AI Glossary

A/B testing , also known as split testing or bucket testing, is a statistical method of comparing two versions of a single variable to determine…

An accelerator is a specialized hardware or software component designed to enhance the performance of artificial intelligence tasks by optimizing…

Actionable intelligence refers to information that has been processed and analyzed to the point where it can be directly applied to strategic…

An activation function is a mathematical operation applied to the output of a neuron in a neural network that determines whether and to what extent…

Active learning is a specialized machine learning strategy in which the algorithm proactively queries a human annotator to label specific, selected…

Agentic AI refers to AI systems designed to pursue multi-step goals with a degree of autonomy: they perceive context, decide on a sequence of actions…

Artificial General Intelligence (AGI) is a theoretical form of artificial intelligence characterized by the ability to understand, learn, and apply…

An AI agent is a software system that uses a model, usually a large language model, to autonomously pursue a goal. It perceives its environment or…

AI ethics is the field of study that examines the moral implications of artificial intelligence systems and establishes principles to guide their…

AI guardrails are rules, checks, and constraints placed around an AI system, typically a large language model application, to keep its behavior…

An AI jailbreak is a technique or prompt designed to bypass an AI model's safety guidelines or built-in restrictions, getting it to produce content…

An algorithm is a finite sequence of well-defined instructions or rules designed to perform a specific task or solve a particular problem. In the…

Alignment is the concept of ensuring that artificial intelligence systems behave in ways that correspond to human intentions, values, and goals. It…

Anaphora is the linguistic phenomenon where a word or phrase, known as an anaphor, refers back to a previously mentioned entity, called the…

Area Under the Curve (AUC) is a performance measurement used to evaluate the predictive power of classification models, particularly in binary…

Artificial Intelligence (AI) is a branch of computer science dedicated to the creation of systems capable of performing tasks that typically require…

Artificial Neural Networks are computational models inspired by the biological neural networks of the human brain, designed to recognize complex…

Artificial Super Intelligence (ASI) is a hypothetical form of artificial intelligence that possesses cognitive capabilities surpassing those of human…

Association Rule Learning is a machine learning method used to discover relationships among a set of items within large datasets.

An attention mechanism is a component in a neural network that lets the model weigh the relevance of different parts of the input when producing each…

Auto-classification is the automated process of assigning predefined categories or labels to data items using machine learning algorithms. It enables…

Auto-complete , also known as predictive text, is a feature that provides suggestions or predicts the rest of a word or phrase that a user has…

Autoencoders are a class of artificial neural networks designed to learn efficient, compressed representations of data through unsupervised learning.…

Automated Speech Recognition , also known as Computer Speech Recognition, is a technology that converts spoken language into written text. It is a…

Back propagation is a supervised learning algorithm used to train artificial neural networks by calculating the gradient of the loss function with…

Backpropagation through time is a gradient-based learning algorithm used to train recurrent neural networks by treating the temporal sequence of…

Batch is a fundamental technique in machine learning where a subset of training examples is processed together to compute an approximate gradient for…

Bayes’s Theorem is a fundamental mathematical formula in probability theory that describes how to update the probability of a hypothesis as more…

BERT , short for Bidirectional Encoder Representations from Transformers, is a neural network architecture designed for natural language processing…

In AI, bias has two distinct meanings. Statistical bias is the systematic error between a model’s average prediction and the true value, forming one…

The bias-variance tradeoff is a fundamental concept in machine learning that describes the relationship between a model's ability to capture…

A bounding box is a rectangular region defined by coordinate pairs that localizes an object within a two-dimensional image or video frame. It serves…

Cataphora is an anticipatory referencing mechanism in linguistics where a pronoun or other referring expression points forward to a specific word,…

Category trees are hierarchical structures used to organize information by grouping concepts or items into parent categories and child subcategories.…

Causal AI is an approach to artificial intelligence that models cause-and-effect relationships between variables, aiming to answer questions about…

The Central Processing Unit (CPU) is the primary component of a computer system responsible for executing instructions and performing calculations.…

Chain of Thought is a cognitive process in artificial intelligence where a system connects various pieces of information or ideas in a logical…

