Activation Function

A function applied to the output of each neuron to introduce non-linearity into the network, enabling complex pattern recognition.

Adam Optimizer

An optimization algorithm that adjusts learning rate dynamically, combining advantages of AdaGrad and RMSProp for efficient model training.

Algorithm

A set of rules or instructions that can be used to solve problems, ranging from simple calculations to complex machine learning models.

Artificial General Intelligence (AGI)

A theoretical form of AI that matches or surpasses human cognitive capabilities across a wide range of tasks and domains.

Artificial Intelligence (AI)

Intelligence demonstrated by machines, enabling computers to perceive, learn, reason, and make decisions to perform tasks typically requiring human intelligence.

Artificial Neural Network (ANN)

A computational model inspired by biological neural networks, consisting of interconnected nodes organized into layers for processing information.

Attention Mechanism

A mechanism that allows models to focus on the most relevant parts of input data, dynamically weighting importance of different elements.

Autonomous Agents

Software systems capable of executing tasks autonomously within complex environments, making decisions to achieve desired outcomes.

Backpropagation

An optimization algorithm used to update neural network weights by propagating error gradients backward through the network.

Batch Normalization

A technique to improve training speed and stability by normalizing inputs to each layer in a neural network.

BERT

Bidirectional Encoder Representations from Transformers, a model that analyzes complete text sequences for understanding context.

Bias (AI)

Systematic errors in AI systems that lead to unfair or discriminatory outcomes, often reflecting prejudices in training data.

Big Data

Datasets characterized by large volume, high velocity, and variety that require advanced tools and techniques for analysis.

Blockchain

A distributed ledger technology that maintains a secure, transparent record of transactions across multiple computers.

Chatbot

An AI program that simulates human conversation through text or voice interactions, often used for customer service.

Classification

A supervised learning task that categorizes input data into predefined classes or categories.

Clustering

An unsupervised learning technique that groups similar data points together based on their characteristics.

Computer Vision

A field of AI that enables machines to interpret and understand visual information from images and videos.

Convolutional Neural Network (CNN)

A specialized neural network designed for processing grid-like data such as images, using convolutional layers to detect features.

Cross-Validation

A technique for assessing model performance by dividing data into training and validation sets multiple times.

Data Mining

The process of discovering patterns, correlations, and insights from large datasets using statistical and computational methods.

Dataset

A collection of data used for training, testing, or validating machine learning models.

Deep Learning

A subset of machine learning using neural networks with multiple layers to learn complex patterns from data.

Dimensionality Reduction

Techniques to reduce the number of features in datasets while preserving essential information.

Dropout

A regularization technique that randomly disables neurons during training to prevent overfitting.

Edge Computing

Processing data locally on devices rather than in centralized cloud servers, enabling faster real-time decisions.

Ensemble Learning

A technique that combines multiple models to achieve better performance than individual models alone.

Epoch

One complete pass through the entire training dataset during neural network training.

Ethics (AI)

Principles and guidelines ensuring AI systems are developed and used responsibly, fairly, and transparently.

Feature Engineering

The process of selecting, modifying, or creating input variables to improve machine learning model performance.

Fine-Tuning

Adjusting a pre-trained model for specific tasks by training on domain-specific data.

Foundation Model

Large-scale AI models trained on diverse data that can be adapted for various downstream tasks.

Generative AI

AI systems designed to create new content including text, images, audio, or video based on learned patterns.

GPT

Generative Pre-trained Transformer, an autoregressive language model that generates text by predicting next tokens.

Gradient Descent

An optimization algorithm that minimizes loss functions by iteratively moving in the direction of steepest descent.

Hallucination

When AI models generate factually incorrect or nonsensical outputs due to limitations in training data or architecture.

Hyperparameter

Configuration settings for machine learning algorithms that control the learning process, set before training begins.

Image Recognition

The ability of AI systems to identify and classify objects, people, or scenes within digital images.

Inference

The process of using a trained model to make predictions or decisions on new, unseen data.

JSON

JavaScript Object Notation, a lightweight data-interchange format commonly used for storing and transmitting AI model data.

K-Means

An unsupervised clustering algorithm that partitions data into k clusters based on feature similarity.

Knowledge Graph

A structured representation of knowledge showing relationships between entities, concepts, and facts.

Large Language Model (LLM)

Neural networks trained on vast text datasets to understand and generate human-like language for various tasks.

Learning Rate

A hyperparameter controlling how much model weights are adjusted during each training iteration.

Loss Function

A mathematical function measuring the difference between predicted and actual outcomes, guiding model optimization.

Machine Learning (ML)

A subset of AI enabling computers to learn and make decisions from data without explicit programming for each task.

Multi-Head Attention

An attention mechanism using multiple parallel attention heads to capture different types of relationships in data.

Natural Language Processing (NLP)

A field of AI focused on enabling computers to understand, interpret, and generate human language.

Neural Network

A computational model inspired by biological neural networks, consisting of interconnected processing nodes.

Normalization

The process of scaling data to a standard range to improve model training stability and performance.

Optimization

The process of adjusting model parameters to minimize error and improve performance on given tasks.

Overfitting

When a model learns training data too well, capturing noise and failing to generalize to new data.

Prompt Engineering

The practice of crafting effective input prompts to guide AI models toward desired outputs and behaviors.

PyTorch

An open-source machine learning framework providing tools for building and training neural networks.

Quantum Computing

Computing technology using quantum mechanical principles to process information exponentially faster than classical computers.

Query

A request for information from a database or AI system, often in natural language form.

Reinforcement Learning

A learning paradigm where agents learn optimal actions through trial and error, receiving rewards or penalties.

Regression

A supervised learning task predicting continuous numerical values based on input features.

Robotics

The field combining AI with mechanical engineering to create intelligent machines capable of physical tasks.

Supervised Learning

A machine learning approach using labeled data to train models for prediction or classification tasks.

Synthetic Data

Artificially generated data that mimics real-world data characteristics, used when actual data is scarce or sensitive.

Transformer

A neural network architecture using attention mechanisms, fundamental to modern language models like GPT and BERT.

Transfer Learning

A technique using knowledge from pre-trained models to improve performance on related tasks with limited data.

Turing Test

A test of machine intelligence measuring ability to exhibit intelligent behavior indistinguishable from humans.

Unsupervised Learning

Machine learning using unlabeled data to discover hidden patterns, structures, or relationships without human guidance.

Validation

The process of evaluating model performance on data separate from training set to assess generalization ability.

Vector

A mathematical representation of data as arrays of numbers, enabling computational processing in AI systems.

Weights

Numerical parameters in neural networks that determine the strength of connections between neurons, adjusted during training.

XAI (Explainable AI)

AI systems designed to provide clear, understandable explanations for their decisions and reasoning processes.

YOLO

You Only Look Once, a real-time object detection algorithm that identifies and locates objects in images efficiently.

Zero-Shot Learning

A machine learning approach enabling models to perform tasks or recognize classes without explicit training examples.