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Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
The artificial intelligence (AI) boom has brought with it a cornucopia of jargon — from "generative AI" to "synthetic data" — that can be hard to parse. An AI glossary: The words and terms to ...
Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks. [62]
AI has potential benefits and potential risks. [203] AI may be able to advance science and find solutions for serious problems: Demis Hassabis of DeepMind hopes to "solve intelligence, and then use that to solve everything else". [204] However, as the use of AI has become widespread, several unintended consequences and risks have been ...
This is an alphabetical list of notable technology terms. It includes terms with notable applications in computing, networking, and other technological fields. Contents
A thesaurus is composed by at least three elements: 1-a list of words (or terms), 2-the relationship amongst the words (or terms), indicated by their hierarchical relative position (e.g. parent/broader term; child/narrower term, synonym, etc.), 3-a set of rules on how to use the thesaurus.
Eventually these words will all be translated into big lists in many different languages and using the words in phrase contexts as a resource. You can use the list to generate your own lists in whatever language you're learning and to test yourself.
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]
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