A chatbot is a software application designed to simulate human conversation through text-based or voice-based interfaces. It functions as a…

ChatGPT is an advanced chatbot model developed by OpenAI that generates human-like text responses and engages in interactive conversations with…

CLIP (Contrastive Language-Image Pretraining) is a neural network created by OpenAI that learns to associate images with the text descriptions that…

Clustering is a data analysis technique used to group similar objects or data points together based on their features or characteristics. The essence…

Co-Occurrence refers to the phenomenon where two or more items, events, or concepts appear together or in close proximity more often than would be…

A cognitive map is a mental representation or framework that individuals construct to organize and navigate their understanding of the physical or…

Cold-start refers to a situation or problem that arises when a system or model lacks sufficient data or information to make accurate predictions or…

Collaborative filtering is a recommendation technique that predicts a user’s interest in an item by aggregating the preferences of other users who…

Completions is a predictive technique that forecasts missing or upcoming data points by analyzing patterns and historical information. This method…

Composite AI is an architectural approach that integrates multiple distinct artificial intelligence technologies into a single, cohesive system to…

Computational linguistics is an interdisciplinary field that combines computer science and linguistics to enable computers to analyze, understand,…

Computational semantics , also known as Semantic Technology, is a field of study that combines aspects of semantic theory, computer science, and…

Computer Vision is a specialized field of artificial intelligence that focuses on teaching machines to interpret and understand visual information…

A confidence interval is a range of values derived from sample data that is likely to contain an unknown population parameter. In the context of…

A confusion matrix is a table that compares a classification model's predicted labels with the actual labels, counting correct and incorrect…

Content Enrichment is the process of enhancing raw data with additional or relevant information to increase its value and render it more…

A contributor is an individual who actively participates in the development, refinement, or implementation of artificial intelligence systems. This…

A controlled vocabulary is a curated list of terms used to describe content within a specific domain, designed to standardize how information is…

Conversational AI is a subfield of artificial intelligence that focuses on enabling machines to engage in human-like dialogue, capturing context and…

Convolutional Neural Networks are a class of deep learning algorithms designed to process data with a known grid-like topology, such as images, by…

In the subfield of Natural Language Processing (NLP), a corpus is defined as a substantial and diverse collection of textual data. This collection…

Cross-entropy loss measures the difference between two probability distributions: the true labels and the model’s predicted probabilities. It is the…

Cross-validation is a statistical resampling procedure used in machine learning to evaluate how well a predictive model will perform on unseen data.…

A Custom/Domain Language Model is a specialized artificial intelligence system designed to understand, interpret, and generate language within a…

Data augmentation is a technique in machine learning that artificially expands the size and diversity of a training dataset by generating new,…

Data discovery is an iterative process of collecting, exploring, and analyzing data to uncover hidden patterns, relationships, and insights that…

Data drift is the phenomenon in which the statistical properties of the input data provided to a predictive model change over time, leading to a…

Data extraction is the process of retrieving data from various sources for further processing or storage. It involves gathering information from…

Data ingestion is the process of obtaining, importing, and processing data from various sources for immediate use or storage in a database. It serves…

Data labeling , also known as data annotation, is the process of adding informative tags or labels to datasets to make raw data understandable and…

Data scarcity is the condition in which the volume or quality of available information is insufficient to train machine learning models effectively…

A decision tree is a predictive modeling technique used in machine learning, data mining, and statistics that represents decisions and their possible…

Deep Blue is a specialized artificial intelligence system designed to play the game of chess at a grandmaster level. It achieved global recognition…

Deep Learning is a subfield of machine learning that utilizes artificial neural networks with multiple layers to process data, identify patterns, and…

A deepfake is synthetic media (video, image or audio) in which a real person's face, voice or body is generated or altered with deep learning so that…

Did You Mean is a feature in search engines and information retrieval systems that suggests alternative queries to correct user errors. It activates…

Diffusion is a term with two distinct meanings in artificial intelligence. In the context of generative modeling, it describes a class of algorithms…

Disambiguation is the process of clarifying the meaning of words, phrases, or visual elements that can be interpreted in multiple ways. It involves…

