A/B Testing, also known as split testing or bucket testing, is a widely used practice in digital marketing, user experience design, and product development, aimed at improving and optimizing a product, feature, or marketing campaign. It involves conducting a controlled...

In the context of AI, an accelerator refers to specialized hardware or software components designed to enhance the performance of AI tasks. These accelerators are optimized to handle the computationally intensive operations that are common in AI workloads, such as...

Actionable intelligence refers to information that can be directly applied to strategic decision-making processes and activities, typically within an operational or business context. This form of intelligence provides valuable insights that drive logical, data-driven decisions and stimulate effective actions. Applying...

Activation function is a crucial component that takes the input in the form of a weighted sum and bias, transforms it, and produces an output that is used for predictions or to feed the next layer. By introducing non-linearity into...

Active learning in the context of artificial intelligence (AI) refers to a specialized form of machine learning, where the algorithm proactively queries the user (often a human annotator) to provide outputs for selected inputs to gather new data. In contrast...

Artificial General Intelligence (AGI) refers to a type of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks and domains, much like human intelligence. Unlike specialized AI systems that are designed...

AI ethics encompasses a set of ethical principles, guidelines, and reflections that dictate the way artificial intelligence (AI) systems are developed, deployed, and utilized. It involves critically examining the ethical implications of AI technology and ensuring that AI systems are...

In AI, an algorithm is a step-by-step procedure or set of rules that guides the AI system in performing a specific task or solving a particular problem. Algorithms in AI are designed to process input data and generate output or...

Alignment  refers to the concept of making sure that AI systems act in a way that aligns with human intentions, values, and goals. It is about designing, training, and deploying AI systems in a way that their behavior and decisions...

Anaphora, in the context of artificial intelligence (AI), particularly Natural Language Processing (NLP), is a concept related to the interpretation of pronouns, possessive determiners, and other referential expressions in a text. The aim is to determine the noun or phrase...

Area Under the Curve (AUC), in the context of artificial intelligence and machine learning, is a performance measurement that is often used for classification problems. More specifically, it is commonly used with Receiver Operating Characteristic (ROC) curves. The ROC curve...

Artificial Intelligence (AI) is a field of computer science that focuses on creating intelligent machines and systems that can perform tasks typically requiring human intelligence. It involves developing algorithms and models that enable computers to learn from data, adapt to...

Artificial Neural Networks (ANN) are a class of computational models inspired by the structure and functioning of biological neural networks in the human brain. They form the backbone of modern machine learning and are used in various AI applications.  ...

Artificial Super Intelligence (ASI) refers to a type of artificial intelligence that surpasses human beings in practically every field of interest - be it professional skills, general knowledge, creative abilities, or social capabilities. It is the point where AI no...

Association Rule Learning is a machine learning method that is used to find or associations among a set of items in large datasets. This technique is often used in market basket analysis, where the task is to find associations between...

Auto-classification in AI refers to the use of machine learning algorithms to categorize a dataset automatically, based on predefined criteria or features. The objective is to help systems understand, sort, and manage data without requiring human intervention. Auto-classification systems can...

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 started typing. This function, embedded in numerous systems and devices such as search engines, messaging...

Autoencoders are a type of artificial neural network used for learning efficient representations of data, typically for the purpose of dimensionality reduction or denoising. These networks are unsupervised learning models that rely on the principle of data encoding and decoding....

Automated Speech Recognition (ASR), also known as Computer Speech Recognition, is a technology that converts spoken language into written text. It's a cross-disciplinary area within computer science and computational linguistics that focuses on creating strategies and technologies that allow computers...

Backpropagation is an essential concept in machine learning and neural networks. It is a learning algorithm used to train neural networks by adjusting the weights and biases of the network based on the error of the predicted output compared to...

Backpropagation through time (BPTT) is a variant of the backpropagation algorithm specifically designed for training recurrent neural networks (RNNs) and architectures that involve temporal data. While standard backpropagation is used to update the weights and biases of the network, BPTT...

In the context of machine learning and neural networks, "batch" refers to a technique used during training where multiple training examples are processed together before updating the weights and biases of the network. Instead of updating the network's parameters after...

Bayes's Theorem is a fundamental concept in probability theory and statistics. It allows us to update our belief about the probability of an event occurring based on new evidence or information. The theorem is named after Thomas Bayes, an 18th-century...

BERT, short for Bidirectional Encoder Representations from Transformers, is a state-of-the-art natural language processing (NLP) model introduced by Google in 2018. It is designed to understand and generate human-like text by leveraging the power of transformers, a type of neural...

Bias refers to the presence of systematic errors or prejudices in the data, algorithms, or decision-making processes that can lead to unfair or inaccurate outcomes. These biases may arise from various sources, such as the data used to train AI...

The-variance tradeoff is a fundamental concept in machine learning that refers to the relationship between the bias and variance of a model and its overall predictive performance. It highlights the tradeoff between the model's ability to accurately capture the underlying...

A Bounding Box, in the context of computer vision and image processing, refers to a rectangular box that can be digitally implemented to recognize, localize, and distinguish objects found in an image or video. This box encompasses the object within...

 Cataphora refers to an anticipatory referencing mechanism where a pronoun or a word refers forward to another word, phrase or clause in a sentence. This concept is crucial for understanding discourse, as it enables entities or concepts to be referred...

Category trees, also known as hierarchical classification or taxonomic classification, refer to a method of organizing and categorizing information in a hierarchical structure. The essence of category trees lies in their ability to provide a systematic and hierarchical representation of...

