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Webdesk AI Glossary

Webdesk AI Glossary

A* Search: A computer algorithm used in graph traversal and pathfinding, combining the best features of uniform-cost search and pure heuristic search.

Abstraction Layer
An abstraction layer is a crucial concept in software architecture that facilitates the separation of a system's functionality into distinct levels or parts. This separation allows developers to interact with a system without needing detailed knowledge of its underlying implementations. In the context of AI, abstraction layers can help isolate machine learning algorithms from the complexities of data processing, model training, and inference engines, enabling easier integration, scalability, and maintenance.
Accuracy Paradox
The Accuracy Paradox is a phenomenon observed in predictive modeling where models with lower overall accuracy can outperform models with higher accuracy in specific application areas. This paradox often arises due to imbalanced datasets or the misalignment between the model's performance metrics and the actual goals of the application. It highlights the importance of choosing appropriate metrics that reflect the real-world effectiveness of predictive models, beyond just accuracy.
In the realm of classification models, accuracy is a fundamental metric that measures the proportion of correct predictions made by the model out of all predictions. It is calculated as the number of true positives and true negatives divided by the total number of cases. While accuracy is intuitive and widely used, it may not always provide a complete picture of a model's performance, especially in cases of class imbalance. Therefore, it's often used alongside other metrics like precision, recall, and F1 score.
Activation Function
An activation function in a neural network plays a pivotal role by deciding whether a neuron should be activated or not, based on the weighted sum of its inputs. It introduces non-linearity into the network, enabling it to learn and model complex data patterns. Common activation functions include Sigmoid, Tanh, ReLU (Rectified Linear Unit), and Softmax, each serving different purposes and characteristics in the network's learning process.
Activation Layer
In neural networks, an activation layer is a layer that applies a non-linear activation function to its input signals. This transformation allows the network to process and learn complex patterns in the input data. Activation layers are critical for deep learning models' ability to perform tasks beyond simple linear classification, such as image recognition, natural language processing, and more, by enabling the modeling of complex functions.
Activation Maps
Activation maps are visual tools used to understand and interpret the behaviors of neural networks. They provide a graphical representation of the activations (outputs) produced by neurons in a given layer for a specific input, such as an image. By analyzing activation maps, researchers and developers can gain insights into which features of the input data are being recognized and emphasized by different layers of the network, aiding in model optimization and debugging.
Active Learning
Active Learning is a semi-supervised machine learning approach where the algorithm selectively queries the user or an oracle to label new data points with the aim of improving learning efficiency and model performance. This technique is particularly useful in scenarios where unlabeled data is plentiful but labeling is costly or time-consuming. By focusing on samples that the model finds uncertain or informative, active learning seeks to maximize performance gains with minimal labeled data.
Active Perception
Active Perception refers to a strategy in robotics and artificial intelligence where a system actively alters its state or manipulates its environment to enhance its sensory feedback. Instead of passively analyzing incoming data, the system engages in behaviors or movements that improve its understanding or perception of the environment. This approach is critical in tasks that require spatial awareness and interaction with complex, dynamic environments.
Actor-Critic Method
The Actor-Critic Method is a sophisticated framework in reinforcement learning that combines the advantages of policy-based and value-based approaches. It involves two main components: the actor, which decides on the best action to take, and the critic, which evaluates the action taken by the actor by estimating the value function. This method allows for more stable and efficient learning by simultaneously optimizing both the policy and the value function.
Adaboost, short for Adaptive Boosting, is an ensemble learning technique that combines multiple weak classifiers to create a strong classifier. The algorithm iteratively adjusts the weights of incorrectly classified instances so that subsequent classifiers focus more on difficult cases. This process leads to improved model accuracy on complex classification tasks. Adaboost is widely used due to its simplicity, effectiveness, and the ease with which it can be combined with any learning algorithm.
Adaptive Behavior
Adaptive Behavior encompasses the actions and strategies employed by AI systems to adjust their behavior in response to changing environmental conditions. The aim is to enhance the system's effectiveness and efficiency in achieving its goals. This concept is fundamental in areas such as autonomous vehicles, smart robotics, and dynamic decision-making systems, where the ability to adapt to new situations is critical for success.
Adaptive Control
Adaptive Control is a control strategy utilized in robotics and automation where the controller dynamically adjusts its parameters in real-time to handle variations in the system or the environment. This approach ensures optimal performance even in the face of changing conditions or system dynamics, making it invaluable for applications that require high precision and adaptability.
Adaptive Learning
Adaptive Learning is an advanced application of AI in education that personalizes the learning experience based on the individual learner's needs, abilities, and performance. By continuously analyzing the learner's interactions and progress, the system adjusts the content difficulty, learning paths, and pacing, offering a tailored educational experience that enhances learning efficiency and engagement.
Adaptive Systems
Adaptive Systems are designed to autonomously adjust their behavior or parameters in response to internal feedback or external environmental changes. These systems are characterized by their flexibility, resilience, and ability to learn from experience, making them suitable for a wide range of applications from natural language processing to complex ecosystem management.
Addressable Memory
Addressable Memory is a fundamental concept in computer science where each byte of memory can be accessed directly using a unique identifier or address. This principle is critical for the efficient storage and retrieval of data, including the implementation of neural networks, where the ability to access and modify specific weights and biases directly impacts learning and performance.
Adversarial Examples
Adversarial Examples are specially crafted inputs designed to deceive machine learning models into making incorrect predictions. These inputs exploit the model's vulnerabilities, revealing significant challenges in the security and robustness of AI systems. Understanding and defending against adversarial examples is crucial for applications where reliability and safety are paramount.
Adversarial Machine Learning
Adversarial Machine Learning studies the interaction between machine learning models and malicious adversaries who aim to compromise the models' integrity or performance. This field explores both the creation of adversarial attacks to test model vulnerabilities and the development of defensive techniques to enhance model robustness against such attacks.
Adversarial Networks
Adversarial Networks, often exemplified by Generative Adversarial Networks (GANs), involve the training of two models in opposition to each other: a generator that creates data resembling the training set, and a discriminator that attempts to distinguish between real and generated data. This competitive process improves both models, enabling the generation of highly realistic synthetic data.
Adversarial Training
Adversarial Training is a robustification technique that involves incorporating adversarial examples into the training process. By learning to correctly classify these intentionally misleading inputs, the model develops increased resilience against adversarial attacks, enhancing its security and reliability in adversarial environments.
Affective Computing
Affective Computing is the study and development of systems and devices capable of recognizing, interpreting, processing, and simulating human emotions. This interdisciplinary field combines insights from psychology, computer science, and cognitive science to enable computers to respond to human emotions in a nuanced and context-aware manner, improving human-computer interaction.
Affinity Propagation
Affinity Propagation is a clustering algorithm that identifies exemplars among data points and forms clusters based on the concept of message passing. Unlike other clustering methods that require the number of clusters to be specified in advance, Affinity Propagation adapts to the data, making it effective for tasks where the structure of the data is not known a priori.

