AI Glossary
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A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
A
AI (Artificial Intelligence)
The simulation of human intelligence in machines to perform tasks like learning, problem-solving, and decision-making.
Algorithm
A step-by-step procedure or formula for solving a problem or performing a task.
Artificial Neural Network (ANN)
A computing model inspired by the human brain, used for recognizing patterns and processing data.
Agent
An autonomous entity that perceives its environment and takes actions to achieve goals.
Artificial General Intelligence (AGI)
AI capable of performing any intellectual task a human can, often referred to as "strong AI."
Artificial Narrow Intelligence (ANI)
AI designed for specific tasks, such as voice assistants or image recognition systems.
Artificial Superintelligence (ASI)
A hypothetical AI surpassing human intelligence in all domains.
AutoML (Automated Machine Learning)
A framework to simplify machine learning by automating data preprocessing, model selection, and tuning.
Autonomous Systems
Systems or machines that operate independently without human intervention.
Anomaly Detection
Identifying unusual patterns or outliers in data.
B
Backpropagation
An algorithm for training neural networks by adjusting weights based on error feedback.
Bayesian Network
A graphical model representing variables and their probabilistic dependencies.
Big Data
Extensive datasets analyzed computationally to uncover patterns and trends.
Binary Classification
A classification task with two possible outcomes, such as "yes" or "no."
Bias
A systematic error in AI models caused by imbalanced data or incorrect assumptions.
Bioinformatics
The application of AI in analyzing biological data like DNA sequences.
Black Box Model
A model whose internal processes are not easily interpretable.
Boosting
An ensemble technique combining weak models to create a stronger predictive model.
Bots
Automated software programs designed for repetitive tasks, such as chatbots.
C
Chatbot
An AI system designed to simulate human conversation.
Classifier
A model that categorizes data into predefined classes.
Clustering
An unsupervised learning technique for grouping similar data points.
Computer Vision
A field of AI that enables machines to interpret and analyze visual information.
Convolutional Neural Network (CNN)
A deep learning model used for tasks like image recognition.
Cognitive Computing
AI that mimics human thought processes, such as reasoning and learning.
Collaborative Filtering
A recommendation system technique predicting preferences based on similar users.
Cross-Validation
A method to evaluate machine learning models by splitting data into training and testing sets.
D
Data Mining
The process of discovering patterns, trends, and insights from large datasets using statistical and computational methods.
Data Augmentation
A technique used to increase the size of a dataset by generating new data through transformations like rotation, flipping, or scaling.
Data Imputation
The process of replacing missing data with substituted values to maintain dataset integrity.
Data Labeling
The task of assigning meaningful labels to data, often used in supervised learning to train models.
Data Preprocessing
The steps taken to clean, transform, and prepare raw data for analysis or modeling.
Data Drift
A phenomenon where the statistical properties of data change over time, potentially impacting model performance.
Data Normalization
The process of scaling data to fall within a specific range, often to improve model training.
Decision Tree
A machine learning model structured like a tree that splits data into branches based on feature conditions to make predictions.
Deep Learning
A subset of machine learning involving neural networks with multiple layers to model complex data relationships.
Dimensionality Reduction
Techniques used to reduce the number of features in a dataset while retaining its essential information.
Domain Adaptation
A transfer learning technique where models trained on one domain are adapted to perform well in another.
Dropout
A regularization method in neural networks where random nodes are ignored during training to prevent overfitting.
Dynamic Programming
An optimization technique that solves complex problems by breaking them into simpler subproblems.
Dataset
A structured collection of data, often used as input for training and testing AI models.
Deepfake
Synthetic media, such as images or videos, generated using AI techniques to mimic real people.
Distributed Computing
The use of multiple computers working together to process large-scale data or solve complex problems.
Domain-Specific AI
AI systems tailored to perform tasks within a specific field or industry.
E
Edge Computing
A computing paradigm where data processing happens closer to the source of data generation, reducing latency and bandwidth use.