The Central Processing Unit (CPU) is a critical component of a computer system responsible for executing instructions and performing calculations. It is often referred to as the "brain" of the computer, as it processes and controls the majority of the...

In the context of AI, "Chain of Thought" refers to a cognitive process where an AI system connects various pieces of information or ideas in a logical sequence to arrive at a conclusion or make informed decisions. This concept draws...

A chatbot, short for chat robot, is an application or software program designed to interact with human users through conversational interfaces, typically text. The essence of a chatbot lies in its ability to simulate intelligent conversation by understanding natural language...

ChatGPT is an advanced chatbot model developed by OpenAI. The essence of ChatGPT lies in its ability to generate human-like text responses and engage in interactive conversations with users. It is built upon the GPT (Generative Pre-trained Transformer) architecture, which...

CLIP, which stands for Contrastive Language–Image Pretraining, is a cutting-edge model developed by OpenAI. The essence of CLIP lies in its ability to enable cross-modal understanding between images and text Unlike traditional models that focus on image or text separately,...

Clustering  is a data analysis technique used to group similar objects or data points together based on their features or characteristics. The essence of clustering lies in its ability to discover inherent patterns and structures in data and organize them...

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 expected by chance alone. The essence of co-occurrence lies in its ability to reveal relationships...

A cognitive map refers to a mental representation or framework that individuals construct to organize and navigate their understanding of the physical or abstract world. The essence of a cognitive map lies in its ability to capture an individual's internalized...

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 recommendations for new or unseen instances. The essence of the cold-start problem lies in the difficulty...

Collaborative filtering is a method used by recommendation systems, particularly in the field of online shopping, streaming platforms, and social media. Its fundamental premise is based on the idea that users who agreed in the past are likely to agree...

Completions is a technique utilized to predict the missing or upcoming data based on the patterns or historical data available. This method of predictions provides significant information and aids in optimal decision-making processes in various fields, including natural language processing,...

Composite AI refers to a combination of different AI models working together to deliver more complex and advanced decision-making capabilities. Unlike a single-task AI that performs one task independently, composite AI assembles various AI technologies into one holistic system. This...

Computational linguistics is a field at the intersection of computer science and linguistics, where specialised algorithms and software are used to analyse natural human language. The goal is to enable computers to understand, interpret, and generate text in a way...

Computational semantics, also known as Semantic Technology, is a field of study that combines aspects of semantic theory, computer science, and artificial intelligence to develop automated systems that understand, interpret, and generate human language. More simply put, it's about creating...

Computer Vision in AI is a specialized field that focuses on teaching machines to interpret and understand visual information from the world, much like how humans perceive and process visual stimuli. It involves developing algorithms and models that enable computers...

A confidence interval refers to a range of values which is likely to contain an unknown population parameter. In the context of AI, it is often used to represent the uncertainty around the prediction made by a model.   The...

Content Enrichment refers to the process of enhancing raw data with additional or relevant information to increase its value and render it more comprehensible, useful, and relevant for users or systems. Much of the data or content we deal with...

Contributors include the researchers who develop novel AI algorithms, engineers who implement these algorithms and build AI models, and project managers who oversee AI project implementation. These contributors play a crucial role in pushing the boundaries of what is possible...

 Controlled vocabulary emerges as a tool for managing lexical variations and semantic similarities, which in turn, foster efficient data organization, categorization, and information retrieval.   Controlled vocabulary in AI is used to streamline and simplify machine learning and Natural Language...

Conversational AI is a subfield of artificial intelligence that focuses on enabling machines to engage in human-like dialogue, capturing context and providing appropriate responses. It's the driving force behind the likes of virtual personal assistants, messaging apps, speech-based assistants and...

Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that have revolutionized the field of computer vision. These networks are inspired by the structure and functioning of the visual cortex in animals, which is known to be responsible...

In the subfield of Natural Language Processing (NLP), a corpus specifically refers to a substantial and diverse collection of textual data. This collection is utilized to train, validate, and test AI models so they learn and understand human language parameters...

Cross-validation is a crucial statistical method used in machine learning to understand how well a predictive model will perform on unseen data, the kind of data the model was not trained on. This technique is primarily used to assess the...

A Custom, or Domain Language Model is specialized language model that is tailored specifically to understand, interpret, and generate language in a particular area of expertise or industry. These models are trained on a specific corpus of text that is...

Data augmentation is a powerful strategy used in machine learning and data science that increases the size and diversity of data sets. It involves creating new and modified versions of the existing data, which helps to prevent overfitting, improve model...

Data Discovery refers to the process of collecting, exploring, and analyzing data to draw insights and take well-informed decisions. It's a business user-oriented process that encompasses various aspects of data analytics, including data preparation, visual analysis, and guided advanced analytics....

Data Drift refers to the phenomenon where the statistical properties of the input data to a predictive model change over time, causing model performance to degrade. It is a common challenge in machine learning models, specifically those deployed in real-world...

Data extraction refers to the process of retrieving data from various sources for further data processing or data storage. The data gathered could be in an unstructured format such as websites, PDFs, emails, images or videos, or in a structured...

Data ingestion refers to the process of obtaining, importing, and processing data for immediate use or storage in a database. The data can be in the form of streams of real-time data or batches of data collected over varying time...

Data labeling, also known as data annotation, refers to the process of adding informative tags or labels to datasets. These labels, which could be any meaningful information related to the data points, aid in making the raw data understandable and...

