Black Boxes

A flight recorder is an electronic recording device placed in an aircraft for the purpose of facilitating the investigation of aviation accidents and incidents. Flight recorders are also known by the misnomer black box—they are in fact.
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What exactly do we mean by a model? Although perhaps somewhat vague, a statistical model basically captures the assumptions we make about how things work in the world, with details to be learned from data.

9/11 - What Happened to the Aircraft's Black Boxes?

In particular, a model specifies what the inputs are, what the outputs are, and typically how we think the inputs might interact with each other in generating the output. A classic example of a model is the equations which govern Newtonian gravity. The model states that the output the force of gravity between two objects is determined by three input values: More precisely, it states that gravity will be proportional to the product of the two masses, divided by the distance squared.

Of course, even if this were completely correct, in order to be able to make a prediction, we also need to know the corresponding scaling factor, G. In principle, however, it should be possible to learn this value through observation. If we have assumed the correct or close to correct model for how things operate in reality, we have a good chance of being able to learn the relevant details from data. In most circumstances, it gives approximately the same prediction as the Newtonian model would, but it is more accurate in extreme circumstances, and of course has been essential in the development of technologies such as GPS.

Even more impressively, the secondary predictions of relativity have been astounding, successfully predicting, for example, the existence of black holes before we could ever hope to test for their existence. Gravitation, of course, is deterministic as far as we know. In machine learning and statistics, by contrast, we are typically dealing with models that involve uncertainty or randomness. For example, a simple model of how long you are going to live would be to just predict the average of the population for the country in which you live.

A better model might take into account relevant factors, such as your current health status, your genes, how much you exercise, whether or not you smoke cigarettes, etc.

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In addition to being an incredibly successful rebranding of neural networks and machine learning itself arguably a rather successful rebranding of statistics , the term deep learning refers to a particular type of model, one in which the outputs are the results of a series of many simple transformations applied to the inputs much like our wiring diagram from above. Although deep learning models are certainly complex, they are not black boxes.

In fact, it would be more accurate to refer to them as glass boxes, because we can literally look inside and see what each component is doing. The problem, of course, is that these systems are also complicated. This is also true, though to a lesser extent, with a class of models known as linear models , where the effect of changing any one input can be interpreted without knowing about the value of other inputs. Deep learning models, by contrast, typically involve non-linearities and interactions between inputs, which means that not only is there no simple mapping from input to outputs, the effect of changing one input may dependent critically on the values of other inputs.

The actual computation performed by these models in making a prediction is typically quite straightforward; where things get difficult is in the actual learning of the model parameters from data. As described above, once we assume a certain form for a model in this case, a flexible neural network ; we then need to try to figure out good values for the parameters from data. With modern deep learning systems, by contrast, there can easily be millions of such parameters to be learned. In practice, nearly all of these deep learning models are trained using some variant of an algorithm called stochastic gradient descent SGD , which takes random samples from the training data, and gradually adjusts all parameters to make the predicted output more like what we want.

black box (black box testing)

Exactly why it works as well as it does is still not well understood, but the main thing to keep in mind is that it, too, is transparent. Because it is usually initialized with random values for all parameters, SGD can lead to different parameters each time we run it. The algorithm itself, however, is deterministic, and if we used the same initialization and the same data, it would produce the same result. In other words, neither the model nor the algorithm is a black box. Although it is somewhat unsatisfying, the complete answer to why a machine learning system did something ultimately lies in the combination of the assumptions we made in designing model, the data it was trained on, and various decisions made about how to learn the parameters, including the randomness in the initialization.

Why does all this matter? Well, there are at least two ways in which the concept of black boxes are highly relevant to machine learning. The Cockpit Voice Recorder is usually located in the tail of a plane. This piece of equipment is essential to the work of Air Crash Investigators as it records the many different operating functions of a plane all at once, such as the time, altitude, airspeed and direction the plane is heading. But these are just the primary functions of the recorder, in fact, modern Flight Data Recorders are able to monitor countless other actions undertaken by the plane, such as the movement of individual flaps on the wings, auto-pilot and fuel gauge.

The “black box” metaphor in machine learning – Towards Data Science

Information stored in the Flight Data Recorder of a plane that has crashed is invaluable for investigators in their search for determining what caused a specific crash. The data stored on the recorders helps Air Crash Investigators generate computer video reconstructions of a flight, so that they can visualise how a plane was handling shortly before a crash. As technology continues to develop it is likely that Black Boxes, or flight data recorders, will become more and more sophisticated and more reliable, giving Air Crash Investigators more to go on when painstakingly trying to piece together what caused a plane crash.

Potentially, the humble MP3 player — adored by music fans the world over - could become part of the flight data recording software. In , US light aircraft manufacturer LoPresti Speed Merchants announced that it planned to fully integrate the device as flight data recorder on all of its Fury piston aircraft. The company believes that if suitable software was used then MP3s would be capable of recording over hours of flight time data. National Geographic showcases leading explorers, scientists, environmentalists, film makers and renowned photographers.

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Flight tracking - Aviation industry 'not ahead of the game'

Free SMS and Email reminders so you never miss a show. Get notified when content that interests you is published. Predefined cases reduce test results variation, which leads to the minefield problem , also known as reduced application test coverage over time. Preplanned tests also prohibit the results from influencing what the tester will do next, commonly referred to as exploratory testing. Black box testing separates the tester from the code creator.

This software testing technique forces the team to see it from an outsider's view. The black box tester acts from the user's perspective. This creates both social distance and critical distance between software development and test, which makes it more likely that the tester will manipulate the application, referred to as the box, in a manner its creator had not considered.

Clear box testing requires setup and instrumentation, or at least poring over code, while most black box techniques can begin immediately; the operator simply tries to use the software. A system could behave correctly as a black box, but still contain defects in the code itself. In telecommunications, a black box is a resistor that connects to a phone line that makes it impossible for the telephone company's equipment to detect when a call has been answered.

In data mining, a black box is an algorithm or a technology that doesn't provide an explanation of how it works. In film making, a black box is a dedicated hardware device: In the financial world, a black box is a computerized trading system that does not make its rules easily available. Please check the box if you want to proceed.

How Black Boxes Work

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