Agent: An entity that observes and acts upon its environment, aiming to achieve certain goals.

Algorithm: A rule or instruction set that computers follow for problem-solving.

AlphaGo: A computer program developed by DeepMind to play the board game Go, known for defeating a world champion.

Anomaly Detection: Identifying rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.

Artificial General Intelligence (AGI): The hypothetical ability of an artificial intelligence system to understand or learn any intellectual task that a human being can. It involves having common sense, general knowledge, and the ability to reason and make judgments like humans do.

Artificial Intelligence (AI): The simulation of human intelligence processes by computer systems, encompassing learning, reasoning, and self-correction.

Attention Maps: Visualizations that show where a neural network, especially in tasks like image classification, is 'looking' when making a decision.

Attention Mechanism: A mechanism in deep learning models that allows them to focus on specific parts of the input when producing an output.

AutoML: Automated machine learning, where the process of constructing machine learning models is automated.

Backdoor Attacks: Malicious attacks on machine learning models where the attacker introduces a backdoor to the model during training.

Backpropagation: A method for training neural networks by updating weights using the gradient of the loss function.

Bag of Words (BoW): A representation of text data where the frequency of each word is used as a feature.

Bagging: An ensemble method that creates separate subsets of the original data and uses them to generate multiple classifiers.

Batch Normalization: A technique used to increase the stability of a neural network by normalizing the input of each layer.

Batch Size: The number of training examples used in one iteration or forward/backward pass of algorithm optimization.

Bayesian Network: A probabilistic graphical model representing variables and their dependencies via a directed acyclic graph.

BERT (Bidirectional Encoder Representations from Transformers): A transformer-based model designed to understand the context of words in a sentence by considering both the left and right context in all layers.

Bias (in AI): Systematic prejudice in AI decisions or predictions due to flawed algorithmic assumptions.

Bias (Statistical): The systematic error introduced by approximating a real-world problem which does not take into account all possible factors.

Bias-Variance Tradeoff: The balance between the error due to bias (wrong assumptions) and the error due to variance (overly complex models) in machine learning models.

Bidirectional RNN: A type of RNN that processes sequences from both ends towards the center, commonly used in natural language processing.

Capsule Network: A type of neural network designed to overcome shortcomings of convolutional neural networks, particularly in handling spatial hierarchies between features.

