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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 ...
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 ...
Machine learning is the study of programs that can improve their performance on a given task automatically. [41] It has been a part of AI from the beginning. [e] There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance. [44]
Later symbolic AI work after the 1980's incorporated more robust approaches to open-ended domains such as probabilistic reasoning, non-monotonic reasoning, and machine learning. Currently, most AI researchers [citation needed] believe deep learning , and more likely, a synthesis of neural and symbolic approaches ( neuro-symbolic AI ), will be ...
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...
On AI, the company’s revenue has gotten a lift from selling AI servers. Dell shipped $2.9 billion of them in the most recent third quarter and has a backlog of $4.5 billion in orders.
Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. [129] It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. [ 130 ]
Generative language models are not trained on the translation task, let alone on a parallel dataset. Instead, they are trained on a language modeling objective, such as predicting the next word in a sequence drawn from a large dataset of text. This dataset can contain documents in many languages, but is in practice dominated by English text. [36]
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