Data scarcity refers to the situation whereby there is a lack of sufficient data to make informed decisions or to build accurate machine learning models. It's a common challenge in many fields, including healthcare, environmental conservation, and any other fields...

A decision tree is a key tool used in machine learning, data mining, and statistics for predictive modeling. The concept, as the name suggests, looks just like a tree turned upside down. The structure comprises a root, branches, and leaves....

Deep Blue is a piece of historic technology developed by IBM, which was one of the earliest and most famous artificial intelligence systems. Created in the early 1990s, Deep Blue was a chess-playing computer that used brute force and strategic...

Deep Learning is a subfield of machine learning that focuses on algorithms inspired by the structure and function of the brain called artificial neural networks. It's capable of processing large volumes of data to identify patterns and make predictions. Deep...

Did You Mean is a feature commonly found in search engines and information retrieval systems. Its main goal is to help users correct possible errors in their search queries and find the most relevant results. When a user types in...

Diffusion in the context of artificial intelligence (AI) can take multiple forms. One common usage is in the realm of diffusion models, which are a class of generative models used in machine learning. These models create new data instances that...

Disambiguation is process of clarifying the meaning of words or phrases that can be interpreted in multiple ways. These words or phrases, known as ambiguities, hold different interpretations depending on the context in which they are used. The task of...

Domain knowledge refers to the understanding and awareness of a specific area of interest or field of study. It includes specialized insights, understandings, and information that experts have about a specific subject. This knowledge is usually obtained over a considerable...

Edge modeling, in the realm of computer science and artificial intelligence, refers to the process of deploying machine learning models on edge devices. Edge devices are any pieces of hardware that control data flow at the boundary between two networks....

In machine learning and natural language processing (NLP), embedding refers to the representation of categorical data, such as words or phrases, in a high-dimensional space where similar entities are closer together. These representations are in the form of dense vectors...

Emergence or emergent behavior is a concept that encapsulates the idea of complex results arising from simple rules or interactions within an AI system. It refers to a phenomenon where the collective system exhibits behaviors or characteristics that are more...

Emotion AI, also known as Affective Computing, is a branch of artificial intelligence that aims to simulate, comprehend, and respond to human emotions. It involves developing systems and devices that are capable of recognizing, interpreting, processing, and simulating human emotions....

End-to-end learning can be described as a type of machine learning model which doesn't demand manual feature engineering. In conventional machine learning, the process usually involves separate stages where one first extracts features from the raw data and then uses...

Ensemble methods are a type of machine learning paradigm that involves aggregating the predictions of several models to generate a final prediction. The underlying concept here is the combination of several weak learning models in order to create a stronger...

Entity often refers to important pieces of information in text that represent real-world objects, such as people, places, organizations, dates, etc. These are referred to as named entities, and identifying these in a text is known as named entity recognition...

Entropy in artificial intelligence, particularly in machine learning, is a concept borrowed from information theory that measures the impurity, disorder, or uncertainty within a set of data. Higher entropy values correspond to higher levels of unpredictability or randomness within the...

Environmental, Social, and Governance (ESG) in the context of Artificial Intelligence (AI) pertains to the application of AI to support or enhance sustainable practices, ethical conduct, and sound governance in businesses and organizations. ESG factors are essential components in assessing...

Epoch is a term used to denote one complete pass through the entire training dataset while training a learning algorithm. This is specifically relevant in context of neural networks and other iterative learning algorithms. The term epoch is used in...

Entity Recognition and Extraction, related to artificial intelligence (AI) and specifically natural language processing (NLP), is a method used to identify and classify key information in text data. It seems there might be a mix-up since ETL traditionally stands for...

Expert Systems a branch of artificial intelligence (AI), are computer systems that emulate the decision-making ability of a human expert in a particular domain. They are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as...

Explainable AI (XAI) refers to the methods and techniques used in the application of artificial intelligence technology that yield results which can be easily understood by humans. The goal of explainable AI is to create a system that can clearly...

Keyphrase Extraction is a process in the field of Natural Language Processing (NLP) that involves automated extraction and identification of key phrases or keywords from a large amount of unstructured text data. These key phrases ideally represent the main theme...

The F-score, also known as F1 score or F-measure, is a statistical metric widely used in machine learning and data analysis. It's a measure that helps us evaluate the performance of a binary classification system (a system that categorizes data...

False Negative, a term often used in Statistics and Machine Learning, refers to a specific kind of error that occurs during a binary classification test. In a binary classification scenario, the aim is to sort instances into one of two...

A False Positive, in the field of statistics and machine learning, refers to a type of error that occurs during a binary classification test. In a binary classification, data or test results are categorized into one of two groups: positive...

Feature Learning  is a technique used in machine learning where the system automatically discovers the representations or features needed for data analysis, such as classification or predictions, directly from the raw data inputs. This form of learning allows machine learning...

Feed-forward is a type of artificial neural network where data moves from the input layer to the output layer in a single direction and never goes back. It is among the simplest types of neural networks in terms of the...

Few-shot learning is a concept in machine learning designed to mimic human cognitive abilities by learning new concepts and making accurate predictions from a small number of examples - hence the term "few-shot". Normal machine learning models usually require large...

Fine-tuning refers to the process of tweaking a pre-trained model for a specific task. It is a form of transfer learning, where a model developed for one task is repurposed on a second related task. It leverages the learnings of...

Forward propagation is a key process in neural network modeling that involves the flow of information from the input layer to the output layer. It reveals the essence of the term by outlining how the neural network makes predictions or...

