Thank August to raise the subject, that is quite correct.
I like just to add that a measure in Shannon sense is not a physical measure, but a function with a certain type of property defined in a probability space.
It took me some monthes to realize that that was the obstacle for me to get Shannon's theorem...
"measure" as like in Measure theory: cfr. for ex. Kolmogorov and Fomin.
Now I am far from my library... i will be more precise on the book title...

f


On Aug 15, 2012, at 4:45 PM, august wrote:

>
>> August -- I definitely had Shannon, but hadn't added it to that library
>> yet... I'll cover coding, to be sure, from linguistic, technical,
>> control, and social points-of-view, though we don't have the time to go
>> into it (it's a seminar versus studio course -- I'd like it better to be
>> doing things as well as talking about things!) (I'm intrigued about your
>> statement that the (function?) of coding-as-information is
>> counter-intuitive/directly at odds with a normative definition of
>> information -- can you expand on that?)
>>
>
> Basically, the layman definition of information tends to define it as
> the communicable signal that rises above the noise (randomness) and
> carries meaning.
>
> According to Shannon's narrow definition, information is a _measure_ of
> entropy. In other words, information is not the signal in a noisy
> channel, but a measure of the signal's content. It's a quantity not a
> thing. The more information in a signal means more randomness (noise)
> in a signal. A signal with lots of redundancy has little information.
>
> Shannon is also careful to unhinge "meaning" from his concept of
> "information".
>
> It's more complicated, subtle, and interesting than what I describe
> above, but that's the gist. Most of our communication technologies
> depend on these ideas.
>
> The stuff on coding theorem and stochastic signals is what I find most
> interesting. What makes a large part of his information theory work is
> that most "human" signals (music, writing, etc.) are stochastic;
> non-deterministic but statistically predictable.
>
> One interesting thing regarding stochastic signals is that you can
> remove parts of them and still send enough communication for it to be
> "understood". Eg: I cn wrt t y wtht vwls nd y shld b ble t ndrstnd
>
> zzzzt.
>
> best -august.
>
>
>
> --
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