Ensemble Learning
A machine learning technique that combines multiple models to improve predictive performance.
Ethics in AI
The study and application of moral principles to ensure AI systems are developed and used responsibly.
Epoch
One complete cycle through the entire training dataset during model training.
Evolutionary Algorithm
A class of optimization algorithms inspired by natural selection and genetics.
Expert System
An AI system designed to mimic human decision-making in a specific domain using predefined rules.
Exploratory Data Analysis (EDA)
An approach to analyze datasets and summarize their main characteristics, often visualized graphically.
Explainable AI (XAI)
AI systems designed to provide human-understandable explanations for their decisions and predictions.
Exponential Smoothing
A statistical technique used in time series forecasting to smooth out short-term fluctuations.
Entity Recognition
A natural language processing task that identifies entities like names, dates, and locations within text.
Embedded Systems
Computing systems integrated into devices to perform specific tasks, often powered by AI for enhanced functionality.
Expectation-Maximization (EM)
An iterative algorithm used to find maximum likelihood estimates in models with latent variables.
Edge Detection
A computer vision technique used to identify the boundaries of objects in an image.
Emotion Recognition
AI technology that detects and interprets human emotions from facial expressions, voice, or text.
Eager Learning
A type of learning in AI where models generalize and train on data before making predictions.
ElasticNet
A regularization technique that combines L1 and L2 penalties for linear regression.
G
Generative Adversarial Network (GAN)
A type of neural network with two components, a generator and a discriminator, that work together to create realistic data.
Gradient Descent
An optimization algorithm used to minimize the loss function by iteratively adjusting model parameters.
Graph Neural Network (GNN)
A neural network designed to process data structured as graphs, such as social networks or molecular structures.
Gaussian Mixture Model (GMM)
A probabilistic model that represents a dataset as a mixture of multiple Gaussian distributions.
Genetic Algorithm
An optimization technique inspired by the principles of natural selection and genetics.
Global Pooling
A method in convolutional neural networks to reduce the spatial dimensions of feature maps.
Ground Truth
The accurate and real-world data used to validate the predictions of AI models.
Grid Search
A hyperparameter tuning method that systematically evaluates different combinations of parameters.
Gradient Clipping
A technique used to prevent exploding gradients by capping the gradient values during training.
Greedy Algorithm
An approach that makes the locally optimal choice at each step, aiming for a global optimum.
Generalization
The ability of a machine learning model to perform well on unseen data.
Graph Embedding
A method to represent graph nodes, edges, or entire graphs as numerical vectors for machine learning tasks.
H
Heuristic
A problem-solving approach using practical methods or rules of thumb to find approximate solutions quickly.
Hyperparameter
A configuration variable set before training a machine learning model, such as learning rate or batch size.
Hyperparameter Tuning
The process of finding the optimal hyperparameters to improve model performance.
Hierarchical Clustering
A clustering method that builds a tree-like structure to group similar data points.
Hidden Layer
The layers in a neural network between the input and output layers where computations occur.
Human-in-the-Loop (HITL)
A hybrid AI approach where human feedback is integrated into the decision-making process.
Hinge Loss
A loss function used in classification models, particularly support vector machines.
Hashing
A method of mapping data to fixed-size values for faster retrieval or storage.
Hawkes Process
A statistical model for events occurring over time, often used in predicting sequences.
Hidden Markov Model (HMM)
A probabilistic model used to describe systems that transition between states with hidden variables.
Histogram of Oriented Gradients (HOG)
A feature descriptor used in computer vision for object detection tasks.
Head
In attention mechanisms like in transformers, a component that focuses on specific parts of input data.
Hierarchical Reinforcement Learning
An approach to reinforcement learning where tasks are divided into subtasks to simplify learning.
I
Image Recognition
The process of identifying and classifying objects, scenes, or patterns in images using AI algorithms.
Inference
The process of using a trained machine learning model to make predictions on new, unseen data.
