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XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision. [6] [7] XAI hopes to help users of AI-powered systems perform more effectively by improving their understanding of how those systems reason. [8] XAI may be an implementation of the social right to ...
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 ...
Xai, XAI or xAI may refer to: Explainable artificial intelligence, in artificial intelligence technology; Xai-Xai, a city in the south of Mozambique; XAI, the IATA airport code for Xinyang Minggang Airport, in Xinyang, China; xai, the ISO 639-3 language code of Kaimbé language, an extinct language in Brazil.
The multibillionaire had previously co-founded OpenAI in 2015 but left the firm in 2018 to avoid conflicts of interest with Tesla which had its own AI operations for the vehicles’ autopilot mode.
A proposal that has been shown to investors calls for Tesla to license xAI’s AI models to help power Full Self-Driving (FSD), which is the company’s driver-assistance software, as well as a ...
The company has raised $134.7 million in equity financing from a total offering amount of $1 billion, the filing with the Securities and Exchange Commission showed. Musk has been vocal about his ...
The earliest regression form was seen in Isaac Newton's work in 1700 while studying equinoxes, being credited with introducing "an embryonic linear aggression analysis" as "Not only did he perform the averaging of a set of data, 50 years before Tobias Mayer, but summing the residuals to zero he forced the regression line to pass through the ...
A regression model may be represented via matrix multiplication as y = X β + e , {\displaystyle y=X\beta +e,} where X is the design matrix, β {\displaystyle \beta } is a vector of the model's coefficients (one for each variable), e {\displaystyle e} is a vector of random errors with mean zero, and y is the vector of predicted outputs for each ...