A family of large language models developed by OpenAI using the Transformer decoder architecture, pre-trained on massive text datasets to pr…
BERT
Bidirectional Encoder Representations from Transformers — Google's landmark language model that reads text bidirectionally, capturing richer…
Fine-tuning
The process of adapting a large pre-trained model to a specific task or domain by continuing its training on a smaller, task-specific datase…
Prompt Engineering
The practice of designing, refining, and structuring inputs (prompts) given to AI language models to elicit the most accurate, relevant, and…
Applications
6 termsComputer Vision
A field of AI that enables machines to interpret and understand visual information from images and video — detecting objects, recognizing fa…
Natural Language Processing (NLP)
A field of AI dedicated to enabling computers to understand, interpret, generate, and reason about human language — in text and speech form …
Object Detection
A computer vision task that identifies and locates multiple objects within an image or video, typically outputting both a class label and a …
Semantic Segmentation
A computer vision task that classifies every pixel of an image into a semantic category, producing a dense map that labels each pixel as bel…
Sentiment Analysis
An NLP technique that automatically determines the emotional tone or opinion expressed in text — typically classifying it as positive, negat…
Named Entity Recognition (NER)
An NLP task that identifies and classifies named entities in text — such as people, organizations, locations, dates, and monetary values — e…
Technical Concepts
8 termsAlgorithm
A finite, well-defined sequence of instructions or rules that takes an input, performs a series of computational steps, and produces an outp…
Training Data
The dataset used to train a machine learning model — the examples from which the model learns the statistical patterns, relationships, and r…
Test Data
A held-out dataset used exclusively to evaluate a trained model's performance on unseen examples — providing an unbiased estimate of how the…
Loss Function
A mathematical function that quantifies the difference between a model's predictions and the true values — the signal that guides the learni…
Epoch
One complete pass through the entire training dataset during model training — a unit of training progress used to track how many times every…
Batch Size
The number of training examples processed together in a single forward and backward pass during model training — a hyperparameter that balan…
Learning Rate
A hyperparameter that controls the size of the weight updates during gradient descent — determining how quickly or slowly a model learns fro…
Inference
The process of using a trained machine learning model to make predictions or generate outputs on new, unseen data — the production phase tha…
Ethics & Society
7 termsAI Ethics
The field that establishes principles and frameworks to guide the development and deployment of AI systems in ways that are fair, transparen…
Algorithmic Bias
The tendency of AI systems to produce systematically unfair or discriminatory outcomes for certain groups — arising from biased training dat…
Fairness
An AI ethics principle and active research area focused on ensuring AI systems produce equitable outcomes across demographic groups — a goal…
Explainable AI (XAI)
A set of methods and principles aimed at making AI model decisions interpretable and transparent to humans — enabling auditing, debugging, r…
AI Alignment Problem
The fundamental challenge of ensuring that advanced AI systems pursue goals and exhibit behaviors that are genuinely aligned with human valu…
AI Safety
An interdisciplinary research field focused on ensuring that AI systems are reliable, controllable, and beneficial — addressing both near-te…
Technological Singularity
A hypothetical future point at which technological progress — particularly AI-driven recursive self-improvement — becomes so rapid and trans…
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