Domain knowledge is the specialized understanding, insights, and contextual awareness that experts possess within a specific field of study or…

Edge modeling is the practice of deploying machine learning models directly onto edge devices, which are hardware components that control data flow…

Embedding is a technique in machine learning and natural language processing that represents categorical data, such as words, phrases, or entities,…

Emergence behavior is a phenomenon in which a complex system exhibits properties, capabilities, or patterns that are not present in its individual…

Emotion AI , also known as Affective Computing, is a branch of artificial intelligence that aims to simulate, comprehend, and respond to human…

End-to-end learning is a machine learning approach in which a model maps raw input data directly to the desired output, eliminating the need for…

Ensemble methods are a machine learning paradigm that combines the predictions of multiple individual models to produce a single, more robust final…

An entity is a distinct, well-defined unit of information that represents a real-world object, concept, or instance within a dataset. In artificial…

Entropy is a fundamental concept borrowed from information theory that quantifies the impurity, disorder, or uncertainty within a set of data.

Environmental, Social, and Governance (ESG) is a framework for evaluating an organization’s performance across three distinct dimensions: its impact…

An epoch is a unit of measurement in machine learning that denotes one complete pass through the entire training dataset during the training of a…

Entity Recognition and Extraction is a natural language processing technique that identifies specific, meaningful chunks of text within a larger body…

An expert system is an artificial intelligence program that uses a large collection of hand-coded "if-then" rules, written by human experts in a…

Explainable AI (XAI) is a set of methods and techniques designed to make the outputs and internal decision-making processes of artificial…

Keyphrase Extraction is a process in the field of Natural Language Processing that involves the automated identification and selection of significant…

The F-score , also known as the F1 score or F-measure, is a statistical metric used to evaluate the performance of binary classification systems. It…

Facial recognition is a biometric technology that identifies or verifies a person by analyzing and comparing patterns in their facial features, using…

A false negative is a case where a classification model or test predicts the negative outcome (for example, "no disease," "not spam," "not fraud")…

A false positive is a classification error in which a model or test incorrectly identifies a negative instance as positive. In binary classification,…

Feature engineering is the process of using domain knowledge to create, select, and transform the input variables (features) fed into a machine…

Feature learning is a machine learning approach in which a system automatically discovers the representations or features required for data analysis…

Federated learning is a distributed machine learning approach in which a shared model is trained across many devices or servers that each hold their…

A feed-forward neural network is a class of artificial neural network in which information moves unidirectionally from the input layer, through any…

Few-shot learning is a machine learning paradigm designed to enable models to acquire new concepts and make accurate predictions using only a small…

Fine-tuning is a machine learning technique in which a pre-trained model is further trained on a specific, smaller dataset to adapt its learned…

Forward propagation is a fundamental process in neural network modeling that describes the flow of information from the input layer to the output…

A foundation model is a large-scale machine learning model trained on a massive, diverse corpus of data, typically sourced from the internet, to…

Garbage in, garbage out (GIGO) is a fundamental principle in computer science and information technology stating that the quality of output is…

Generative Adversarial Networks (GANs) are a class of machine learning models designed to generate new data instances that resemble those from an…

The General Data Protection Regulation (GDPR) is a European Union law that sets rules for how organizations collect, store, and process the personal…

Generative AI , or GenAI, refers to artificial intelligence technologies and algorithms capable of generating new, original content such as images,…

Genetic algorithms are a class of optimization techniques inspired by the principles of natural selection and biological evolution. They are employed…

Generative Pretrained Transformer (GPT) is a class of large language models that generate human-like text by predicting the next token in a sequence…

A GPU (graphics processing unit) is a processor originally designed to render computer graphics. It is built around thousands of small cores that can…

Gradient descent is an iterative optimization algorithm that finds the minimum of a function by repeatedly adjusting its parameters in the direction…

Ground truth in artificial intelligence and machine learning denotes the absolute or "true" information about the data within a model. As a benchmark…

Grounding is the process of linking abstract, symbolic representations in artificial intelligence to sensory or perceptual data from the real world.…

Hallucinate/Hallucination describes the phenomenon where an artificial intelligence system generates information, perceptions, or sensory experiences…