Catastrophic Forgetting: When neural networks forget previously learned information upon learning new information.

Chatbot: A software application designed to simulate human conversation either through text or voice interaction.

Clustering: The task of grouping a set of objects so that objects in the same group are more similar to each other than to those in other groups.

Cognitive Computing: Systems imitating human cognition to provide insights, typically involving NLP and ML.

Collaborative Filtering: A method used in recommendation systems where users get recommendations based on the likes and dislikes of similar users.

Concept Drift: Situations where the statistical properties of target variables change over time, making model updates necessary.

Confusion Matrix: A table that describes the performance of a classification model by comparing actual versus predicted classifications.

Content-based Filtering: Recommendation algorithms that provide personalized recommendations by comparing content descriptions and user profiles.

Contrastive Loss: A type of loss function that encourages a neural network to produce similar or dissimilar embeddings for pairs of inputs based on their labels.

Convolution: A mathematical operation used in convolutional neural networks, applied to the input data using a convolution filter or kernel to produce a feature map.

Convolutional Neural Network (CNN): A deep learning algorithm predominantly used for image and video recognition.

Cross-Validation: A technique to assess how well the model will generalize to an independent data set.

Curriculum Learning: A training method where the model is first trained on simpler tasks, gradually increasing the task's complexity.

Data Augmentation: Techniques that increase the amount of training data by slightly altering the input data without changing its meaning or interpretation.

Data Imputation: The process of replacing missing data with substituted values.

Data Leakage: When information from the testing dataset is, in some way, used during training, often leading to overly optimistic performance metrics.

Data Mining: Uncovering patterns and knowledge from vast data amounts using ML and statistical techniques.

Data Pipeline: A set of data processing elements that manage and transform raw data into usable input for analytics or machine learning models.

Data Wrangling: The process of cleaning, structuring, and enriching raw data into a desired format for better decision-making.

Decision Tree: A flowchart-like structure wherein each node represents a test on an attribute, each branch represents the test outcome, and each leaf node represents a class label.

Deep Learning: A ML subset that employs multi-layered neural networks to analyze data factors.

Deterministic Algorithm: An algorithm that, given a particular input, will always produce the same output, with the underlying machine always passing through the same sequence of states.

Differential Privacy: A system that provides means to maximize the accuracy of queries from statistical databases while minimizing the chances of identifying its entries.

Digital Twin: A virtual model designed to accurately reflect a physical object, system, or process.

Domain Adaptation: Techniques to adapt a machine learning model from a source domain to a different, but related, target domain.

Dropout: A regularization technique for neural networks which randomly sets a number of outputs of hidden units to zero during training.

Dropout: A regularization technique used in neural networks where a subset of neurons is randomly ignored during training.

Eager Execution: An imperative programming environment available in TensorFlow that evaluates operations immediately without building a computational graph.

Early Stopping: A form of regularization used to avoid overfitting when training a model with an iterative method, such as gradient descent.

Elman Network: A type of recurrent neural network where connections between units form a directed cycle, useful in time series prediction.

Embedding Layer: A layer in neural networks that transforms categorical data into a dense vector of fixed size.

Embedding Space: The vector space in which embeddings (like word embeddings) are positioned.

Embeddings: Representation of categorical data or text in a continuous vector space, often used in neural networks.

Ensemble Learning: Using multiple models to obtain better predictive performance than could be obtained from any of the constituent models.

Ensemble Methods: Combining predictions from multiple machine learning algorithms to produce a more robust and accurate prediction.

Entropy: A measure of randomness or unpredictability in a dataset.

Episodic Memory: Memory of specific events or experiences, as opposed to general knowledge.

Epoch: A single pass through the entire training dataset during training.

Evolutionary Algorithm: Algorithms inspired by the process of natural selection, used in optimization and search tasks.

Expert System: Computer systems that emulate decision-making abilities of a human expert.

eXplainable AI (XAI): An area in AI focused on creating transparent models that human users can understand.

Exponential Decay: A mathematical function where the decrease is proportional to the current value.

eXtreme Gradient Boosting (XGBoost): An efficient and scalable implementation of gradient boosting.

F1 Score: A measure of a test's accuracy, defined as the harmonic mean of precision and recall.

Feature Engineering: The process of creating new features or transforming existing features to improve machine learning model performance.

Feature Extraction: The process of transforming raw data into a set of characteristics (features) that are relevant for analysis or modeling.

Feature Scaling: The method used to normalize the range of independent variables or features of the data.

Feature Selection: The process of selecting a subset of relevant features to construct a model.

Feature: A measurable phenomenon property, serving as an ML input variable.