Instance-Based Learning
A machine learning technique where models make predictions based on individual data instances rather than generalizing over the entire dataset.
Input Layer
The first layer in a neural network where the raw data is fed into the model.
Intelligent Agent
A system capable of perceiving its environment, reasoning, and acting autonomously to achieve specific goals.
Inductive Bias
The set of assumptions that a machine learning model makes to generalize from the training data to unseen data.
Instance
A single data point or observation in a dataset.
Image Segmentation
The process of dividing an image into regions or segments to make analysis or processing easier.
Interpretability
The ability to understand and explain how a machine learning model makes decisions.
Isolation Forest
An unsupervised learning algorithm used for anomaly detection by isolating outliers from normal data.
Inverse Reinforcement Learning (IRL)
A type of reinforcement learning where the agent learns from observing the behavior of others.
Input-Output Mapping
The relationship between input data and the output predictions made by a model.
J
Jaccard Index
A statistical measure used to quantify the similarity between two sets.
Jupyter Notebook
An open-source web application used to create and share documents that contain live code, equations, visualizations, and text.
Joint Distribution
A probability distribution that models the likelihood of two or more variables occurring together.
JSON (JavaScript Object Notation)
A lightweight data-interchange format often used for transferring data in AI applications.
Job Scheduling
The process of determining when and how machine learning tasks or computational jobs are executed.
JIT Compilation
A method of executing machine learning algorithms where code is compiled just before execution to improve performance.
K
K-Means Clustering
A clustering algorithm that partitions data into K distinct groups based on their features.
K-Nearest Neighbors (KNN)
A supervised machine learning algorithm that classifies data points based on the majority class of their nearest neighbors.
Kernel
A function used in support vector machines and other algorithms to map data into a higher-dimensional space to make it linearly separable.
Kurtosis
A statistical measure that describes the shape of a probability distribution’s tails relative to a normal distribution.
K-fold Cross-Validation
A technique used to assess the performance of a machine learning model by splitting the dataset into K subsets and training K times, each time using a different subset for testing.
Kalman Filter
An algorithm used to estimate the state of a dynamic system from noisy measurements, commonly used in time-series forecasting and robotics.
Knowledge Graph
A structure that stores interconnected descriptions of entities, their attributes, and the relationships between them.
K-means++
An enhanced version of the K-means clustering algorithm that improves the selection of initial centroids to speed up convergence.
L
Logistic Regression
A machine learning algorithm used for binary classification tasks, predicting the probability of an event occurring.
Learning Rate
A hyperparameter that controls the step size during the optimization of a machine learning model’s parameters.
Latent Variable
A variable that is not directly observed but is inferred from other variables in a model.
Linear Regression
A statistical method used to model the relationship between a dependent variable and one or more independent variables.
Loss Function
A function used to quantify the difference between the predicted and actual values in a machine learning model, guiding the optimization process.
Long Short-Term Memory (LSTM)
A type of recurrent neural network (RNN) designed to model sequential data with long-range dependencies, often used in natural language processing and time series forecasting.
Log-Likelihood
A statistical measure of the probability of observed data given a set of model parameters, often used in probabilistic models.
Latent Dirichlet Allocation (LDA)
A generative probabilistic model used for topic modeling in text analysis.
Label Propagation
A semi-supervised learning algorithm that spreads labels through a graph based on the connectivity of data points.
Local Minima
A point in the optimization process where the loss function is lower than in neighboring points but not necessarily the lowest overall.
Logistic Function
A mathematical function used in logistic regression that outputs a probability value between 0 and 1.
Linear Discriminant Analysis (LDA)
A statistical method used for dimensionality reduction and classification by finding the linear combination of features that best separates multiple classes.
M
Machine Learning (ML)
A field of AI focused on developing algorithms that enable computers to learn from and make predictions based on data.
Model
A mathematical representation of a process that uses data to learn and make predictions or decisions.