A hidden layer is an intermediate processing stage within an artificial neural network, positioned between the input layer that receives raw data and…

Human-in-the-Loop is a collaborative framework in artificial intelligence where human intelligence and machine learning algorithms work together…

Hybrid AI is a computational approach that integrates machine learning, which excels at pattern recognition, with traditional symbolic reasoning or…

A hyperparameter is a configuration variable external to a machine learning model that cannot be learned directly from the training data. These…

Hyperparameters are configuration settings that define the structure and control the behavior of a learning algorithm before the training process…

Image recognition is the computational process by which artificial intelligence systems identify, classify, and interpret the content of visual data.…

Image segmentation is a computer vision task that assigns a label to every pixel in an image, dividing it into regions that correspond to different…

ImageNet is a large-scale visual database designed for use in visual object recognition research. It consists of hundreds of thousands of labeled…

Inference is the process of applying a trained machine-learning model to new, unseen data in order to make predictions or draw conclusions. It is the…

An inference engine is a core component of expert systems in artificial intelligence that applies reasoning capabilities to a knowledge base to…

Information retrieval is a discipline within computer science focused on the efficient organization, storage, and retrieval of information from…

An insight engine , also referred to as a cognitive search engine, is an information retrieval system that uses artificial intelligence to analyze…

Instruction Tuning is a technique used in high-performance computing to enhance the efficiency and speed of computation by optimizing the ordering…

Intelligent Document Processing (IDP) is an advanced technology that utilizes artificial intelligence and machine learning to recognize, classify,…

K-means clustering is an unsupervised machine learning algorithm that divides a dataset into k groups. Each point belongs to the cluster whose…

Knowledge distillation is a machine learning technique where a smaller "student" model is trained to reproduce the behavior of a larger,…

A Knowledge Graph is a structured representation of knowledge that captures relationships between entities, concepts, and facts in a graph-like…

A Knowledge Model is a structured representation of information, insights, and relationships extracted from data sources. It encompasses a system's…

The KV cache (key-value cache) is a store of the key and value vectors, computed by a transformer's self-attention layers for every token generated…

Labelled data is a dataset in which each data point is associated with a specific and well-defined label or category. These labels serve as…

LangOps , or Language Operations, is a specialized operational discipline that manages the end-to-end lifecycle of language-centric artificial…

Language Data is textual or spoken information that serves as the raw material for training, fine-tuning, and evaluating language-related artificial…

A large language model (LLM) is a neural network, almost always built on the transformer architecture, trained on very large amounts of text to…

Latent space is a mathematical representation where complex, high-dimensional data is mapped into a lower-dimensional form. This transformation…

A layer is a collection of interconnected nodes or neurons that process input data or the output from a preceding layer within a neural network. A…

The learning rate is a hyperparameter that controls the magnitude of weight updates during the optimization of a machine learning model. It…

Learning to rank is a machine learning approach for training a model to order a list of items, such as search results or product recommendations, by…

Learning-to-learn , also known as meta-learning, is a machine learning paradigm in which a model is trained on a distribution of tasks rather than a…

A lemma is a fundamental building block used to simplify complex structures, though its specific meaning depends on the field. In mathematics, it is…

A lexicon is a structured collection of words, phrases, and other language elements, accompanied by metadata that describes their semantic, phonetic,…

Linked Data is a method of publishing structured data on the Internet so that it can be interlinked and become more useful. It extends the…

The logit function is a mathematical transformation that maps a probability value from the interval (0, 1) to the entire real number line,…

Long Short-Term Memory Networks (LSTM) are a specialized architecture of recurrent neural networks designed to overcome the limitations of standard…

LoRA (low-rank adaptation) is a parameter-efficient fine-tuning technique that adapts a pretrained model to a new task by training a small number of…

A loss function , also known as a cost function, is a mathematical formula used to quantify the discrepancy between a model's predictions and the…

Machine learning is a branch of artificial intelligence that enables computer systems to improve their performance on a specific task through…

Machine Translation is the automated process of converting text or speech from a source language into a target language using computational…

Metadata is descriptive information that provides context, attributes, and characteristics about a piece of data. It serves as a structural layer…