Foundation Models in machine learning refers to models that are trained on a large volume of data from the internet, and can be further fine-tuned for specific tasks. These models absorb a broad understanding of the world and can be...

Garbage in, garbage out  (GIGO) is a widely-used phrase in the field of computer science and information and communication technologies to imply that the quality of output is determined by the quality of the input. If incorrect or poor quality...

Generative Adversarial Networks (GANs) are a class of machine learning models initially introduced by Ian Goodfellow and his collaborators in 2014. These models aim at generating new data instances that could have been drawn from the original dataset, hence the...

The General Data Protection Regulation (GDPR) is a legal structure established by the European Union (EU) to guide the accumulation, processing, and preservation of personal data from EU residents. Enforced in May 2018, the GDPR, which supersedes the 1995 EU...

Generative AI, or GenAI, refers to artificial intelligence technologies and algorithms that can generate new, original content, such as images, texts, music, or even videos. It is a branch of AI that focuses on creating models capable of producing creative...

In realm of artificial intelligence, genetic algorithms (GAs) are a type of optimization technique inspired by the principles of natural selection and evolution. GAs are employed to solve complex problems where traditional heuristic or deterministic algorithms may struggle. The essence...

Generative Pretrained Transformer (GPT) is a state-of-the-art language model developed by OpenAI. It represents a significant advancement in natural language processing (NLP) and text generation. The essence of GPT lies in its ability to understand and generate human-like text by...

A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the rendering of images and videos in a frame buffer intended for output to a display device. It's a critical component...

Ground truth in artificial intelligence (AI) and machine learning denotes the absolute or "true" information about the data in a model. As a benchmark standard or a reference point, ground truth aids in the training and development of machine learning...

Grounding refers to the idea of linking abstract, symbolic knowledge or concepts in AI systems to sensory or perceptual data. It is about how machines tie their understanding of language, learned from data, to real-world concepts. The focus is on creating...

Hallucination refers to the simulation of sensory experiences by an artificial intelligence system. These experiences can involve generating perceptions such as images, sounds, or even other sensory modalities without the presence of corresponding external stimuli. Hallucination in AI is a...

In the realm of artificial neural networks, a hidden layer is a crucial component that plays a pivotal role in transforming input data into meaningful output predictions. Hidden layers are situated between the input layer, where the initial data is...

Human-in-the-Loop (HITL) is a concept that embodies the collaborative synergy between artificial intelligence systems and human intelligence. In the context of AI, it refers to a collaborative framework where humans and AI work together iteratively to achieve a desired outcome....

Hybrid AI is a computational approach that combines elements of machine learning, which excels at pattern recognition, with traditional symbolic reasoning or rule-based methods, which excel at logical reasoning and knowledge handling. It aims to leverage the strengths of these...

Hyperparameter is a configuration variable external to the model which cannot be learned from the data. These variables directly control the training process and the structure of the machine learning models. They are often manually set prior to the start...

Hyperparameters in the context of machine learning and artificial intelligence (AI) refer to the parameters or settings that define the structure and control the behavior of learning algorithms. Unlike other parameters in machine learning models, hyperparameters are not learned from...

Image Recognition in AI refers to the technology's capacity to identify and interpret the content and characteristics of images. It involves training artificial neural networks or other machine learning models to comprehend visual data, enabling them to recognize objects, patterns,...

ImageNet holds a significant place in the field of artificial intelligence as one of the most influential and widely used large-scale image datasets. It consists of millions of labeled images across thousands of categories, making it a cornerstone for training...

Inference is the process of applying a trained machine-learning model to new, unseen data in order to make predictions or draw conclusions. It's the stage where the model's learned patterns and relationships are utilized to provide insights or decisions based...

An inference engine a core component of expert systems in artificial intelligence is a tool used to apply reasoning capabilities to a knowledge base to derive conclusions from a set of facts or assertions. It is the brain of the...

Information retrieval (IR) is a crucial aspect of computer science which deals with the organization, storage, retrieval, and evaluation of information from a large collection or database. The main objective of IR is to select relevant and necessary data that...

Insight engines, also known as cognitive search engines, represent the next evolution in information retrieval and discovery. These advanced tools use artificial intelligence (AI) techniques to deliver actionable insights from a company's structured and unstructured data. This involves gathering, organizing,...

Instruction Tuning, also known as Instruction Level Parallelism (ILP), is a powerful technique used in high-performance computing to enhance the efficiency and speed of computation. The basis of instruction tuning involves optimizing the ordering and combination of low-level instructions that...

Intelligent Document Processing (IDP) is an advanced technology utilized in the field of data extraction and processing. This process involves using artificial intelligence (AI) and machine learning (ML) technologies to recognize, classify, and extract relevant data from a wide range...

Knowledge Graph is a structured representation of knowledge that captures relationships between entities, concepts, and facts in a graph-like structure. It serves as a powerful tool for organizing and understanding information in a more interconnected and contextual manner. The essence...

Knowledge Model refers to a structured representation of information, insights, and relationships extracted from data sources. It encompasses a system's understanding of domain-specific knowledge, facts, rules, and patterns that enable it to reason, make informed decisions, and generate intelligent responses....

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 annotations that provide context and meaning to the data, guiding machine learning algorithms to learn patterns...

LangOps, short for Language Operations, refers to the practices, processes, and methodologies that focus on managing, deploying, and optimizing language-related applications and resources in the field of artificial intelligence. LangOps lies in its role in streamlining the development and deployment...

Language Data is textual or spoken information that serves as the raw material for training, fine-tuning, and evaluating language-related artificial intelligence models. It comprises a diverse range of texts, sentences, paragraphs, or spoken utterances that provide the foundation for machine...