Federated Learning: A machine learning setting where the model is trained across multiple devices or servers while keeping data localized.

Feedforward Network: Neural networks wherein connections between the nodes do not form a cycle.

Few-shot Learning: Training a machine learning model using very few labeled examples of the task of interest.

Fully Connected Layer: A layer in a neural network where each neuron is connected to every neuron in the previous layer.

Fuzzy Logic: A system of logic that allows for degrees of truth, rather than just true or false.

Gated Neural Networks: Neural networks containing logic gates within their architecture, typically used to control the flow of information.

Gated Recurrent Units (GRUs): A type of recurrent neural network that can adaptively capture dependencies of different time scales.

Gaussian Mixture Model (GMM): A probabilistic model representing normally distributed subpopulations within an overall population.

Generative Adversarial Networks (GANs): ML systems where two neural networks, a generator and a discriminator, compete to refine their capabilities.

Genetic Algorithm: An optimization algorithm based on the process of natural selection, used in AI to find approximate solutions to optimization and search problems.

Gradient Clipping: A technique to prevent gradients from becoming too large, which can result in an unstable training process.

Gradient Descent: An optimization algorithm used to minimize a function iteratively.

Graph Neural Network: Neural networks designed to process data structured as graphs, capturing the relationships between nodes.

Graph Theory: A field of mathematics about graphs, which are structures used to model pairwise relations between objects.

Greedy Algorithm: An algorithmic paradigm that follows the problem-solving heuristic of making the locally optimal choice at each stage.

Grid Search: An exhaustive search method used to find the best combination of hyperparameters for a machine learning model.

GridWorld: A common environment used in reinforcement learning where an agent learns to navigate a grid to reach a goal.

Hamming Distance: A metric used to measure the difference between two strings of equal length, counting the number of positions at which the corresponding elements are different.

Hashing: The transformation of data into a fixed-size series of bytes, often used in data retrieval and for checking data integrity.

Hebb's Rule: A neuroscientific theory suggesting an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell.

Hebbian Learning: A learning rule that states that if a synapse repeatedly takes part in firing the postsynaptic cell, the strength of the synapse is selectively increased.

Heteroscedasticity: A situation where the variability of a variable is unequal across different values of another variable, typically seen in regression analysis.

Heuristic Optimization: Techniques that use heuristic methods to find reasonably good solutions in situations where finding the optimal solution is computationally challenging.

Heuristic Search: A search strategy that uses rules or shortcuts to produce good-enough solutions to complex problems more quickly.

Heuristic: A problem-solving approach using practical methods to find an optimal solution.

Hierarchical Clustering: A method of cluster analysis that builds a hierarchy of clusters either by a bottom-up or top-down approach.

Hopfield Network: A form of recurrent artificial neural network that serves as an associative memory with binary threshold units.

Hyperbolic Tangent (tanh): An activation function that outputs values between -1 and 1.

Hyperparameters: Parameters in a machine learning model that are set before training starts, as opposed to parameters which are learned during training.

Image Segmentation: The process of dividing a digital image into distinct categories to simplify or change the image's representation.

Imbalanced Data: Datasets where classes are not represented equally.

Imputation: The process of replacing missing data with substituted values.

Incremental Learning: A training paradigm where the model is trained gradually, typically by being exposed to new data over time.

Inductive Reasoning: A type of reasoning where generalizations are made based on specific instances.

Inference: The process of using a trained machine learning model to make predictions on new, unseen data.

Information Bottleneck: A theory that seeks to understand the fundamental trade-off between the complexity and accuracy of representations in neural networks.

Information Retrieval: The science of extracting the relevant parts from large collections of data.

Instance-based Learning: A family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training.

Interpretability: The degree to which a machine learning model's predictions can be understood by humans.

Isocontours: Curves on which a function has a constant value, used in optimization landscapes to understand the shape of loss functions.

Isotropic: Having properties that are uniform in all directions, commonly referenced in algorithms dealing with distance or similarity.

Jaccard Similarity: A statistic used to measure similarity between finite sample sets.

Jensen-Shannon Divergence: A method to measure the similarity between two probability distributions.

Johnson Noise: The electronic noise generated by the thermal agitation of the charge carriers (usually electrons) inside an electrical conductor at equilibrium.

Johnson-Lindenstrauss Lemma: A mathematical result concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space.

Joint Embeddings: Representations learned from data of multiple modalities, such as learning embeddings from both text and images.

Joint Probability Distribution: The probability distribution of two or more random variables.

Joint Probability: A statistical measure that calculates the likelihood of two events occurring together and at the same point in time.

Jupyter Notebook: An open-source web application that allows for the creation and sharing of live code, equations, visualizations, and narrative text.