Mixture of experts (MoE) is a neural network design where, instead of one large network processing every input, the network is split into many…

The Model Context Protocol (MCP) is an open standard that defines how AI applications, such as assistants, coding tools and agents built on large…

Model drift , also known as concept drift, refers to the change in data patterns over time that results in the degradation of a predictive model's…

A model parameter is an internal configuration variable of a machine learning model whose value is estimated from data during the training process.…

The Monte Carlo method is any technique that uses repeated random sampling to estimate a numerical result. It is used when the exact answer would be…

Morphological analysis is a fundamental process in natural language processing and computational linguistics that decomposes words into their…

Multi-modal learning is a subfield of machine learning that builds models capable of processing and relating information from multiple distinct data…

Multi-task learning is a machine learning approach in which a single model is trained to perform multiple related prediction problems simultaneously.…

Multimodal describes systems or models designed to receive, process, and analyze multiple types of data, known as "modes," simultaneously. These…

Naive Bayes is a probabilistic classification technique rooted in Bayesian probability theory that estimates the likelihood of a data point belonging…

Named Entity Recognition is a natural language processing technique that identifies and classifies specific named entities within unstructured text…

Natural language generation (NLG) is the subfield of AI and natural language processing concerned with producing human-readable text. This output can…

Natural Language Processing (NLP) is a multidisciplinary field that focuses on enabling computers to understand, interpret, and generate human…

Natural Language Understanding (NLU) is a branch of artificial intelligence that focuses on the interaction between computers and human languages,…

NeRF , or Neural Radiance Fields, is a deep learning technique for representing and rendering three-dimensional scenes from two-dimensional images.…

Neural networks , also known as artificial neural networks (ANNs), are computational models inspired by the biological structure of the human brain.…

A neuron , also referred to as a node, is the fundamental computational unit within an artificial neural network. It serves as the basic building…

Natural Language Query (NLQ) is a subset of Natural Language Processing that enables users to interact with databases or information repositories…

Natural Language Technology (NLT) is a broad term encompassing all technologies designed to handle and interact with human language. It forms the…

Object detection is a computer vision task that identifies and locates objects within an image, typically outputting a bounding box around each…

An Objective Function is a mathematical formula that quantifies the performance or effectiveness of a model by measuring the discrepancy between its…

An ontology is a formal representation of knowledge that defines the concepts, entities, relationships, and properties within a specific domain. It…

Open weights describes a model whose trained parameters (weights) are published and can be downloaded and run by anyone, as distinguished from a…

Optical Character Recognition is a technology that enables computers to convert images containing printed or handwritten text into machine-readable…

Overfitting is when a machine learning model learns the training data too closely, including its noise and random fluctuations, so it performs well…

Parsing is the computational process of analyzing a string of symbols, such as a sentence or code, to determine its grammatical structure and…

Part-of-Speech (POS) tagging is the computational process of assigning a specific grammatical category, or "tag," to each word in a text based on its…

Pattern recognition is a branch of artificial intelligence focused on the identification and classification of structures within data. It involves…

Post Edit Machine Translation , commonly referred to as PEMT, is a hybrid translation workflow that combines the speed of automated machine…

Personally Identifiable Information (PII) is any data that can be used to distinguish or trace the identity of a specific individual. This category…

Pooling , specifically Max Pooling , is a downsampling operation used within Convolutional Neural Networks to reduce the spatial dimensions of…

Post-processing refers to any operations or adjustments applied to data, images, or materials after the initial phase of production or recording. It…

Pre-processing is a preliminary stage in any production or analysis pipeline, where initial input is prepared or conditioned to optimize it for the…

A pre-trained model is a machine learning or artificial intelligence model that has already been trained on a large benchmark dataset, rather than…

Pre-training is the initial stage of training a model, usually on a large, general, often unlabeled dataset with a self-supervised objective, before…

Precision measures the proportion of positive identifications that were actually correct, while recall measures the proportion of actual positives…

Preprocessing is the systematic transformation of raw data into a standardized, clean, and structured format suitable for analysis by machine…

Principal Component Analysis is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly…

A prompt is a specific instruction, query, or input cue provided to an artificial intelligence system, such as a language model, to guide its…