Large Language Model (LLM) is a type of artificial intelligence focusing on understanding and generating human language. It's a subset of the broader field of natural language processing (NLP) that leverages machine learning methods to train language models on vast...

Latent space refers to a mathematical space in which higher-dimensional data is represented in a lower-dimensional form. This concept comes into play in the field of machine learning and data science, particularly in the context of dimensionality reduction, which is...

Layer refers to a collection of nodes or neurons that process a set of input data or the output of previous layers. Among these layers, the hidden layer is a primary component that plays a significant role in the learning...

The learning rate is a crucial hyperparameter that determines the step size at which an algorithm proceeds while optimizing a loss function.The learning rate determines the extent to which the weights of our network are modified in response to the...

The term learning-to-learn, often also referred to as meta-learning, is a concept in artificial intelligence (AI) and machine learning (ML), where the primary aim is to create models capable of quickly acquiring new abilities or adjusting to different situations with...

Learning to rank, sometimes called machine-learned ranking, is a significant aspect of machine learning that focuses on constructing models used to sort items into a specific order. This technique is largely used in applications where the items' order matters such...

Lemma is a minor theorem or result that is used to help prove a more complex or significant theorem. It's essentially a stepping stone along the way to proving a larger, overarching theory. Lemmas are used to break down a...

Lexicon refers to a collection of words, phrases, and other language elements along with their associated information. This information can include semantic, phonetic, or syntactic properties of the language elements, which can be used to assist in the analysis and...

Linked Data is a method of publishing structured data on the Internet so it can be interlinked and become more useful. It extends the traditional notion of data as a static and rigid structure and takes the concept one step...

The logit function is a core component in statistics and machine learning, particularly in logistic regression models. It is the inverse of the sigmoid function and it mathematically expresses the logarithm of the odds ratio. The term "logit" comes from...

Long Short-Term Memory (LSTM) Networks are a type of recurrent neural network (RNN) devised to address the problem of vanishing and exploding gradients, which is a common issue in traditional RNNs. Developed by Sepp Hochreiter and Jürgen Schmidhuber in 1997,...

A loss function, also known as a cost function, is a fundamental concept used to quantify how well a predictive model is performing. It computes the discrepancy between the predicted output of the model and the actual or true values....

Machine learning in AI is a transformative approach that empowers computers to learn from data and improve their performance on a specific task over time, without being explicitly programmed. Machine learning leverages algorithms and statistical techniques to enable systems to...

Machine Translation refers to the automated process of translating text or speech from one language to another using computational methods. At its core, machine translation aims to bridge linguistic barriers and enable effective communication between people who speak different languages....

Metadata in AI refers to the descriptive information that provides context, attributes, and characteristics about a piece of data. It serves as a way to organize, categorize, and understand the underlying content without directly altering the data itself. Metadata is...

A Mixture of Experts (MoE) is a machine learning technique developed by Michael I. Jordan and Robert A. Jacobs in the early 1990s. The model is a type of ensemble learning approach that consists of multiple learning components, termed "experts,"...

Model drift, also known as concept drift, refers to the change in data patterns over time that result in the degradation of a predictive model's performance. In the field of machine learning, predictive models are trained with the assumption that...

A model parameter is a configuration that is internal to the model and whose value can be estimated from the given data. They are an essential part of the model structure as they help in predicting future outcomes and are...

The Monte Carlo method refers to a set of techniques that use random sampling to solve problems that might be deterministic in principle. Monte Carlo methods can be used when a problem is complex and the solution space or possibility...

Morphological analysis is a primary facet of natural language processing (NLP) and computational linguistics, where it's used to interpret, understand and manipulate human language. It is a process that breaks down words into their smallest constituents, or morphemes, which are...

Multi-modal learning is a subfield of machine learning that aims to build models that can process and relate information from multiple types of data, or "modes." These modes can include diverse data types, such as text, images, audio, video, sensor...

Multi-task learning is an approach in machine learning where a model is trained to perform multiple tasks simultaneously, with the goal of improving the overall performance and efficiency of the system. The premise is that by learning several tasks concurrently,...

The term "multimodal" pertains to systems or models that are designed to receive and analyze multiple types of data, or "modes". These modes can include a wide range of data types, such as text, images, audio, video, among others. A...

Naive Bayes is a probabilistic classification technique rooted in Bayesian probability theory. Despite its seemingly simplistic assumptions, Naive Bayes lies in its effectiveness and efficiency in various real-world applications. This algorithm assumes that features are conditionally independent given the class...

Named Entity Recognition (NER) is a process that involves identifying and classifying named entities within text data, such as names of people, organizations, locations, dates, and more. NER is the ability to extract structured information from unstructured text, enabling AI...

Natural Language Processing (NLP) is a multidisciplinary field that focuses on enabling computers to understand, interpret, and generate human language. The essence of NLP lies in its aim to bridge the communication gap between humans and machines, allowing computers to...

Natural Language Understanding (NLU) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human languages. Specifically, it involves the processing and analysis of human language by a computer program. The primary goal of NLU...

NeRF, or Neural Radiance Fields, is an emerging method in the field of computer vision and deep learning developed in 2020. Its primary role is in the generation and rendering of novel, high-quality 3D views of a scene from 2-Dimensional...

Neural networks, also known as artificial neural networks (ANNs), are a key component of artificial intelligence (AI). Modelled after the human brain, they're designed to replicate the way humans learn by processing interconnected layers of artificial neurons, also known as...