Jupyter: An open-source tool for interactive computing and data analysis.

Just-In-Time Compilation: Compilation done during the execution of a program, rather than before the program is run.

K-fold Cross Validation: A technique for assessing the performance of an algorithm by training and evaluating it multiple times using different training and testing splits.

K-means: An unsupervised machine learning algorithm used for partitioning a dataset into a set of distinct, non-overlapping subgroups.

K-nearest Neighbors (KNN): An algorithm that classifies a data point based on how its neighbors are classified.

k-NN (k-Nearest Neighbors): A type of instance-based learning where the output is classified based on the majority of k nearest data points in the training dataset.

Kernel Trick: A method used in machine learning to make linear algorithms work in non-linear situations without explicitly computing the coordinates in the higher-dimensional space.

Kernel: A function used in kernel methods to compute the similarity or distance between data points.

Keyphrase Extraction: The process of extracting relevant and representative phrases from a piece of text.

Knowledge Base: A technology used to store complex structured and unstructured information used by computers.

Knowledge Discovery in Databases (KDD): The process of discovering useful knowledge from a collection of data.

Knowledge Distillation: A technique where a smaller model is trained to reproduce the behavior of a larger model (or an ensemble of models).

Knowledge Graph: A knowledge base that links data items in a structured manner, employing a graph-based structure.

Knowledge Representation: The area of AI concerned with emulating human knowledge on a computer.

Label Encoding: Converting categorical data into a form that could be provided to ML algorithms to do a better job in prediction.

Labeled Data: Data that has been tagged with one or more labels, often used in supervised learning.

Latent Dirichlet Allocation (LDA): A generative probabilistic model used for collections of discrete data such as text corpora.

Latent Semantic Analysis (LSA): A technique in NLP and information retrieval to identify relationships between a collection of documents and terms they contain.

Latent Space: The compressed representation of data in a lower-dimensional space, often the output of an encoder in architectures like autoencoders.

Leaky ReLU: A type of activation function that is defined as the positive part of the operand, allowing a small gradient when the unit is not active.

Learning Curve: A plot of the learning performance of a machine learning model over time or experience.

Learning Rate: A hyperparameter defining the adjustment step size when updating the weights in neural networks.

Learning to Rank: Techniques used in machine learning to train models for ranking tasks, commonly used in recommendation systems and search engines.

Lexical Analysis: The process of converting a sequence of characters into a sequence of tokens in NLP.

Linear Regression: A linear approach to modeling the relationship between dependent and independent variables.

Long Short-Term Memory (LSTM): A type of recurrent neural network capable of learning long-term dependencies.

Loss Function: A measure of how well a model's predictions match the true values, guiding training algorithms.

Low-Rank Approximation: A mathematical method used to approximate data by its most important components (often used in matrix factorization).

Machine Learning (ML): A subset of AI wherein computers learn from data without being explicitly programmed.

Masked Language Model (MLM): A model that is trained to predict a masked word in a sentence, often used in models like BERT.

Maximum Likelihood Estimation (MLE): A method used to estimate the parameters of a statistical model.

Mean Squared Error: A metric that measures the average squared differences between the estimated and true values.

Meta-learning: Algorithms that learn from multiple tasks and use that learning to perform new, unseen tasks.

Model Agnostic: A machine learning method or tool that is designed to work with any model or framework.

Model Evaluation: The process of assessing the performance of a trained machine learning model using various metrics and techniques.

Model Inversion Attack: An attack on machine learning models wherein the attacker tries to reconstruct the training input from model outputs.

Model: An ML term denoting systems trained to make predictions or decisions without using explicit instructions.

Momentum Optimization: An optimization algorithm used to accelerate gradients vectors in the right directions, thus leading to faster converging.

Momentum: A method used to accelerate the gradient vector in the right direction, thus leading to faster converging.

Monte Carlo Methods: Computational algorithms that rely on repeated random sampling to obtain numerical results for probabilistic computation.

Multi-task Learning: A machine learning approach where a model is trained to solve multiple tasks at the same time, improving generalization.

Multimodal Learning: Training models on data from multiple modalities (e.g., text and images) to improve performance and enable cross-modality predictions.

Naive Bayes: A classification technique based on applying Bayes’ theorem with the assumption of independence between every pair of features.

Natural Language Processing (NLP): An AI branch focusing on computer and human interaction through natural language.

Nearest Neighbor Search: An optimization problem to find closest points in metric spaces.

Nesterov Accelerated Gradient: A method to speed up gradient descent algorithms in optimization problems.

Neural Architecture Search (NAS): The automated process of discovering neural network architectures that perform better for a specific task.

