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  2. Explainable artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Explainable_artificial...

    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 ...

  3. Right to explanation - Wikipedia

    en.wikipedia.org/wiki/Right_to_explanation

    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 ...

  4. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    In the more general multiple regression model, there are independent variables: = + + + +, where is the -th observation on the -th independent variable.If the first independent variable takes the value 1 for all , =, then is called the regression intercept.

  5. What Elon Musk is really building inside his ChatGPT ... - AOL

    www.aol.com/finance/elon-musk-really-building...

    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.

  6. Artificial intelligence engineering - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence...

    Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.

  7. Design matrix - Wikipedia

    en.wikipedia.org/wiki/Design_matrix

    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 ...

  8. Fraction of variance unexplained - Wikipedia

    en.wikipedia.org/wiki/Fraction_of_variance...

    In this case, no variation in Y can be accounted for, and the FVU then has its maximum value of 1. More generally, the FVU will be 1 if the explanatory variables X tell us nothing about Y in the sense that the predicted values of Y do not covary with Y. But as prediction gets better and the MSE can be reduced, the FVU goes down.

  9. Elon Musk’s xAI startup could help Tesla with FSD, Optimus ...

    www.aol.com/finance/elon-musk-xai-startup-could...

    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 ...