Neuron is used to describe the fundamental processing units of these complex systems. Artificial neurons, also known as nodes, are inspired by biological neurons in the human brain. These neurons form the basis of a neural network. They receive information,...

Natural Language Generation (NLG) is a subfield of artificial intelligence (AI) that focuses on generating text that is natural, clear, and concise. The aim is to create written or spoken narrative from a dataset, with context and variability that would...

Natural Language Query (NLQ) is a subset of Natural Language Processing (NLP), which allows users to interact with systems using everyday, conversational language. The main goal of NLQ is to enable users to ask questions to databases or other information...

Natural Language Technology (NLT) is a broad term that encompasses all technologies designed to handle and interact with human language. It forms the basis for many artificial intelligence applications, aiming to bridge the gap between human and machine communication. NLT...

In the realm of artificial intelligence and machine learning, an Objective Function is a critical component that quantifies the performance or effectiveness of a model. The essence of an objective function lies in its role as a guiding metric that...

Ontology is a formal representation of knowledge that defines the concepts, entities, relationships, and properties within a specific domain. The essence of an ontology lies in its ability to structure and organize information in a way that facilitates better understanding,...

Optical Character Recognition (OCR) in AI refers to the technology that allows computers to convert images containing printed or handwritten text into machine-readable and editable text. OCR is capable to bridge the gap between physical documents and digital data, enabling...

Parsing embodies the intricate process of dissecting and comprehending the structure and meaning of complex data, most notably natural language. Parsing is the cognitive task of breaking down sentences or textual content into constituent parts, such as words, phrases, and...

Part-of-Speech (POS) tagging in the field of artificial intelligence encapsulates the crucial task of assigning specific grammatical categories or "tags" to each word in a given text, based on its syntactic role within a sentence. This process involves the identification...

Pattern Recognition is a branch of artificial intelligence that focuses on the identification and classification of patterns or structures in data. The essence of pattern recognition lies in teaching machines to learn from observations and make decisions, predictions, or classifications...

Post-Editing Machine Translation, commonly known as PEMT, is a process in which machine-generated translations are reviewed and corrected by human linguists. The essence of PEMT lies in the fusion of machine efficiency with human expertise to produce higher-quality translations. By...

Personally Identifiable Information (PII) is any information that can be utilized to distinguish or trace an individual's identity. This data can include direct identifiers, such as a person's name, social security number, or contact information, which can uniquely identify a...

Pooling, with Max Pooling being a popular type, is an essential concept in the field of Convolutional Neural Networks (CNN) – a deep learning algorithm often utilized for image processing and recognition tasks. The essence of pooling lies in its...

Post-processing refers to any operations or adjustments that are applied to data, images, or materials after the initial phase of production or recording. Post-processing is a value-added stage meant to improve, optimize, or adapt the initial output to satisfy specific...

Pre-processing is a preliminary stage in any production or analysis pipeline, where initial input is prepared or conditioned to optimize it for the subsequent stages. The essence of pre-processing lies in its ability to transform raw input into a more...

A pre-trained model is a machine learning or artificial intelligence model that, instead of being trained from scratch with a randomly initialized network, has already been trained on a large benchmark dataset. This process, typically carried out on massive datasets,...

Pre-training in artificial intelligence (AI) refers the process of training a machine model on a large-scale dataset to using it for a task. Such a model, from previously established networks, is called a pre-trained model. The concept behind pre-training lies...

Preprocessing involves a series of preparatory steps to transform raw data into a suitable format for analysis by machine learning algorithms or other computational methods. It encompasses various techniques aimed at cleaning, organizing, and enhancing data quality. Common preprocessing tasks...

Principal Component Analysis (PCA)  is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional representation while preserving the most important information. It works by identifying the principal components, which are linear combinations of the original features that...

Prompt refers is a specific instruction or query provided to a language model or other AI system to guide its generation of text or responses. Prompts serve as input cues that direct the AI's output toward desired outcomes. They can...

Prompt engineering involves the strategic formulation and design of prompts that effectively guide the behavior and output of language models or other AI systems. It encompasses the process of tailoring input cues to elicit desired responses, generate specific types of...

Random Forest stands as a versatile and robust machine learning algorithm within the realm of artificial intelligence. At its core, a Random Forest is an ensemble learning method that harnesses the power of multiple decision trees to enhance predictive accuracy...

Recall stands as a critical performance metric used to evaluate the effectiveness of classification models, particularly in tasks involving binary outcomes or imbalanced datasets. Recall, also known as sensitivity or true positive rate, focuses on the model's ability to correctly...

In the realm of artificial intelligence and neural networks, a Rectified Linear Unit (ReLU) is a widely used activation function that has transformed the landscape of deep learning. The essence of ReLU lies in its simplicity and effectiveness in enhancing...

Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed to handle sequences of data, making them particularly suited for tasks involving time series, natural language processing (NLP), speech recognition, and more. In contrast to conventional feedforward artificial...

Regression is a fundamental concept in statistics and artificial intelligence that involves predicting a continuous or numerical output based on input features. It encompasses a range of techniques and models used to establish relationships between variables and make quantitative predictions....

In artificial intelligence, a Regressor refers to a type of machine-learning model that specializes in solving regression problems. Regression is a predictive modeling technique where the goal is to predict a continuous numerical value as the output based on input...

Regularization's purpose lies in mitigating overfitting tendencies and enhancing the overall capacity of models to generalize accurately. Overfitting occurs when a model learns to fit the training data too closely, capturing noise and irrelevant patterns that may not generalize well...

