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25 de abr. de 2024 · xAI, Elon Musk’s 10-month-old competitor to the AI phenom OpenAI, is raising $6 billion on a pre-money valuation of $18 billion, according to one trusted source close to the deal.
26 de abr. de 2024 · Además, es probable que también formen parte del acuerdo Valor Equity Partners y Gigafund, cuyos fundadores también forman parte del círculo íntimo de Musk. La startup de inteligencia artificial de Elon Musk que busca competir contra OpenAI, xAI, está a punto de recaudar 6.000 millones de dólares.
Hace 1 día · Enter Explainable Artificial Intelligence, otherwise known as XAI. XAI bridges the gap between complex AI systems and human users, making AI models more transparent and interpretable while paving the way for responsible and trustworthy development of the technology. In this article, we'll be delving deep into the meaning of Explainable A ...
26 de abr. de 2024 · ¿Qué busca xAI de Elon Musk? xAI tiene como objetivo principal conectar los mundos digital y físico a través de la IA. Esta meta se planea lograr mediante la recopilación de datos de entrenamiento de diversas empresas de Musk, incluidas Tesla, SpaceX y Neuralink.
Hace 4 días · In response to the demand for Explainable Artificial Intelligence (XAI), we investigate the use of Large Language Models (LLMs) to transform ML explanations into natural, human-readable narratives. Rather than directly explaining ML models using LLMs, we focus on refining explanations computed using existing XAI algorithms. We outline several research directions, including defining evaluation ...
24 de abr. de 2024 · Explainable AI (XAI) aims to balance model explainability with high learning performance, fostering human understanding, trust, and effective management of AI partners. Drawing from social sciences and psychology, XAI seeks to create a suite of techniques facilitating transparency and comprehension in the evolving landscape of AI.
2 de may. de 2024 · Abstract: Artificial Intelligence (XAI) has found numerous applications in computer vision. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected.
