In science, computing, and engineering, a black box is a system which can be viewed in terms of its inputs and outputs (or transfer characteristics), without any knowledge of its internal workings. Its implementation is "opaque" (black). The term can be used to refer to many inner workings, such as those of a transistor, an engine, an algorithm, the human brain, or an institution or government.
To analyse an open system with a typical "black box approach", only the behavior of the stimulus/response will be accounted for, to infer the (unknown) box. The usual representation of this black box system is a data flow diagram centered in the box.
The opposite of a black box is a system where the inner components or logic are available for inspection, which is most commonly referred to as a white box (sometimes also known as a "clear box" or a "glass box").
The modern meaning of the term "black box" seems to have entered the English language around 1945. In electronic circuit theory the process of network synthesis from transfer functions, which led to electronic circuits being regarded as "black boxes" characterized by their response to signals applied to their ports, can be traced to Wilhelm Cauer who published his ideas in their most developed form in 1941. Although Cauer did not himself use the term, others who followed him certainly did describe the method as black-box analysis. Vitold Belevitch puts the concept of black-boxes even earlier, attributing the explicit use of two-port networks as black boxes to Franz Breisig in 1921 and argues that 2-terminal components were implicitly treated as black-boxes before that.
In cybernetics, a full treatment was given by Ross Ashby in 1956. A black box was described by Norbert Wiener in 1961 as an unknown system that was to be identified using the techniques of system identification. He saw the first step in self-organization as being to be able to copy the output behavior of a black box. Many other engineers, scientists and epistemologists, such as Mario Bunge, used and perfected the black box theory in the 1960s.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Cybernetics is a wide-ranging field concerned with circular causal processes such as feedback. Norbert Wiener named the field after an example of circular causal feedback—that of steering a ship where the helmsman adjusts their steering in response to the effect it is observed as having, enabling a steady course to be maintained amongst disturbances such as cross-winds or the tide.
In electrical circuit theory, a port is a pair of terminals connecting an electrical network or circuit to an external circuit, as a point of entry or exit for electrical energy. A port consists of two nodes (terminals) connected to an outside circuit which meets the port condition – the currents flowing into the two nodes must be equal and opposite. The use of ports helps to reduce the complexity of circuit analysis. Many common electronic devices and circuit blocks, such as transistors, transformers, electronic filters, and amplifiers, are analyzed in terms of ports.
A hypothesis (: hypotheses) is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories. Even though the words "hypothesis" and "theory" are often used interchangeably, a scientific hypothesis is not the same as a scientific theory.
Recently, remarkable progress has been made in the application of machine learning (ML) techniques (e.g., neural networks) to transformer fault diagnosis. However, the diagnostic processes employed by these techniques often suffer from a lack of interpreta ...
The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...
Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...