Neural Network: Algorithms aiming to identify data relationships, simulating the human brain's structure and function.

Neural Turing Machine: A neural network model that, in addition to training data, can read and write to memory matrices, mimicking some behavior of the Turing machine.

Neurosymbolic AI: An approach that combines neural networks with symbolic logical reasoning. It aims to bridge the strengths of data-driven deep learning models like pattern recognition with the interpretability and generalization abilities of symbolic AI.

Node Embedding: Techniques used to learn continuous representations for nodes in a network.

Noise Contrastive Estimation (NCE): A method used in machine learning to approximate the likelihood in models with a large number of output classes.

Non-linear Activation Function: A function applied at each node in a neural network, introducing non-linearity to the model.

Non-parametric Model: Models that do not assume a particular form for the relationship between a dataset's features and its output.

Normal Distribution: A probability distribution characterized by a bell-shaped curve, often used in statistics and machine learning.

Normalization: The process of scaling input data to a standard range, often to help neural networks converge more quickly during training.

Object Detection: The task of detecting instances of objects of a certain class within an image.

One-hot Encoding: A process by which categorical variables are converted into a binary matrix.

Ontology: A formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts.

OpenAI: A research organization focused on creating and promoting friendly AI that benefits humanity as a whole.

Optical Character Recognition (OCR): The mechanical or electronic conversion of scanned or photographed images of handwritten, typewritten or printed text into machine-encoded text.

Optimization Landscape: A visualization or representation of how a metric (like loss) changes as the parameters of a machine learning model change.

Out-of-Bag Error: An error estimate for random forests, computed as the mean prediction error on each training sample using only trees that did not have this sample in their bootstrap.

Out-of-Core Learning: Techniques used to train machine learning models on data that cannot fit into memory at once, often by using disk storage efficiently.

Outlier: A data point that differs significantly from other observations and may arise from variability or errors.

Over-segmentation: In image processing, the result of segmenting an image into more regions than necessary.

Overfitting: A model that learns training data noise due to its complexity, performing poorly on new data.

Overparameterization: Using more parameters than needed in a model. This can allow models to fit training data more closely, but may lead to overfitting.

Parameter Tuning: The process of selecting the best parameters for a machine learning model.

Pattern Recognition: The classification of input data into objects or classes based on key features.

Perception: An AI system's capacity to interpret its surroundings by recognizing objects, speech, and text.

Perceptual Loss: A loss function that compares high-level features between the predicted and target images in a pre-trained neural network.

Pooling Layer: A layer in a convolutional neural network used to downsample the spatial dimensions of the input, commonly using max or average operations.

Pose Estimation: The task of estimating the pose of an object, typically a person, in images or videos.

Precision: The number of true positive results divided by the number of all positive results, a metric used in classification.

Predictive Modeling: Using statistical techniques to predict outcomes, often based on historical data.

Principal Component Analysis (PCA): A method used to emphasize variation and bring out strong patterns in a dataset, reducing its dimensions.

Probabilistic Graphical Model: A framework for modeling large systems of variables that have inherent uncertainty.

Probabilistic Programming: A high-level programming method to define probabilistic models and then solve these automatically.

Prompt Engineering: The practice of carefully crafting the prompts provided to AI systems in order to elicit more useful, relevant, and helpful responses.

Prototype Networks: Neural network models that are trained to produce prototypes, which are used to classify new examples.

Q-learning: A model-free reinforcement learning algorithm used to learn a policy that tells an agent what action to take under certain circumstances.

Quality Assurance in AI: Processes to ensure that AI systems operate safely, effectively, and as intended.

Quantization: The process of constraining an input from a large set to output in a smaller set, primarily in digital signal processing.

Quantum Bits (Qubits): The fundamental unit of quantum information, analogous to a bit in classical computing.

Quantum Computing: Computation using quantum mechanical phenomena, such as superposition and entanglement, with implications for AI.

Quantum Machine Learning: An interdisciplinary field that bridges quantum physics with machine learning, often making use of quantum computing.

Quantum Neural Network (QNN): A type of artificial neural network that is based on the principles of quantum mechanics.

Quasi-Newton Method: An optimization algorithm to find the local maximum or minimum of a function.

Query Expansion: A technique used in information retrieval where the query sent by the user is expanded by adding synonyms or related words.

Query Optimization: The process of finding the most efficient way to execute a given query by considering the possible query plans.

Radial Basis Function (RBF): A real-valued function whose value depends only on the distance from the origin or a fixed point.

Random Forest: An ensemble learning method that creates a 'forest' of decision trees and merges their outputs.

Random Walk: A mathematical object known as a stochastic or random process that describes a path consisting of a succession of random steps.

