AI Glossary
Welcome to the AI Glossary, a comprehensive guide to the terminology and concepts related to the field of Artificial Intelligence (AI).
AI is a rapidly advancing field that is transforming our world, and understanding the terminology and concepts is essential to staying up to date with the latest developments.
This glossary provides clear and concise definitions of key terms and concepts in AI, ranging from basic concepts like machine learning and neural networks to more advanced topics like deep learning and natural language processing.
Whether you are a student, researcher, or simply interested in learning more about AI, this glossary is designed to provide you with the information you need to navigate the complex world of AI terminology.
So, let’s get started and explore the fascinating world of Artificial Intelligence!
| Activation function | A function applied to the output of a neural network node to determine its activation level. |
| Activation map | A visual representation of the output of each node in a convolutional neural network layer. |
| Adversarial machine learning | A technique where an attacker intentionally introduces misleading or false data to a model to cause it to make incorrect predictions. |
| Algorithm | A set of rules or steps used to solve a problem or perform a task. |
| Artificial intelligence (AI) | The simulation of human intelligence processes by computer systems. |
| Attention mechanism | A technique used in neural networks to selectively focus on certain parts of the input data. |
| Autoencoder | A neural network architecture that learns to compress and decompress data, often used for unsupervised learning. |
| Backpropagation | An algorithm used to adjust the weights of a neural network to improve its performance. |
| Bayesian network | A probabilistic graphical model that uses Bayesian inference to make predictions about uncertain events. |
| Bias | The tendency for a model to make inaccurate predictions due to a lack of diversity in the training data or other factors. |
| Big data | Extremely large and complex datasets that require advanced computing tools to analyze. |
| Capsule network | A type of neural network that uses capsules to represent features at different levels of abstraction. |
| Chatbot | An AI application designed to simulate conversation with human users. |
| Clustering | A technique in unsupervised learning where similar data points are grouped together. |
| Computer vision | The ability of machines to interpret and understand visual information. |
| Convolutional neural network (CNN) | A type of neural network that is commonly used in computer vision tasks. |
| Convolution | A mathematical operation used in computer vision to extract features from images. |
| Data augmentation | The process of artificially increasing the size of a dataset by creating variations of the original data. |
| Data mining | The process of discovering patterns and insights in large datasets. |
| Data pipeline | A series of steps used to prepare and transform data for use in a machine learning model. |
| Data preprocessing | The process of cleaning and transforming raw data to make it suitable for use in a machine learning model. |
| Decision boundary | The boundary that separates the different classes in a classification problem. |
| Decision tree | A type of machine learning algorithm that uses a tree-like structure to model decisions and their possible consequences. |
| Deep learning | A subset of machine learning that uses neural networks with many layers to extract high-level features from data. |
| Deep reinforcement learning | A type of reinforcement learning that uses deep neural networks to make decisions. |
| Dimensionality reduction | The process of reducing the number of variables or features in a dataset to improve its performance. |
| Dropout | A regularization technique used in neural networks to prevent overfitting by randomly dropping out nodes during training. |
| Ensemble learning | A technique where multiple models are trained and combined to improve overall performance. |
| Ensemble model | A model composed of multiple individual models that are combined to make predictions. |
| Ethics in AI | The study of ethical issues related to the design, development, and deployment of AI systems. |
| Explainable AI | The ability of an AI system to provide transparent and understandable explanations for its decisions or predictions. |
| Feature engineering | The process of selecting and extracting relevant features from raw data to improve the performance of a machine learning model. |
| Federated learning | A type of machine learning where the model is trained on distributed devices without sharing the raw data. |
| Gated recurrent unit (GRU) | A type of recurrent neural network that uses gates to control the flow of information. |
| Generative adversarial network (GAN) | A type of neural network that consists of two models trained together to generate new data that resembles the original training data. |
| Generative model | A type of machine learning model that generates new data based on patterns learned from a training set. |
| Gradient descent | An optimization algorithm used to minimize the error of a machine learning model by adjusting its parameters. |
