Tuesday, April 16, 2024 Text is available under the CC BY-SA 3.0 licence.

Nick Bostrom

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Knowledge about limitations of your data collection process affects what inferences you can draw from the data.
--
Bostrum's Anthropic Principle Primer

 
Nick Bostrom

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A person not familiar with all of the special knowledge about a particular instrument should not try to draw too many conclusions from printed data. Such data typically contains certain assumptions about the equipment not necessarily known to outsiders.

 
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We are thus led to a somewhat vague distinction between what we may call "hard" data and "soft" data. This distinction is a matter of degree, and must not be pressed; but if not taken too seriously it may help to make the situation clear. I mean by "hard" data those which resist the solvent influence of critical reflection, and by " soft " data those which, under the operation of this process, become to our minds more or less doubtful.

 
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Incomplete knowledge of the future, and also of the past of the transmitter from which the future might be constructed, is at the very basis of the concept of information. On the other hand, complete ignorance also precludes communication; a common language is required, that is to say an agreement between the transmitter and the receiver regarding the elements used in the communication process...
[The information of a message can] be defined as the 'minimum number of binary decisions which enable the receiver to construct the message, on the basis of the data already available to him.' These data comprise both the convention regarding the symbols and the language used, and the knowledge available at the moment when the message started.

 
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…git actually has a simple design, with stable and reasonably well-documented data structures. In fact, I'm a huge proponent of designing your code around the data, rather than the other way around, and I think it's one of the reasons git has been fairly successful […] I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.

 
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While the creative works from the 16th century can still be accessed and used by others, the data in some software programs from the 1990s is already inaccessible. Once a company that produces a certain product goes out of business, it has no simple way to uncover how its product encoded data. The code is thus lost, and the software is inaccessible. Knowledge has been destroyed.

 
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