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<blockquote data-quote="Epic Meepo" data-source="post: 9275737" data-attributes="member: 57073">Computer scientists like to use terms like &quot;black box&quot; and &quot;hidden layers&quot; as technical jargon. Personally, I think it would be more accurate to use terminology from chaos theory: every sufficiently complex computer program is a chaotic system. Roughly speaking, a chaotic system is one whose end state can change dramatically if you make even small changes to its initial state.If you have perfect knowledge of a chaotic system's initial conditions, you can perfectly predict the end state. But you can't use that knowledge to perfectly predict what would happen if you made even a small change to the initial conditions. The only way to perfectly predict the outcome you would get by changing the initial conditions is to go back and recalculate your prediction from scratch using the new initial conditions.If we're talking about a computer program, recalculating your prediction about the end state from scratch is identical to just running the program and seeing what happens; in other words, brute force. To make meaningful predictions about large numbers of multiple end states without resorting to brute force calculations, you have to take shortcuts and make predictions based on what's statistically likely to happen given your understanding of the system.That statistical modeling is the &quot;science&quot; part of computer science. Presumably, that's also the source of all the &quot;black box&quot; and &quot;hidden layers&quot; jargon. The actual behavior of the software is one step removed from the simplified model you are using to predict its behavior. Since you can't realistically perform brute force calculations to determine every possible outcome, you must study patterns and make testable hypothesis about what the software is most likely to do.</blockquote>

[QUOTE="Epic Meepo, post: 9275737, member: 57073"] Computer scientists like to use terms like "black box" and "hidden layers" as technical jargon. Personally, I think it would be more accurate to use terminology from chaos theory: every sufficiently complex computer program is a [I]chaotic system[/I]. Roughly speaking, a chaotic system is one whose end state can change dramatically if you make even small changes to its initial state. If you have perfect knowledge of a chaotic system's initial conditions, you can perfectly predict the end state. But you can't use that knowledge to perfectly predict what would happen if you made even a small change to the initial conditions. The only way to perfectly predict the outcome you would get by changing the initial conditions is to go back and recalculate your prediction from scratch using the new initial conditions. If we're talking about a computer program, recalculating your prediction about the end state from scratch is identical to just running the program and seeing what happens; in other words, brute force. To make meaningful predictions about large numbers of multiple end states without resorting to brute force calculations, you have to take shortcuts and make predictions based on what's statistically likely to happen given your understanding of the system. That statistical modeling is the "science" part of computer science. Presumably, that's also the source of all the "black box" and "hidden layers" jargon. The actual behavior of the software is one step removed from the simplified model you are using to predict its behavior. Since you can't realistically perform brute force calculations to determine every possible outcome, you must study patterns and make testable hypothesis about what the software is most likely to do. [/QUOTE]

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