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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.
The understanding of a black box is based on the \"explanatory principle\", the hypothesis of a causal relation between the input and the output. This principle states that input and output are distinct, that the system has observable (and relatable) inputs and outputs and that the system is black to the observer (non-openable).
Black box theories are those theories defined only in terms of their function. The term can be applied in any field where some inquiry is made into the relations between aspects of the appearance of a system (exterior of the black box), with no attempt made to explain why those relations should exist (interior of the black box). In this context, Newton's theory of gravitation can be described as a black box theory.
Specifically, the inquiry is focused upon a system that has no immediately apparent characteristics and therefore has only factors for consideration held within itself hidden from immediate observation. The observer is assumed ignorant in the first instance as the majority of available data is held in an inner situation away from facile investigations. The black box element of the definition is shown as being characterised by a system where observable elements enter a perhaps imaginary box with a set of different outputs emerging which are also observable.
In humanities disciplines such as philosophy of mind and behaviorism, one of the uses of black box theory is to describe and understand psychological factors in fields such as marketing when applied to an analysis of consumer behaviour.
After talking with his friend, Dr. Gary Yeboah, Nolan ultimately decides to opt for an experimental procedure that might help him get his memory back by enlisting the help of Dr. Brooks, a neurologist at the hospital he was first brought to after the accident. After using hypnosis, Dr. Brooks explores Nolan's mind and deems him a suitable subject for her \"black box\" treatment, saying that together they can try to regain his memory.
Dr. Brooks reveals that Thomas died some time previously, but before he died she had mapped out his consciousness and uploaded it to the black box, so she could download his consciousness into the suitable host when one arrived. Thomas leaves, pretending to still be a Nolan, but is struggling with this new knowledge. Eventually, he leaves Ava with Dr. Yeboah, as he says he no longer trusts himself. Thomas seeks out his wife, and tries to explain to her that he is back, but finds that she has erased all traces of him, and does not want him in her life.
It appears that Thomas has let go of his hold on Nolan, as we see Nolan, Ava, and Dr. Yeboah leave, but Thomas's exact fate is left unknown. Dr. Brooks is then shown repairing the black box and trying to run Thomas's mapped consciousness, which seems to work, as she looks into the black box, says his name, and smiles.
The use of the black box model in psychology can be traced to B.F. Skinner, father of the school of behaviorism. Skinner argued that psychologists should study the brain's responses, not its processes.
The user of the black box can understand the results but cannot see the logic behind them. When machine learning techniques are used in the model's construction, the inputs are in fact too complex for a human brain to interpret.
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TIM: Okay, I'll do that. Okay, let me do it one more time. Three, two, one. This is Tim Howard, and today on Radiolab we've been talking about black boxes. And the next story started with a radio piece that I heard at the Third Coast International Audio Festival. There were a lot of incredible stories, but there was this one called Keep Them Guessing that I just loved and I couldn't get it out of my head. So I sat Jad and Robert down in our little black box of a studio.
MOLLY: It was like a Dr. Seuss-ian land of butterflies, but I was there to look at the moment right before they become butterflies, which remains one of the most mysterious black boxes in nature. What I'm talking about is something called ...
Dean Pomerleau can still remember his first tussle with the black-box problem. The year was 1991, and he was making a pioneering attempt to do something that has now become commonplace in autonomous-vehicle research: teach a computer how to drive.
Several groups began to look into this black-box problem in 2012. A team led by Geoffrey Hinton, a machine-learning specialist at the University of Toronto in Canada, entered a computer-vision competition and showed for the first time that deep learning's ability to classify photographs from a database of 1.2 million images far surpassed that of any other AI approach1.
Using techniques that could maximize the response of any neuron, not just the top-level ones, Clune's team discovered in 2014 that the black-box problem might be worse than expected: neural networks are surprisingly easy to fool with images that to people look like random noise, or abstract geometric patterns. For instance, a network might see wiggly lines and classify them as a starfish, or mistake black-and-yellow stripes for a school bus. Moreover, the patterns elicited the same responses in networks that had been trained on different data sets3.
If you're a vehicle owner and happen to have a car accident in the near future (we hope you don't), it's likely the crash details will be recorded. Automotive \"black boxes\" are now built into more than 90 percent of new cars, and the government is considering making them mandatory.
\"They could do something like put a notification in the owner's manual saying that the driver has a reasonable expectation of privacy in that black box data. We think that would go a long way towards making the issue of who owns that data a lot more clear,\" Cardozo says.
Design and setting: Examination of the Physicians' Desk Reference for all new chemical entities approved by the US Food and Drug Administration between 1975 and 1999, and all drugs withdrawn from the market between 1975 and 2000 (with or without a prior black box warning).
Results: A total of 548 new chemical entities were approved in 1975-1999; 56 (10.2%) acquired a new black box warning or were withdrawn. Forty-five drugs (8.2%) acquired 1 or more black box warnings and 16 (2.9%) were withdrawn from the market. In Kaplan-Meier analyses, the estimated probability of acquiring a new black box warning or being withdrawn from the market over 25 years was 20%. Eighty-one major changes to drug labeling in the Physicians' Desk Reference occurred including the addition of 1 or more black box warnings per drug, or drug withdrawal. In Kaplan-Meier analyses, half of these changes occurred within 7 years of drug introduction; half of the withdrawals occurred within 2 years.
The black box issue with AI is not just limited to the medical field. A June 2020 International Data Corporation report showed that 43% of business leaders believe explainability is an important factor in deploying AI. In order to allow AI to reach its full potential, we need to either open up the so-called the black boxes or develop other methods to ensure responsible AI development to cultivate trust.
As more black box models are being created and implemented, there is a growing need for stakeholders to better understand the thought process of these models, which has led to the rise of explainable AI, or XAI. Research in the XAI field focuses on both building transparent models by design and post-hoc explainability models. For the purpose of this blog, the focus will be on post-hoc explanations, which is currently the more widely-used method. 59ce067264