Mean Squared Error (MSE)
A commonly used loss function that measures the average squared difference between the predicted and actual values.
Multilayer Perceptron (MLP)
A type of neural network composed of multiple layers of neurons, commonly used for supervised learning tasks.
Monte Carlo Simulation
A statistical technique used to model and simulate the behavior of complex systems using random sampling.
Minimum Viable Product (MVP)
The initial version of a product that includes only the essential features needed to meet early user needs and gather feedback.
Moving Average
A statistical technique used to analyze time series data by smoothing out fluctuations and highlighting trends.
Mutual Information
A measure of the dependence between two variables, quantifying the amount of information shared by them.
Meta-Learning
A field of machine learning focused on algorithms that learn how to learn, improving their ability to generalize across different tasks.
Model Evaluation
The process of assessing the performance of a machine learning model using various metrics like accuracy, precision, recall, and F1-score.
N
Neural Network
A computational model inspired by the human brain, consisting of interconnected nodes (neurons) to process and learn from data.
Natural Language Processing (NLP)
A subfield of AI that focuses on enabling computers to understand, interpret, and generate human language.
Normalization
The process of adjusting the values of numerical data to a common scale, improving model performance and convergence.
Naive Bayes
A classification algorithm based on Bayes’ theorem, assuming that features are independent, often used for text classification.
Neuroevolution
The use of evolutionary algorithms to optimize neural network architectures and their parameters.
Non-Linear Activation Function
A mathematical function applied to the output of a neural network layer, allowing the model to learn complex patterns.
Nash Equilibrium
A concept from game theory where no player can improve their outcome by unilaterally changing their strategy, often used in reinforcement learning.
Nearest Neighbor Search
A process for finding the closest data points to a given query point, commonly used in clustering and classification tasks.
Noise
Unwanted or irrelevant data that can interfere with the performance of machine learning models.
O
Optimization
The process of adjusting the parameters of a machine learning model to minimize the loss function and improve performance.
Overfitting
A modeling error where a machine learning model becomes too complex and performs well on training data but poorly on new, unseen data.
Outlier
An observation in the data that deviates significantly from other observations, often requiring special treatment in analysis.
Objective Function
A mathematical function that the model tries to minimize or maximize during training, commonly the loss function.
Object Detection
A computer vision task that involves identifying and locating objects in an image or video.
Overfitting Prevention
Techniques like regularization, cross-validation, and pruning used to prevent overfitting in machine learning models.
Online Learning
A machine learning paradigm where the model is trained incrementally as new data arrives, rather than on a fixed dataset.
Ovation Algorithm
A heuristic optimization method for solving complex optimization problems by finding the best solution incrementally.
One-Hot Encoding
A method for converting categorical data into binary vectors, where each category is represented by a unique binary value.
Optimization Algorithm
A method used to find the optimal solution for a given problem, commonly used in machine learning for tuning model parameters.
P
Precision
A performance metric that measures the proportion of true positive predictions to the total predicted positives.
Predictive Modeling
A statistical technique used to create a model that predicts future outcomes based on historical data.
Principal Component Analysis (PCA)
A dimensionality reduction technique used to identify the most important features in a dataset by transforming it into a new set of variables.
Pooling
A technique used in convolutional neural networks to reduce the spatial size of the feature maps, improving efficiency and generalization.
Perceptron
A simple type of neural network model used for binary classification, consisting of a single layer of neurons.
P-value
A statistical measure that helps determine the significance of results in hypothesis testing.
Precision-Recall Curve
A graphical representation of a model’s performance in terms of precision and recall across different thresholds.
Phantom Data
Fictitious or synthetic data created for training or testing machine learning models when real data is unavailable.
Preprocessing
The process of cleaning and transforming raw data into a format suitable for training machine learning models.
Predictive Analytics
The use of statistical models and machine learning techniques to analyze historical data and predict future outcomes.
Policy
In reinforcement learning, the strategy that defines the actions an agent should take given the state of the environment.