Prompt engineering is the strategic design and formulation of input cues, known as prompts, to guide the behavior and output of language models or…

Prompt injection is an attack on applications built with large language models in which instructions hidden in input text override or subvert the…

Quantization is a model compression technique that reduces the numeric precision used to represent a model's weights and sometimes its activations,…

Random Forest is an ensemble machine learning algorithm that constructs a collection of decision trees during training and outputs the mode of the…

A reasoning model is a large language model specifically trained to produce an extended chain of intermediate reasoning steps before giving a final…

Recall is a performance metric used to evaluate classification models, measuring the proportion of actual positive instances that are correctly…

The Rectified Linear Unit is a mathematical activation function used in neural networks to introduce non-linearity into the model's decision-making…

Recurrent Neural Networks (RNN) are a class of artificial neural networks designed to process sequences of data by maintaining an internal memory of…

Regression is a fundamental statistical and machine learning technique used to model the relationship between input features and a target variable.…

A Regressor is a machine-learning model designed to predict continuous numerical values by learning the relationship between input features and a…

Regularization is a technique used in machine learning to prevent overfitting by adding constraints or penalties to the training process. It works by…

Reinforcement Learning is a machine learning paradigm in which an agent learns to make sequential decisions by interacting with an environment to…

Responsible AI refers to the ethical, transparent, and accountable development and deployment of artificial intelligence technologies. It encompasses…

Restricted Boltzmann Machines are a class of generative artificial neural networks that learn a probability distribution over their input data. They…

Retrieval-augmented generation (RAG) is a technique that grounds a large language model's response in external information. Before generating an…

Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that aligns an AI agent’s behavior with human preferences by…

Rules-based Machine Translation (RBMT) is an early approach to machine translation that relies on predefined linguistic rules and structures to…

SAO (Subject-Action-Object) is a fundamental linguistic structure that represents the core elements of a sentence by breaking down linguistic content…

Self-supervised learning is a training approach in which a model learns from unlabeled data by generating its own supervisory signal from the…

A Semantic Network is a structured representation of knowledge that models relationships between concepts using interconnected nodes and edges.

Semantic Search is an information retrieval approach that interprets the meaning, context, and intent of a user’s query rather than relying solely on…

Semi-structured data is a form of information that lacks the rigid, fixed schema of traditional relational databases but retains a degree of…

Semi-supervised learning is a machine learning approach that utilizes a small amount of labeled data alongside a large volume of unlabeled data to…

Sentiment analysis is the computational process of evaluating and discerning the emotional tone, opinion, or attitude expressed within text data. It…

Similarity (and Correlation) are fundamental mathematical concepts used to quantify relationships within data. Similarity measures the degree of…

The Simple Knowledge Organization System (SKOS) is a standardized framework designed to represent and structure knowledge concepts for use in…

Singularity is a hypothetical future point in time when technological progress, particularly in artificial intelligence, reaches a level where…

A small language model (SLM) is a language model with a comparatively small number of parameters, built to run efficiently on limited hardware such…

The softmax function turns a vector of real numbers (logits) into a probability distribution: every output is between 0 and 1 and all outputs add up…

Speculative decoding is an inference-time technique that accelerates text generation from a large language model by having a smaller, faster draft…

Speech analytics is the process of analyzing spoken language to extract valuable insights and information from audio data. It involves using natural…

Speech recognition , also known as automatic speech recognition (ASR), is the technology that enables computers to convert spoken language into text.…

A statistical distribution is a mathematical function that describes the likelihood of different outcomes occurring within a set of data or a random…

Structured data is a form of information organization where content is arranged according to a predefined schema or model, typically following a…

Supervised learning is a fundamental machine learning paradigm in which a model learns to map input data to specific output labels using a dataset of…

A support vector machine (SVM) is a supervised learning algorithm, most commonly used for classification, that works by finding the hyperplane…

Symbolic Artificial Intelligence is a classical approach to artificial intelligence that simulates human intelligence by manipulating symbols and…

Symbolic methodology is an approach to artificial intelligence that centers on the manipulation of symbols, formal logic, and structured knowledge…

