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AI Terms Dictionary: Your Guide to the World of Artificial Intelligence Description: Decode complex AI terminology with our comprehensive dictionary. Find clear and concise definitions for: Core AI concepts Machine learning methods Deep learning algorithms AI application areas This dictionary is an essential resource for: Beginners learning about AI Professionals looking to expand their knowledge Anyone who wants to better understand AI Dictionary of AI terms A Activation Function: A mathematical function within a neuron that determines its output, crucial for enabling networks to learn complex patterns. Algorithm: A set of instructions that a computer follows to perform a task. Artificial General Intelligence (AGI): A hypothetical type of AI that possesses human-level intelligence and adaptability across various domains and tasks. Artificial Intelligence (AI): The broad field encompassing the simulation of intelligent behavior in computers, with the aim of creating systems that can learn, reason, and act autonomously. B Backpropagation: The core method for calculating errors and adjusting weights within neural networks during the training process. Bias: The tendency of a model to favor certain outcomes over others, potentially leading to unfair or discriminatory results. Big Data: Datasets that are exceptionally large and complex, requiring specialized techniques for processing and analysis. C Chatbot: A computer program designed to engage in conversations with human users, often through text or voice interactions. Classification: A machine learning task where a model learns to assign categories to data points (e.g., classifying an email as spam or not spam). Clustering: A machine learning task focused on grouping similar data points without pre-defined labels. Cloud Computing: The on-demand delivery of computing resources, including AI tools and platforms, over the internet. Computer Vision (CV): The field of AI that enables computers to extract meaningful information from images and videos. D Data: The raw information used to train and evaluate machine learning models Data Preprocessing: The essential process of cleaning, transforming, and preparing data for use in machine learning models. Dataset: A structured collection of data used for training and testing machine learning models. Deep Learning (DL): A subset of machine learning that uses multi-layered artificial neural networks to learn complex representations from data. Deepfake: Manipulated media (images, videos, audio) created using AI techniques, often with the intent to deceive. E Embeddings: Mathematical representations of words or other data points that capture their semantic meaning and relationships. Explainable AI (XAI): Techniques and methods aimed at understanding and interpreting the decision-making processes of AI models. F Feature: A measurable characteristic or property of a data point used as an input to a machine learning model. G Generalization: The ability of a machine learning model to perform accurately on new, unseen data. Generative Adversarial Network (GAN): A deep learning architecture where two neural networks compete: one generates samples, the other tries to distinguish between real and fake. Gradient Descent: An iterative optimization algorithm commonly used to minimize errors and find the best parameters for a machine learning model. H Hyperparameter: A configuration setting for a machine learning model that is set before the training process begins (e.g., learning rate, number of layers in a neural network). I Inference: The process of using a trained machine learning model to make predictions or decisions on new data. Internet of Things (IoT): A network of interconnected devices with sensors that can collect and exchange data. L Label: In supervised learning, the target output or correct answer associated with a data point, used to guide the model's learning. M Machine Learning (ML): A subset of AI focused on algorithms and techniques that enable computers to learn from data without being explicitly programmed. Model: A mathematical representation of patterns learned from data, used for making predictions or decisions. N Natural Language Processing (NLP): The field of AI concerned with the interaction between computers and human language, including understanding and generation. Neural Network: A type of machine learning algorithm inspired by the structure of the biological brain, composed of interconnected nodes (neurons). Neuron: The basic computational unit within an artificial neural network. O Overfitting: A situation where a model learns the training data too well, including noise and anomalies, leading to poor performance on new data. P Precision: A performance metric in classification tasks. Measures the proportion of true positive predictions out of all positive predictions made by the model. R Recall: A performance metric in classification tasks. Measures the proportion of true positives correctly identified by the model. Regression: A machine learning task where the model predicts a continuous numerical value (e.g., house price prediction). Reinforcement Learning (RL): A type of machine learning where an agent learns through trial and error by interacting with an environment and receiving rewards or punishments. S Supervised Learning: A machine learning paradigm where models are trained on labeled datasets (input-output pairs are provided). T Transfer Learning: A technique where a model pre-trained on one task is re-purposed for a related task, improving efficiency and performance. U Unsupervised Learning: A machine learning paradigm where models discover patterns in data without explicit labels. V Validation Set: A portion of the dataset used to tune model hyperparameters and help prevent overfitting. W Weights: The adjustable parameters within a neural network that determine the strength of connections between neurons. Learning involves updating these weights. X XAI (Explainable AI): The field focused on developing techniques to understand and explain the decisions made by AI models, fostering transparency and trust. Less Common (But Important!) Terms Adversarial Examples: Inputs to machine learning models intentionally crafted to cause misclassification, exposing vulnerabilities. Autoencoder: A type of neural network used for unsupervised learning, often for dimensionality reduction or feature representation. Backtracking: A search algorithm that systematically explores potential solutions, reversing direction when a dead-end is reached. Bayesian Inference: A statistical approach to updating beliefs about a hypothesis as new data becomes available. Capsule Networks: A type of neural network architecture designed to better handle hierarchical relationships and viewpoints. Dimensionality Reduction: Techniques for transforming data into a lower-dimensional representation that preserves essential information. Domain Adaptation: The ability to adapt a model trained in one context (domain) to perform well in a different but related domain. Ensemble Learning: The process of combining multiple machine learning models to improve overall predictive performance. Evolutionary Algorithms: Optimization methods inspired by biological evolution, used for finding solutions to complex problems. Fuzzy Logic: A type of logic that deals with degrees of truth rather than simply true or false, useful for handling uncertainty.