Recall: The number of true positive results divided by the number of positive results that should have been returned.

Recency Bias: The tendency to weigh recent events more heavily than earlier events, which can affect machine learning models if not taken into account.

Recurrent Attention Models (RAM): Neural network models that can focus on different parts of the input data at each step in the computation.

Recurrent Neural Network (RNN): Neural networks with loops, allowing information to be stored over time, extensively used for sequential data.

Recursion: A method where the solution to a problem depends on smaller instances of the same problem.

Regularization Parameter: A hyperparameter used in some machine learning models that adds a penalty to increasing model complexity.

Regularization: Techniques to prevent overfitting by adding a penalty to the loss function.

Reinforcement Learning: ML where agents decide through a system of rewards and punishments.

Reinforcement Signal: In reinforcement learning, a signal that tells the agent how well it's doing in terms of achieving its goal.

Residual Connections: Direct connections added from the input to the output of a neural network layer, as seen in architectures like ResNet.

Residual Network (ResNet): A type of neural network architecture designed to overcome the vanishing gradient problem by introducing skip connections or shortcuts.

Robotics: An AI field concerning the design, operation, and use of robots.

Saliency Maps: Visualizations that show the most important parts of an input to a neural network, often used to interpret model decisions.

Self-supervised Learning: A type of machine learning where the model generates its own supervisory signal from the input data.

Semantic Analysis: The process of drawing meaning from textual information.

Semantic Segmentation: The task of classifying each pixel in an image into a specific class.

Semantic Web: An extension of the World Wide Web that allows data to be shared and reused across applications, enterprises, and communities.

Semi-supervised Learning: A type of machine learning that uses both labeled and unlabeled data for training, often to improve model performance without the need for extensive labeling.

Sequence to Sequence Models: Models that convert sequences from one domain to sequences in another domain, often used in machine translation.

Sequential Modeling: Techniques used in machine learning to handle data where order matters, such as time series or sequences.

Softmax: A function that takes an un-normalized vector and normalizes it into a probability distribution.

Sparse Representation: Representing data with a significant number of zero-valued entries.

State Space: The collection of all possible situations or configurations of a system.

Stochastic Gradient Descent (SGD): An iterative method for optimizing an objective function with suitable smoothness properties.

Supervised Learning: ML where models are trained on labeled data, containing both input and desired output.

Swarm Intelligence: Collective behavior of decentralized, self-organized systems, inspired by natural phenomena like bird flocking or ant colonies.

Synthetic Data Generation: The use of algorithms and statistical methods to create artificial data that resembles real data.

Temporal Difference Learning: A combination of Monte Carlo and dynamic programming methods to learn the value function in reinforcement learning.

Tensors: Multi-dimensional arrays used in deep learning frameworks such as TensorFlow to represent data.

Thompson Sampling: A heuristic algorithm used for the multi-armed bandit problem, balancing exploration and exploitation.

Time Series Forecasting: The use of a model to predict future values based on previously observed values.

Time-Series Analysis: Methods used to analyze time series data in order to extract meaningful statistics and characteristics of the data.

Tokenization: The process of converting text into tokens, often words, symbols, or subwords.

Topological Data Analysis (TDA): A method that gives qualitative and quantitative information about metric spaces.

Transfer Learning: A technique where a pre-trained model is used on a new, but related task, with minor adjustments.

Transferable Features: Features in a machine learning model that can be useful for multiple tasks or in multiple domains.

Transformer Architecture: A deep learning model used primarily in the field of NLP and known for its effectiveness and efficiency.

Transformer Models: A type of deep learning model architecture primarily used in natural language processing tasks.

Triplet Loss: A loss function used for metric learning that pulls together similar items and pushes apart dissimilar items.

Turing Test: A machine intelligence measure, gauging its ability to produce indistinguishable human responses.

U-Net: A convolutional neural network designed for biomedical image segmentation, particularly known for its architecture and efficient training.

Unbiased Estimation: In statistics, an estimator is said to be unbiased if its expected value is equal to the true value of the estimated parameter.

Uncertainty Estimation: Techniques used to estimate the uncertainty of predictions in machine learning models.

Under-sampling: Reducing the number of majority class samples to balance out the class distribution, typically used in handling imbalanced datasets.

Underfitting: A statistical model that cannot adequately capture the underlying data structure.

Univariate: Analysis of a single statistical variable.

Universal Approximation Theorem: A theory that states that a feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous functions on compact subsets of R^n, under mild assumptions on the activation function.

Unrolled Network: A representation of recurrent networks where the recurrent structure is "unrolled" into a feedforward structure with repeated layers.

