DataIn common usage and statistics, data (USˈdætə; UKˈdeɪtə) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.
Big dataBig data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.
English languageEnglish is a West Germanic language in the Indo-European language family. It originated in early medieval England and, today, is the most spoken language in the world and the third most spoken native language, after Mandarin Chinese and Spanish. English is the most widely learned second language and is either the official language or one of the official languages in 59 sovereign states. There are more people who have learned English as a second language than there are native speakers.
Data scienceData science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Large language modelA large language model (LLM) is a language model characterized by its large size. Their size is enabled by AI accelerators, which are able to process vast amounts of text data, mostly scraped from the Internet. The artificial neural networks which are built can contain from tens of millions and up to billions of weights and are (pre-)trained using self-supervised learning and semi-supervised learning. Transformer architecture contributed to faster training.
Data analysisData analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.
Data managementData management comprises all disciplines related to handling data as a valuable resource. The concept of data management arose in the 1980s as technology moved from sequential processing (first punched cards, then magnetic tape) to random access storage. Since it was now possible to store a discrete fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems.
Data warehouseIn computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence. Data warehouses are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise. This is beneficial for companies as it enables them to interrogate and draw insights from their data and make decisions.
English-language learnerEnglish-Language Learner (often abbreviated as ELL) is a term used in some English-speaking countries such as the United States and Canada to describe a person who is learning the English language and has a native language that is not English. Some educational advocates, especially in the United States, classify these students as non-native English speakers or emergent bilinguals.
International EnglishInternational English is the concept of using the English language as a global means of communication similar to an international auxiliary language, and often refers to the movement towards an international standard for the language. Related and sometimes synonymous terms include: Global English, World English, Common English, Continental English, General English, and Engas (English as associate language). Sometimes, these terms refer to the actuality of the situation, where English is spoken and used in numerous dialects around the world.
Spring FrameworkThe Spring Framework is an application framework and inversion of control container for the Java platform. The framework's core features can be used by any Java application, but there are extensions for building web applications on top of the Java EE (Enterprise Edition) platform. The framework does not impose any specific programming model.. The framework has become popular in the Java community as an addition to the Enterprise JavaBeans (EJB) model. The Spring Framework is open source.
Sino-Tibetan languagesSino-Tibetan, also cited as Trans-Himalayan in a few sources, is a family of more than 400 languages, second only to Indo-European in number of native speakers. The vast majority of these are the 1.3 billion native speakers of Sinitic languages. Other Sino-Tibetan languages with large numbers of speakers include Burmese (33 million) and the Tibetic languages (6 million). Other languages of the family are spoken in the Himalayas, the Southeast Asian Massif, and the eastern edge of the Tibetan Plateau.
Data modelA data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner. The corresponding professional activity is called generally data modeling or, more specifically, database design.
Generative pre-trained transformerGenerative pre-trained transformers (GPT) are a type of large language model (LLM) and a prominent framework for generative artificial intelligence. The first GPT was introduced in 2018 by OpenAI. GPT models are artificial neural networks that are based on the transformer architecture, pre-trained on large data sets of unlabelled text, and able to generate novel human-like content. As of 2023, most LLMs have these characteristics and are sometimes referred to broadly as GPTs.
Indo-European languagesThe Indo-European languages are a language family native to the overwhelming majority of Europe, the Iranian plateau, and the northern Indian subcontinent. Some European languages of this family—English, French, Portuguese, Russian, Dutch, and Spanish—have expanded through colonialism in the modern period and are now spoken across several continents. The Indo-European family is divided into several branches or sub-families, of which there are eight groups with languages still alive today: Albanian, Armenian, Balto-Slavic, Celtic, Germanic, Hellenic, Indo-Iranian, and Italic; and another nine subdivisions that are now extinct.
Foundation modelsA foundation model (also called base model) is a large machine learning (ML) model trained on a vast quantity of data at scale (often by self-supervised learning or semi-supervised learning) such that it can be adapted to a wide range of downstream tasks. Foundation models have helped bring about a major transformation in how artificial intelligence (AI) systems are built, such as by powering prominent chatbots and other user-facing AI.
Austronesian languagesThe Austronesian languages (ˌɔːstrəˈniːʒən) are a language family widely spoken throughout Maritime Southeast Asia, Malay Peninsula, parts of Mainland Southeast Asia, Madagascar, the islands of the Pacific Ocean and Taiwan (by Taiwanese indigenous peoples). There are also a number of speakers in continental Asia. They are spoken by about 386 million people (4.9% of the world population). This makes it the fifth-largest language family by number of speakers.
Model selectionModel selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context of learning, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection.
Austroasiatic languagesThe Austroasiatic languages ˌɒstroʊ.eɪʒiˈætɪk,_ˌɔː- , are a large language family in Mainland Southeast Asia, South Asia and East Asia. These languages are scattered throughout parts of Thailand, Laos, India, Myanmar, Malaysia, Bangladesh, Nepal, and southern China. Austroasiatic constitute the majority languages of Vietnam and Cambodia. There are around 117 million speakers of Austroasiatic languages, of which more than two-thirds are Vietnamese speakers. Of these languages, only Vietnamese, Khmer, and Mon have a long-established recorded history.
Kalman filterFor statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.
