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Compound verbs, a highly visible feature of Hindi–Urdu grammar, consist of a verbal stem plus a light verb. The light verb (also called "subsidiary", "explicator verb", and "vector" [ 55 ] ) loses its own independent meaning and instead "lends a certain shade of meaning" [ 56 ] to the main or stem verb, which "comprises the lexical core of ...
Hindustani is extremely rich in complex verbs formed by the combinations of noun/adjective and a verb. Complex verbs are of two types: transitive and intransitive. [3]The transitive verbs are obtained by combining nouns/adjectives with verbs such as karnā 'to do', lenā 'to take', denā 'to give', jītnā 'to win' etc.
A human computer, with microscope and calculator, 1952. It was not until the mid-20th century that the word acquired its modern definition; according to the Oxford English Dictionary, the first known use of the word computer was in a different sense, in a 1613 book called The Yong Mans Gleanings by the English writer Richard Brathwait: "I haue [] read the truest computer of Times, and the best ...
Hindi-Urdu, also known as Hindustani, has three noun cases (nominative, oblique, and vocative) [1] [2] and five pronoun cases (nominative, accusative, dative, genitive, and oblique). The oblique case in pronouns has three subdivisions: Regular, Ergative , and Genitive .
The expression was popular in the early days of computing. The first known use is in a 1957 syndicated newspaper article about US Army mathematicians and their work with early computers, [4] in which an Army Specialist named William D. Mellin explained that computers cannot think for themselves, and that "sloppily programmed" inputs inevitably lead to incorrect outputs.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
In computer programming, tracing garbage collection is a form of automatic memory management that consists of determining which objects should be deallocated ("garbage collected") by tracing which objects are reachable by a chain of references from certain "root" objects, and considering the rest as "garbage" and collecting them.
From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward by Joseph Weizenbaum, about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculation and qualitative, value-based ...