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Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), is a field of research within artificial intelligence (AI) that explores methods that provide humans with the ability of intellectual oversight over AI algorithms.
Explainable Artificial Intelligence in the context of black box machine learning models: Saliency maps are a prominent tool in XAI, [6] providing visual explanations of the decision-making process of machine learning models, particularly deep neural networks. These maps highlight the regions in input images, text, or other types of data that ...
The field of Explainable AI seeks to provide better explanations from existing algorithms, and algorithms that are more easily explainable, but it is a young and active field. [ 18 ] [ 19 ] Others argue that the difficulties with explainability are due to its overly narrow focus on technical solutions rather than connecting the issue to the ...
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Elon Musk's artificial intelligence startup xAI plans to expand its Memphis, Tennessee, supercomputer to house at least one million graphics processing units (GPUs), the Greater Memphis Chamber ...
The billionaire also said xAI released its first AI model, a bot named Grok, after making it available to all X Premium+ subscribers on Friday. The startup aims to create AI tools that 'assist ...
X.AI Corp., [4] [5] doing business as xAI, is an American startup company working in the area of artificial intelligence (AI). Founded by Elon Musk in March 2023, its stated goal is "to understand the true nature of the universe".
The need for models that can be understood by humans emerges in quantum machine learning in analogy to classical machine learning and drives the research field of explainable quantum machine learning (or XQML [95] in analogy to XAI/XML). These efforts are often also referred to as Interpretable Machine Learning (IML, and by extension IQML). [96]