A

Algorithm
A step-by-step procedure or formula for solving a problem. In AI, algorithms process data to make predictions or decisions, forming the backbone of machine learning models.

Artificial General Intelligence (AGI)
A type of AI with the ability to understand, learn, and apply intelligence across a wide range of tasks, mimicking human cognitive abilities.

Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, especially computer systems. It includes learning, reasoning, and self-correction.

Automated Machine Learning (AutoML)
The process of automating the end-to-end process of applying machine learning to real-world problems, simplifying model development and deployment.

B

Backpropagation
A training algorithm for neural networks that adjusts weights by calculating the gradient of the loss function, enabling the network to learn from errors.

Bayesian Network
A probabilistic graphical model representing a set of variables and their conditional dependencies using a directed acyclic graph.

Big Data
Extremely large datasets that can be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

C

Chatbot
An AI program designed to simulate conversation with human users, typically over the internet, providing customer service or informational assistance.

Computer Vision
A field of AI that enables computers to interpret and make decisions based on visual data from the world, including images and videos.

Convolutional Neural Network (CNN)
A deep learning algorithm primarily used for image recognition and processing, which employs a grid-like topology to detect patterns in visual data.

D

Data Mining
The process of discovering patterns and knowledge from large amounts of data. Data mining techniques are used in various AI applications to extract useful information.

Deep Learning
A subset of machine learning involving neural networks with many layers, enabling the processing of complex patterns in data such as images, text, and sound.

Domain Adaptation
A technique in machine learning where a model trained in one domain is adapted to perform well in another domain, addressing the challenge of dataset variations.

E

Edge Computing
A distributed computing paradigm bringing computation and data storage closer to the location where it is needed, improving response times and saving bandwidth.

Expert System
A computer system that emulates the decision-making ability of a human expert, using rule-based systems to solve complex problems in a specific domain.

F

Feature Engineering
The process of using domain knowledge to select, modify, or create new features from raw data to improve the performance of machine learning models.

Fine-tuning

Fine-tuning is the process of taking a pre-trained machine learning model and making small adjustments to its parameters using a smaller, task-specific dataset. This technique is commonly used in transfer learning, where a model trained on a large dataset is adapted to perform well on a different but related task by fine-tuning with additional training data.

Example Usage: Fine-tuning a BERT model, originally trained on general language understanding tasks, to improve its performance on a specific task like sentiment analysis or question answering.

Fuzzy Logic
A form of logic used in AI that deals with reasoning that is approximate rather than fixed and exact, mimicking human decision-making.

G

Generative Adversarial Network (GAN)
A class of machine learning frameworks where two neural networks, a generator and a discriminator, contest with each other to generate realistic data samples.

Gradient Descent
An optimization algorithm used to minimize the loss function in machine learning models by iteratively adjusting parameters in the direction of the steepest descent.

H

Hyperparameter
Settings that are used to control the learning process of a machine learning algorithm, which are set before the training process begins.

Hypothesis Space
The set of all hypotheses that can be formulated in response to a learning problem, representing all possible solutions.

I

Image Recognition
The ability of AI to identify objects, people, actions, and text in images. It’s a key component of computer vision.

Instance-Based Learning
A family of learning algorithms that compare new problem instances with instances seen in training, often used in classification tasks.

J

Joint Probability Distribution
A statistical measure that calculates the probability of two events happening at the same time and within a particular context.

K

K-Means Clustering
A method of vector quantization used for cluster analysis in data mining. It partitions n observations into k clusters where each observation belongs to the cluster with the nearest mean.

Knowledge Representation
The way in which information and relationships about the world are structured to be utilized by AI systems for reasoning and decision-making.

L

Label Propagation
A semi-supervised learning algorithm that propagates labels through a network to assign labels to previously unlabelled data points.

Latent Variable
A variable that is not directly observed but is inferred from other variables that are observed, often used in statistical models.

Learning Rate
A hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated.

M

Machine Learning (ML)
A subset of AI that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience.

Model Training
The process of running a machine learning algorithm on data to learn the parameters needed to make accurate predictions.

N

Natural Language Processing (NLP)
A field of AI focused on the interaction between computers and humans through natural language, enabling machines to read, understand, and generate human language.

Neural Network
A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

O

Optimization
The process of making a system as effective or functional as possible. In AI, it often involves finding the best parameters for a model to improve its performance.

Overfitting
A modeling error that occurs when a machine learning model captures noise in the data instead of the underlying pattern, resulting in poor performance on new data.

P

Predictive Analytics
The use of statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Preprocessing
The process of transforming raw data into a format that is more suitable for modeling, which includes cleaning, normalizing, and feature extraction.

Q

Q-Learning
A model-free reinforcement learning algorithm that seeks to find the best action to take given the current state, by learning the value of state-action pairs.

Quantum Computing
An area of computing focused on developing computer technology based on the principles of quantum theory, which could revolutionize AI with vastly superior processing power.

R

 

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a hybrid model that combines retrieval-based and generation-based approaches to improve the performance of natural language processing tasks. In a RAG system, a retriever model first selects relevant documents from a large corpus, and then a generator model uses these documents to produce more accurate and contextually relevant responses.

Example Usage: RAG can be used in chatbots where it retrieves relevant information from a knowledge base and generates human-like responses based on that information.

Reinforcement Learning (RL)
A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.

Recurrent Neural Network (RNN)
A type of neural network where connections between nodes form a directed graph along a temporal sequence, enabling the use of sequential data.

S

Semi-Supervised Learning
A machine learning technique that uses a small amount of labeled data and a large amount of unlabeled data for training, combining aspects of supervised and unsupervised learning.

Supervised Learning
A type of machine learning where the model is trained on labeled data, learning to make predictions or decisions based on input-output pairs.

T

Transfer Learning
A machine learning technique where a model developed for a particular task is reused as the starting point for a model on a second task.

Turing Test
A test proposed by Alan Turing to determine whether a machine can exhibit human-like intelligence, where a human evaluator interacts with a machine and a human without knowing which is which.

U

Unsupervised Learning
A type of machine learning where the model is trained on unlabeled data and must find patterns and relationships in the data without explicit instructions.

Underfitting

Underfitting occurs when a machine learning model is too simplistic and fails to capture the underlying patterns in the data. It happens when the model is not complex enough to learn from the training data, leading to poor performance on both the training set and new, unseen data.

V

Validation Set
A subset of data used to provide an unbiased evaluation of a model fit on the training dataset, helping to tune model parameters and prevent overfitting.

Variational Autoencoder (VAE)
A type of artificial neural network used to learn efficient representations of data, particularly in generative modeling and unsupervised learning.

W

Weight
A parameter within a neural network that transforms input data within the network’s hidden layers. Adjusting weights helps the network learn from training data.

Word Embedding
A type of word representation that allows words with similar meaning to have a similar representation, often used in NLP tasks.

X

Explainable AI (XAI)
AI systems that provide human-understandable explanations for their decisions and actions, enhancing transparency and trust in AI applications.

Y

YOLO (You Only Look Once)
A real-time object detection system that detects objects in images or video with high accuracy and speed by applying a single neural network to the entire image.

Z

Zero-Shot Learning
A machine learning paradigm where a model is capable of recognizing objects it has never seen before, using knowledge transferred from related tasks.