Syntax refers to the grammatical structure and arrangement of words, symbols, or elements within a language or formal system. It dictates the rules…

Synthetic data is artificially generated data, created by a computer rather than collected from real-world events, designed to have statistical…

Tagging is the process of assigning descriptive labels or metadata to data points such as text, images, audio, or video. It serves as a fundamental…

A taxonomy is a hierarchical framework used to systematically categorize and organize concepts, objects, or data based on their inherent…

Temperature is a parameter that controls the randomness of a language model's output by scaling the raw prediction scores (logits) before they are…

TensorFlow is an open-source software library designed for the construction and training of machine learning models. It operates by representing data…

A test set is a distinct subset of data held out from the training process to provide an unbiased evaluation of a machine learning model’s…

Testing , often referred to as the test set or testing data, is a distinct subset of data used to evaluate the performance of a trained machine…

Text analytics is a computational discipline that applies artificial intelligence and natural language processing techniques to process, analyze, and…

Text Summarization is the automatic condensation of longer texts into shorter, coherent versions while retaining the key information and main ideas.…

A thesaurus is a structured database or list of words and phrases organized by their semantic relationships, such as synonyms, antonyms, and…

A time series is a sequence of data points collected at regular intervals over time, characterized by the temporal ordering of each observation. This…

Tokenization is the process of splitting text into smaller units, called tokens, that a language model processes as its basic units of input and…

A token is the basic unit of text that a language model actually processes. It is a chunk of text produced by a tokenizer that may represent a whole…

Topic modeling is an unsupervised machine learning technique used to discover the abstract "topics" that occur in a collection of documents. It…

A Tensor Processing Unit (TPU) is a specialized hardware accelerator designed to execute machine learning workloads with high efficiency,…

Training data is a fundamental component of machine learning that consists of labeled examples used to teach models to recognize patterns, make…

A training set is a collection of data examples used to teach a machine-learning model how to perform a specific task or make predictions. It…

Transfer learning is a machine learning technique in which a model developed for one task serves as the starting point for a model on a second,…

The Transformer is a deep learning architecture designed to process sequential data by utilizing self-attention mechanisms to weigh the significance…

A treemap is a data visualization technique that displays hierarchical data structures by partitioning a rectangular area into nested rectangles.…

Triple or Triplet Relations , also known as Subject-Action-Object (SAO) or Subject-Operation-Target (SOT) connections, are a structured format for…

Tuning , also known as model tuning or fine-tuning, is a critical process in artificial intelligence and machine learning that involves optimizing…

The Turing Test is a behavioral benchmark used to assess whether a machine can exhibit intelligent behavior indistinguishable from that of a human.…

Type I error , also known as a false positive, is a statistical concept describing the incorrect rejection of a true null hypothesis. In the context…

A Type II error , also known as a false negative, is a statistical error that occurs when a hypothesis test or classification model fails to reject a…

Uncertainty in artificial intelligence describes the state of incomplete knowledge or confidence regarding the outcomes and predictions generated by…

Underfitting is a condition in machine learning where a model fails to adequately capture the underlying patterns and relationships within the…

Unstructured data is information that lacks a predefined or organized format, making it challenging for traditional algorithms to interpret and…

Unsupervised learning is a paradigm of machine learning where the emphasis is on discovering patterns, structures, or relationships within data…

Validation data is a subset of labeled data that is distinct from the training data and is used to assess the performance and generalization…

Vanishing and exploding gradients are optimization issues that arise during the training of deep neural networks when the gradients used to update…

A vector database is a database designed to store and efficiently search high-dimensional vectors, called embeddings, using similarity search rather…

Vibe coding is a software development style where a person describes desired functionality in plain natural language, and an AI coding assistant…

A world model is a model that learns an internal representation of how an environment works, including how it changes over time and in response to…

Explainable AI (XAI) is the practice of designing and developing artificial intelligence models and systems in a way that their decision-making…

XGBoost (eXtreme Gradient Boosting) is an open-source machine learning library that implements gradient-boosted decision trees. It is used for…

Zero-shot learning is a machine learning paradigm in which a model performs tasks or recognizes classes for which it has never observed labeled…