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The term "black box" is used because the actual program being executed is not examined. In computing in general, a black box program is one where the user cannot see the inner workings (perhaps because it is a closed source program) or one which has no side effects and the function of which need not be examined, a routine suitable for re-use.
Black Box Corporation. Black Box Corporation is an IT company headquartered in Texas, United States. [1] The company provides technology assistance and consulting services to businesses in a variety of sectors including retail, transportation, government, education, and public safety. Black Box operates in 75 locations across 35 countries.
t. e. Black-box testing, sometimes referred to as specification-based testing, [1] is a method of software testing that examines the functionality of an application without peering into its internal structures or workings. This method of test can be applied virtually to every level of software testing: unit, integration, system and acceptance.
Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this. [1][2] The main focus is usually on the reasoning behind the decisions or ...
Machine ethics (or machine morality) is the field of research concerned with designing Artificial Moral Agents (AMAs), robots or artificially intelligent computers that behave morally or as though moral. [2][3][4][5] To account for the nature of these agents, it has been suggested to consider certain philosophical ideas, like the standard ...
Random search. Random search (RS) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized, and RS can hence be used on functions that are not continuous or differentiable. Such optimization methods are also known as direct-search, derivative-free, or black-box methods.
Neural architecture search (NAS) [1][2] is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par with or outperform hand-designed architectures. [3][4] Methods for NAS can be categorized according to the search space ...
Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [6]Bayesian optimization is typically used on problems of the form (), where is a set of points, , which rely upon less (or equal to) than 20 dimensions (,), and whose membership can easily be evaluated.
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