Reinforcement Learning (RL) is a paradigm in artificial intelligence that deals with the interaction of an agent with an environment to learn optimal decision-making strategies. Unlike supervised learning, where the model is trained on labeled data, and unsupervised learning, where...

Responsible AI refers to the ethical, transparent, and accountable development and deployment of artificial intelligence (AI) technologies. It encompasses a set of principles and practices aimed at ensuring that AI systems are designed and used in ways that align with...

Restricted Boltzmann Machines (RBMs) are a type of artificial neural network that falls under the category of generative models. They are composed of visible and hidden units arranged in a bipartite graph structure, where connections exist only between visible and...

Reinforcement Learning from Human Feedback (RLHF) is an approach in artificial intelligence that combines reinforcement learning (RL) with human-provided feedback to improve the learning process of AI agents. RLHF recognizes that direct RL training, especially in complex and uncertain environments,...

Rules-based Machine Translation (RBMT) is an early approach to machine translation in artificial intelligence that relies on predefined linguistic rules and structures to translate text from one language to another. Unlike statistical machine translation (SMT) or neural machine translation (NMT),...

SAO refers to a fundamental structure that represents the core elements of a sentence: Subject, Action, and Object. The essence of SAO lies in its ability to systematically break down linguistic content into these three components, enabling machines to extract...

In the realm of artificial intelligence, a Semantic Network embodies a structured representation of knowledge that models relationships between concepts using interconnected nodes and edges. The essence of a Semantic Network lies in its ability to depict not only the...

Semantic Search constitutes a paradigm shift in the field of artificial intelligence, redefining the way search engines and information retrieval systems operate. The essence of Semantic Search lies in its ability to comprehend the context, intent, and meaning behind user...

  Semi-structured data within the realm of artificial intelligence (AI), pertains to data that lacks the rigid structure of traditional databases but still maintains a certain level of organization. It occupies the middle ground between structured data, characterized by predefined...

Semi-supervised learning is an AI approach that lies between supervised and unsupervised learning. In this method, a model learns from a combination of labeled and unlabeled data. While supervised learning relies solely on labeled data to train models, and unsupervised...

Sentiment analysis is the computational process of evaluating and discerning the emotional tone, opinion, or attitude expressed within text data. It involves using machine learning and natural language processing techniques to categorize text as positive, negative, or neutral based on...

Similarity refers to the measure of likeness or resemblance between objects, data points, or concepts. It quantifies the degree to which two entities share common attributes, characteristics, or features. Similarity is a fundamental concept in various AI applications, such as...

The Simple Knowledge Organization System (SKOS) in AI is a framework designed to organize and represent knowledge and concepts in a structured manner. It is used to create controlled vocabularies, taxonomies, and thesauri that facilitate efficient data management and retrieval....

Singularity is a hypothetical point in the future when technological progress, particularly in artificial intelligence, reaches a level where machines become capable of surpassing human intelligence and potentially leading to rapid and uncontrollable advancements. The concept envisions a scenario where...

Speech analytics is the process of analyzing spoken language to extract valuable insights and information. It involves using natural language processing (NLP) and machine learning techniques to transcribe, interpret, and understand spoken interactions. Speech analytics can be applied across various...

Speech recognition in AI refers to the technology that enables computers to convert spoken language into text. Also known as automatic speech recognition (ASR), this process involves the use of machine learning algorithms to decipher spoken words and phrases and...

Statistical distribution is the pattern or arrangement of data points within a set, describing the likelihood of different values occurring. It is a fundamental concept in probability theory and statistics that characterizes the behavior of a random variable. Statistical distributions...

Structured data in AI refers to organized and well-defined information that is formatted according to a predefined schema or model. It follows a consistent tabular structure, where data is stored in rows and columns, resembling a traditional database format. Structured...

Supervised learning is a fundamental machine learning paradigm that involves training models using labeled data. In this approach, the AI system learns from input-output pairs, where the input data is associated with corresponding target or output labels. The primary goal...

Support Vector Machines (SVMs) stand as a form of supervised machine learning technique utilized in tasks involving classification and regression. Their purpose revolves around identifying the most advantageous hyperplane, one that effectively distinguishes between distinct sets of data points, all...

Symbolic Artificial Intelligence refers to a classical approach in AI that focuses on manipulating symbols and using logical reasoning to simulate human intelligence. In this paradigm, knowledge is represented using symbols and rules, and AI systems perform operations on these...

Symbolic methodology is an approach that centers around the manipulation of symbols, formal logic, and structured knowledge representation to enable intelligent reasoning and problem-solving. It involves representing information using symbolic languages, such as logic statements, rules, and symbols, and then...

Syntax refers to the grammatical structure and arrangement of words, symbols, or elements within a language or formal system. It dictates the rules and patterns governing how words and symbols should be combined to create meaningful expressions and statements. Syntax...

Synthetic data is artificially generated data that mimics real-world data patterns and characteristics. It is created using algorithms or models to replicate the statistical properties and structures of authentic data without directly using actual observations. Synthetic data is used when...

Tagging in AI refers to the process of assigning descriptive labels or metadata to various types of data, such as text, images, audio, or video. It serves as a fundamental technique for organizing, categorizing, and enhancing the understanding of these...

Taxonomy refers to a hierarchical framework used to systematically categorize and organize concepts, objects, or data based on their inherent relationships and characteristics. It serves as a foundational structure for understanding and classifying information in a structured manner. Taxonomies provide...