Q
Quantization
The process of reducing the precision of numbers, often used in machine learning to reduce model size and improve inference speed.
Query
A request for information or data from a database or search engine, often used in recommendation systems and data retrieval.
Q-Learning
A type of reinforcement learning algorithm that learns the value of taking specific actions in specific states to maximize cumulative rewards.
Quality Assurance (QA)
The process of ensuring that a machine learning model or software system meets specified requirements and functions as expected.
Quantum Computing
An emerging field of computation that leverages quantum-mechanical phenomena to process information in fundamentally different ways from classical computing.
Quantile Regression
A type of regression analysis used to predict specific quantiles (e.g., median) of the conditional distribution of the response variable.
R
Reinforcement Learning (RL)
A type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.
Random Forest
An ensemble learning method that constructs multiple decision trees and merges them to improve the accuracy and robustness of predictions.
Root Mean Squared Error (RMSE)
A performance metric used to measure the difference between the predicted and actual values, penalizing larger errors more heavily.
Recurrent Neural Network (RNN)
A type of neural network used for sequential data, where connections between nodes form cycles to capture temporal dependencies.
Regularization
A technique used to prevent overfitting by adding a penalty term to the loss function, discouraging overly complex models.
R-Squared (R²)
A statistical measure that represents the proportion of variance in the dependent variable that is predictable from the independent variables.
ReLU (Rectified Linear Unit)
A widely used activation function in neural networks that outputs the input directly if it is positive and zero otherwise.
Resampling
A technique used in statistics and machine learning to create multiple datasets from the original data, used for model evaluation and improvement.
Recommendation System
A system that uses machine learning algorithms to suggest products, services, or content to users based on their preferences or behavior.
Regression
A type of supervised learning task where the goal is to predict a continuous output variable based on input features.
S
Supervised Learning
A type of machine learning where the model is trained on labeled data, learning to map input features to known output labels.
Support Vector Machine (SVM)
A supervised learning algorithm used for classification and regression tasks, which finds the optimal hyperplane that separates classes in feature space.
Stochastic Gradient Descent (SGD)
An optimization algorithm used to minimize the loss function by updating model parameters incrementally using one or a few data points at a time.
Softmax
An activation function often used in the final layer of a neural network for multi-class classification, which converts raw outputs into probabilities.
Sparsity
A term used to describe a model or dataset where most of the values are zero or insignificant.
Stratified Sampling
A sampling method used in statistics where the population is divided into subgroups (strata) and samples are taken from each stratum proportionally.
Shallow Learning
A type of machine learning that uses simpler models, often with fewer layers, as compared to deep learning models.
Semi-Supervised Learning
A type of machine learning that uses a small amount of labeled data along with a large amount of unlabeled data for training.
Support Vector
A data point that lies closest to the decision boundary in a support vector machine (SVM) model and is critical for defining the optimal hyperplane.
Sequence-to-Sequence (Seq2Seq)
A deep learning model architecture used for tasks like machine translation, where both the input and output are sequences.
Scaling
The process of adjusting the range or distribution of features in a dataset to improve the performance and convergence of machine learning algorithms.
Segmentation
A process in computer vision and image analysis where an image is divided into meaningful regions for further analysis.
Self-Organizing Map (SOM)
An unsupervised learning algorithm that maps high-dimensional data into lower-dimensional grids for data visualization and clustering.
Sampling
The process of selecting a subset of data from a larger dataset for training, validation, or testing purposes.
Survival Analysis
A branch of statistics used to analyze and predict the time until an event occurs, often used in healthcare and risk management.
Sentiment Analysis
A natural language processing (NLP) task that involves determining the sentiment expressed in text, such as positive, negative, or neutral.
Structured Data
Data that is organized in a fixed format, often in rows and columns, making it easier to process and analyze using traditional algorithms.
Stacking
An ensemble learning method where multiple models are trained and their predictions are combined using another model to improve overall accuracy.