Unsupervised Learning: ML trained on unlabeled data, aiming to uncover hidden patterns.

Unsupervised Pre-training: Training a machine learning model on an auxiliary task without using labeled data for the main task, so that it can be fine-tuned later with less labeled data.

Upsampling: The process of increasing the resolution or size of data, such as images.

Validation: The process of evaluating the performance of an ML model on a separate dataset not used during training.

Variance Bias Tradeoff: Refers to the tradeoff between a model's ability to fit the data well (low bias) and its ability to generalize well to new data (low variance).

Variance Inflation Factor: A measure of multicollinearity in regression analysis.

Variance Reduction: Techniques used in optimization to reduce the variance of the gradient estimates to accelerate convergence.

Variance: A measure of how spread out a set of data is, often used in statistics and machine learning.

Variational Autoencoders (VAE): Generative models that can learn complex data distributions and generate new samples similar to the training data.

Variational Inference: A method in machine learning that approximates complex probability distributions by simpler, tractable distributions.

Virtual Reality (VR): A simulated experience that can be similar to or completely different from the real world, with implications for AI in creating virtual environments.

Visual Question Answering: A task where models generate answers to questions about images.

Viterbi Algorithm: A dynamic programming algorithm for finding the most likely sequence of hidden states in a hidden Markov model.

Voxel: A volume pixel, representing values in three-dimensional space, commonly used in medical imaging.

Wasserstein GAN (WGAN): A type of Generative Adversarial Network (GAN) that uses the Wasserstein distance to improve stability and performance of training.

Watson: IBM's AI platform best known for beating human champions on the game show "Jeopardy!".

Weight Decay: A regularization technique that adds a penalty to the loss function based on the magnitude of weights.

Weight Initialization: The method or strategy used to set the initial random weights of neural networks.

Weight Pruning: The process of removing certain weights in a neural network to reduce its size and computational cost.

Weight Regularization: Techniques used in neural networks to add a penalty on the magnitude of weights to prevent overfitting.

Weight Sharing: Using the same weight values across multiple network locations, common in convolutional neural networks.

Weight Tying: A technique where weights are shared among multiple layers or parts of a neural network, reducing the number of parameters and regularizing the model.

Weights: The parameters in neural networks adjusted through training to make accurate predictions.

Wide and Deep Learning: A neural network architecture that combines memorization and generalization, particularly useful in large-scale machine learning problems.

Word Embedding: The representation of words in continuous vector spaces such that semantically similar words are closer together.

Word2Vec: A group of related models used to produce word embeddings in NLP.

XAI (Explainable AI): A subfield of AI focused on creating methods and techniques for making machine learning models more interpretable and understandable.

Xavier Glorot Initialization: A method of weight initialization in neural networks to help propagate the signal deep into the network.

Xavier Initialization: A method of weights initialization in neural networks designed to keep the scale of gradients roughly the same in all layers.

XGBoost Regression: The use of the XGBoost algorithm for regression tasks, where the aim is to predict a continuous output variable.

XOR Problem: A non-linear problem that inspired the development of multi-layered neural networks.

Yann LeCun: A computer scientist known for his work on convolutional neural networks and deep learning.

YellowFin: An optimizer for deep learning that automatically adjusts its settings during training for improved performance.

YOLO (You Only Look Once): A real-time object detection system that can detect objects in images or video as a single regression problem.

YOLOv3: The third version of the YOLO (You Only Look Once) object detection algorithm, known for its speed and accuracy.

YOLOv4: The fourth version of the YOLO (You Only Look Once) object detection algorithm, known for its speed and accuracy enhancements.

YOLOv5: The fifth version of the YOLO (You Only Look Once) object detection algorithm, known for its speed and accuracy enhancements.

Z-normalization: A data normalization technique wherein values are rescaled to have a mean of 0 and a standard deviation of 1.

Z-Score Normalization: A normalization method where each feature is rescaled to have a mean of zero and a standard deviation of one.

Z-Score: A statistical measurement representing the number of standard deviations a data point is from the mean.

Zero Gradient Problem: A situation where the gradients are too close to zero, and the network refuses to learn further.

Zero Trust Architecture: A cybersecurity concept where no entity, whether outside or inside the organization's network, is trusted by default.

Zero-day Attack: A cyber-attack that occurs on the same day a weakness is discovered in software, before a fix becomes available from its creator.

Zero-padding: The addition of zeros to an input tensor, often an image, to control the spatial dimensions after convolution in a neural network.

Zero-shot Learning: A type of machine learning where the model is trained in such a way that it can make predictions for classes it has not seen during training.

Zero-shot Transfer: The ability of a machine learning model to perform a task without having seen any examples of that task during training.

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