| Hyperparameter tuning | The process of finding the optimal settings for the hyperparameters of a machine learning model. |
| Hyperparameters | The settings that can be adjusted to optimize the performance of a machine learning model. |
| Inference time | The time it takes for a trained model to make predictions on new data. |
| Inference | The process of using a trained model to make predictions on new data. |
| Kernel | A function used in machine learning to transform input data into a higher-dimensional space to make it more separable. |
| K-nearest neighbors (KNN) | A machine learning algorithm that makes predictions based on the similarity of new data to known data points. |
| L1 regularization | A regularization technique used in machine learning to reduce the complexity of a model by adding a penalty term for large parameter values. |
| L2 regularization | A regularization technique used in machine learning to reduce the complexity of a model by adding a penalty term for the sum of squared parameter values. |
| Logistic regression | A type of regression analysis used to model the probability of a binary outcome. |
| Long short-term memory (LSTM) | A type of recurrent neural network (RNN) that is designed to handle sequences of data with long-term dependencies. |
| Machine learning | A subset of AI that involves training machines to learn from data and make predictions or decisions. |
| Mini-batch | A subset of the training data used to update the parameters of a machine learning model during training. |
| Multi-layer perceptron (MLP) | A type of neural network architecture that consists of multiple layers of nodes. |
| Multi-task learning | A technique where a single model is trained to perform multiple related tasks. |
| Natural language generation (NLG) | The ability of machines to generate human-like language. |
| Natural language processing (NLP) | The ability of machines to understand and interpret human language. |
| Neural network | A type of machine learning algorithm that is designed to recognize patterns in data. |
| Object detection | The ability of machines to identify and locate objects within an image or video. |
| Overfitting | When a machine learning model becomes too complex and performs well on the training data but poorly on new, unseen data. |
| Overfitting | When a machine learning model becomes too complex and performs well on the training data but poorly on new, unseen data. |
| Precision-recall curve | A visualization of the trade-off between precision and recall for different decision thresholds in a binary classification problem. |
| Precision | A metric used to measure the proportion of true positive predictions among all positive predictions made by a model. |
| Principal component analysis (PCA) | A technique used to reduce the dimensionality of a dataset by identifying the most important features. |
| Random forest | An ensemble learning method that combines multiple decision trees to make predictions. |
| Recall | A metric used to measure the proportion of true positive predictions among all actual positive cases in a dataset. |
| Regression | A type of machine learning that is used to predict continuous values, such as stock prices or temperature. |
| Reinforcement learning agent | The decision-making component of a reinforcement learning system. |
| Reinforcement learning | A type of machine learning that involves training an agent to make decisions based on rewards and punishments. |
| Reinforcement signal | A signal used in reinforcement learning to indicate whether an action was good or bad. |
| Sentiment analysis | The process of using NLP to analyze and classify the emotional tone of text or speech. |
| Stochastic gradient descent (SGD) | A variation of gradient descent that uses a randomly selected subset of the training data for each update. |
| Supervised learning | A type of machine learning where the model is trained on labeled data with known outcomes. |
| Support vector machine (SVM) | A type of machine learning algorithm used for classification and regression analysis. |
| Tensor | A multi-dimensional array used as the fundamental data structure in deep learning. |
| Test data | The data used to evaluate the performance of a machine learning model. |
| Time series analysis | A type of data analysis used to model and predict trends in time-based data. |
| Training data | The data used to train a machine learning model. |
| Transfer learning | A technique where a pre-trained model is used as a starting point for training a new model on a different task. |
| Transformer | A type of neural network architecture used for natural language processing tasks. |
| Underfitting | When a machine learning model is too simple and cannot capture the complexity of the data. |
| Unstructured data | Data that has no predefined structure or organization, such as text, images, or videos. |
| Unsupervised learning | A type of machine learning where the model is trained on unlabeled data and must find patterns on its own. |
| Variational autoencoder (VAE) | A type of autoencoder that learns to generate new data by sampling from a probability distribution. |