Intuition for information theory

In summary, the conversation discusses information theory and the counter-intuitive logic behind it. It is explained that each symbol in English carries about 6 bits of information, but when combining symbols, the total amount of information cannot simply be summed up. This is due to the principle of Shannon Information Theory, which states that a maximum of 12 bits of information can be contained in a 2 symbol message. The conversation also touches on the concept of entropy in English messages and the role it plays in data compression. Ultimately, it is concluded that the understanding of information in this context is not equivalent to meaning, but rather refers to randomness.
  • #1
nigels
36
0
Hi, although I've studied info theory briefly in the past, now revisiting it, I seem to be scratching my head trying to understand the counter-intuitive logic of it.

For instance,

I understand that

the amount of uncertainty associated with a symbol is correlated with the amount of information the symbol carries. Hence, each symbol in English (64 upper- and lowercase characters, a space, and punctuation marks) carries about 6 bits worth of information (2^6=64). However, the textbook also says that "no more than this amount of info can be contained in an English message, because we can in fact encode any such message in binary form using 6 binary digits per symbol".

What this means is that, in a 2 symbol message, if each message contains 6 bits of info, I can't sum the two up and say the whole message contains 12 bits of info. Why is this? What part of my intuition needs to be tweaked? How should I think about this in general?

Thank you very much for your help.
 
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  • #2
nigels said:
What this means is that, in a 2 symbol message, if each message contains 6 bits of info, I can't sum the two up and say the whole message contains 12 bits of info. Why is this? What part of my intuition needs to be tweaked? How should I think about this in general?

Why can't you sum them up?

2^6 * 2^6 = 2^12

It's simple combinatorics. If you have a six bits you have 64 possible values for each character "slot". With two slots you have -- 64 in the first and 64 in the second -- 64*64 possible combinations. Shannon Information Theory then says that you have a maximum of 12 bits of information.

The interesting thing is to then compare that value to the probabilities from the stream of characters you are _actually_ getting to see if your system has any (non-random) order to it. The funny thing is that "Information" should have been "Randomness" in this usage. This leads to the confusing thing that folks interpret "Information" to be "Meaning", but it's the opposite...
 
  • #3
nigels said:
"no more than this amount of info can be contained in an English message, because we can in fact encode any such message in binary form using 6 binary digits per symbol".

What is meant here is that no more than 6-bits PER SYMBOL can be contained in an English message, not 6-bits total.

(What I find more interesting is the amount of entropy in an English message; that is, how there is really a lot less than 6-bits of info carried by each symbol in a message ON AVERAGE. This is because there are common patterns that repeat with high probability. For example, say you have just read a "t"; you would not be terribly surprised to read an "h", since so many words contain the "th" combination. However, you would be surprised if an "m" followed a "t". I find that amusing anyways.)
 
  • #4
navaburo said:
(What I find more interesting is the amount of entropy in an English message; that is, how there is really a lot less than 6-bits of info carried by each symbol in a message ON AVERAGE. This is because there are common patterns that repeat with high probability. For example, say you have just read a "t"; you would not be terribly surprised to read an "h", since so many words contain the "th" combination. However, you would be surprised if an "m" followed a "t". I find that amusing anyways.)

That is, in fact, the key to data compression...
 
  • #5
Thank you all for the wonderfully helpful response! It all makes sense now. :)
 

1. What is intuition for information theory?

Intuition for information theory is the understanding and perception of how information is transmitted, stored, and processed in various systems. It involves concepts such as entropy, information encoding, and communication channels.

2. Why is intuition for information theory important?

Intuition for information theory is important because it helps us make sense of complex systems and processes by quantifying and measuring the amount of information involved. It also allows us to design efficient communication systems and improve data storage and retrieval methods.

3. How is information measured in information theory?

Information is measured in information theory using a unit called bits. A bit is the amount of information needed to represent one of two equally likely outcomes in a binary system. It can also be measured in other units such as nats, shannons, or bytes, depending on the context and application.

4. What is entropy in information theory?

Entropy is a measure of the uncertainty or randomness in a system. In information theory, it represents the amount of information contained in a message or signal. The higher the entropy, the more unpredictable the system is, and the more information is needed to describe it.

5. How is intuition for information theory applied in real-world scenarios?

Intuition for information theory is applied in various fields, such as computer science, telecommunications, data analysis, and machine learning. It is used to optimize data compression and transmission, improve error correction methods, and analyze and interpret large datasets to extract meaningful information.

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