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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.
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 ...
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 ...
Solve problems and you get both the answers and confirmation that your AI can think for itself, unlike models such as OpenAI’s GPT-4 that essentially regurgitate their training material.
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.
Some models assume a special form such as a linear regression [6] [7] or neural network. [8] [9] These have special analysis methods. In particular linear regression techniques [10] are much more efficient than most non-linear techniques. [11] [12] The model can be deterministic or stochastic (i.e. containing random components) depending on its ...
LinkedIn profile says he’s an xAI cofounder. Used to work at Google and DeepMind. Yuhuai (Tony) Wu. Former Google research scientist. Christian Szegedy. Listed as AI researcher and cofounder of xAI.