TensorFlow is a prominent and powerful open-source framework in the field of artificial intelligence that has revolutionized the development of machine learning and deep learning models. At its core, TensorFlow focuses on the concept of tensors, which are multi-dimensional arrays...

In the realm of artificial intelligence and machine learning, a Test Set is a portion of data that is separate from the training data and is used to evaluate the performance and generalization capabilities of a trained model. The primary...

Testing, often referred to as "testing data" or the "test set," is a crucial step in the development of artificial intelligence (AI) models. In the context of machine learning, testing involves evaluating the performance of a trained model on a...

Text analytics, a key component of artificial intelligence (AI), involves the application of computational techniques to process, analyze, and derive insights from textual data. It encompasses a range of tasks aimed at extracting meaningful information from unstructured text sources, such...

Text Summarization is the automatic condensation of longer texts into shorter, coherent versions while retaining the key information and main ideas. It is a vital component of natural language processing (NLP) that addresses the challenge of information overload, enabling efficient...

Thesauri in AI are structured databases or lists of words and phrases that are organized based on their semantic relationships, such as synonyms, antonyms, and hierarchical associations. They serve as valuable linguistic resources for natural language processing (NLP) tasks, aiding...

A time series, in the context of AI and data analysis, refers to a sequence of data points collected at regular intervals over time. Time series data is characterized by its temporal ordering, where each data point is associated with...

Tokens are individual units or elements that make up a piece of text, such as a sentence or document. These units are typically words, but they can also be subwords, characters, or other linguistic components. Tokenization is the process of...

Topic is the process of automatically identifying and extracting meaningful topics or themes from a collection of text documents. It's a powerful technique used to uncover latent patterns and underlying structures within large datasets of unstructured text. Topic modeling helps...

  A Tensor Processing Unit (TPU) is a specialized hardware accelerator developed by Google for accelerating machine learning workloads, particularly those involving deep learning and neural networks. TPUs are designed to perform matrix computations efficiently, which are at the core...

Training data is a fundamental concept in artificial intelligence (AI) that refers to the labeled dataset used to teach machine learning models to recognize patterns, make predictions, and perform tasks. The training data consists of input examples paired with their...

The training set is a subset of data that is specifically used to teach a machine-learning model how to make predictions or perform tasks. The training set consists of examples of input data, along with their corresponding known outputs or...

Transfer learning is a technique where a pre-trained model's knowledge is leveraged to enhance the performance of a related but different task. Instead of training a model from scratch for a specific task, transfer learning involves using a model that...

The Transformer is a groundbreaking architecture in the field of artificial intelligence (AI) that revolutionized natural language processing (NLP) and various other domains. Introduced in the "Attention Is All You Need" paper by Vaswani et al. in 2017, the Transformer...

A treemap in AI is a data visualization technique that displays hierarchical data structures in a hierarchical layout using nested rectangles. Each rectangle represents a category or data element, and the size and color of the rectangles convey additional information...

Triple connections, alternatively termed Subject-Operation-Target (SOT) connections, stand as a structured form employed in the realm of artificial intelligence and the representation of knowledge. These connections serve the purpose of delineating associations amid entities within a given sentence or context....

Tuning, also known as model tuning or fine-tuning, is a critical process in artificial intelligence (AI) and machine learning that involves optimizing the performance of a trained model by adjusting its hyperparameters or architecture. Hyperparameters are configuration settings that are...

The Turing Test is a benchmark and concept in artificial intelligence (AI) introduced by British mathematician and computer scientist Alan Turing in 1950. The test assesses a machine's ability to exhibit human-like intelligence and conversation in natural language. In the...

Type I error, also known as a false positive, is a concept in statistics and artificial intelligence that refers to a situation where a hypothesis or a model incorrectly identifies a condition as true when it is actually false. In...

Type II error, also known as a false negative, is a concept in statistics and artificial intelligence that occurs when a hypothesis or a model incorrectly fails to identify a condition as true when it is actually true. In other...

Uncertainty in AI refers to the lack of complete knowledge or confidence in the outcomes or predictions generated by machine learning models. It acknowledges the inherent limitations of models when dealing with complex, noisy, or ambiguous data, and the inability...

Underfitting refers to a scenario in which a machine learning model fails to capture the underlying patterns and relationships within the training data. It occurs when the model is too simplistic or lacks the capacity to represent the complexity of...

Unstructured data is information that lacks a predefined or organized format, making it challenging for traditional algorithms to interpret and analyze. Unlike structured data, which is neatly organized into tables and fields, unstructured data is often in the form of...

Unsupervised learning is a paradigm of machine learning where the emphasis is on discovering patterns, structures, or relationships within data without explicit labeled guidance. Unlike supervised learning, where models are trained on labeled examples with well-defined target outputs, unsupervised learning...

Validation data is a subset of labeled data that is distinct from the training data and is used to assess the performance and generalization capabilities of a machine learning model during its development and training process. The purpose of validation...

Vanishing and exploding gradients are issues that can occur during the training of deep neural networks in artificial intelligence. These problems arise from the way gradients (derivatives of the loss function with respect to the model's parameters) are propagated backward...

Explainable AI (XAI) is the practice of designing and developing artificial intelligence models and systems in a way that their decision-making processes and outcomes can be understood and interpreted by humans. As AI technologies become more sophisticated and complex, there...

Zero-shot learning in AI refers to a paradigm where a machine learning model is trained to recognize or perform tasks for which it has never seen any examples during training. Unlike traditional supervised learning, where models require labeled training data...