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← Information.9.TheManifoldThingsInformationMeasures

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Showing Revision 4 created 04/16/2017 by seonaid.

  1. I've told you a bit about information.
  2. Bits. Labels. Probabilities.
  3. I is equal to minus the sum over i of
    P sub i, log to the base 2 of P sub i
  4. The fundamental formula of
    information theory
  5. I told you about mutual information
  6. which is if I have two variables,
  7. such as the input and output to a channel.
  8. The mutual information tells you... is
  9. equal to the amount of information that is
  10. shared in common between input and output.
  11. It is the information
  12. that passes through
  13. or gets through the channel.
  14. And in fact, from Claude Shannon,
  15. it's actually equal to
  16. the practical channel capacity.
  17. Or if I take the input probabilities, or
  18. frequencies that maximize the mutual
  19. information, that mutual information is the
  20. rate at which information can be sent
  21. reliably down this channel. You cannot
  22. send information at a higher rate, and
  23. you can send information at that rate.
  24. This is a tremendously practical
  25. application of information theory. Because
  26. it tells us that if we have noisy channels
  27. or lossy channels, channels where we're
  28. using sound, channels where we're using light,
  29. chanels if we're using electromagnetic
  30. radiation, channels where we send information
  31. through the mail, any such channel has
  32. a capacity, and Shannon's theorem tells us
  33. what that capacity is, it tells us that we
  34. can't surpass it, and it tells us how to
  35. achieve it. And this is at the basis of
  36. the application of information theory to
  37. practical communications, for instance via
  38. fiber-optic cables.
  39. So, there are some fun examples of this.
  40. A nice way to look at this picture is that
  41. here we have this channel. We have x in...
  42. we have P of x sub i. Here we have
  43. the output. We have P of y sub j,
    y out, given x sub i in.
  44. And then we have the associated mutual
  45. So here we have I(x), this is the
    information in.

  46. Here we have I(y), this is the
    information that comes out.
  47. The information that goes through
  48. the channel like this is the mutual
  49. information between the input and
  50. the output. We can also look at
  51. some things that I'm going to call "loss,"
  52. and another thing that I'm going to call
  53. "noise." So, what is loss? Loss is
  54. information that goes into the channel,
  55. but does not come out. Like the roaches
  56. going into a roach motel. So, what is that?
  57. It's information that we don't know about
  58. the input, given that we know the output.
  59. So, if we know the output, this is
  60. residual stuff that went in, bits that
  61. went in, that never came out. Similiarly,
  62. the noise is information that came out
  63. that didn't go in. So noise is stuff where
  64. if we know exactly what went in, it's
  65. residual bits of information that came
  66. out that had no explanation in terms of
  67. what went in. So we have a nice picture
  68. in terms of the whole set of processes
  69. that are going on in information. We have
  70. the information going in, we have the
  71. information going out. We have the loss,
  72. which is information that goes in that
  73. doesn't come out. We have noise, which is
  74. informaiton that came from nowhere
  75. that didn't go in - of course, it actually
  76. comes from physical processes. And finally
  77. we have the mutual information, which is
  78. the information that actually goes through
  79. the channel and that represents the
  80. channel capacity.
  81. So, I also talked a bit about computation.
  82. So, if you have a digital computer. Here is
  83. what digital computers looked like when
  84. I was a kid... You had, like, a tape thing,
  85. you had a bunch of little lights on the
  86. front and switches, and then you
  87. read the tape, and then it spewed out some
  88. output, maybe on some paper tape -
    you could even
  89. put some input on paper tape - it would
  90. have some memory like this. All a digital
  91. computer is doing is
  92. breaks up information
  93. into bits which are the smallest chunks of
  94. information, typically called 0 and 1, or
  95. true and false, in a digital computer.
  96. And then flips those bits
  97. in a systematic fashion.
  98. So for all their power and
  99. all their stupidity, all that these
  100. digital computers that we have, including
  101. things like our smart phones, as well as
  102. our desktops and supercomputers, all
  103. they're doing is registering and storing
  104. information as bits and then flipping
  105. those bits in a systematic fashion.
  106. And let me just remind you about this
  107. fundamental theorem about computation
  108. which is that any digital computation
  109. can be written in some kind of
    circuit diagram.
  110. Here's x, here's y, here's z. Here's
  111. something where I make a copy of x,
  112. I take an OR gate... This is "OR",
    you will recall.
  113. Here's a copy of X, here's X here.
  114. This is X or Y.
  115. Also known as X or Y.
  116. And here i can say
  117. for example, take an AND gate, and
  118. I can here send this through a NOT gate
  119. And then I can combine them in another
  120. AND gate, And in the end, I think that
  121. what I have is NOT X AND Z AND
    (X OR Y).
  122. So, when I have a digital computer,
  123. what happens is that it takes bits of
  124. information, it performs simple AND, OR,
    NOT, and copy operations, and
  125. by doing these sequentially, in whatever
  126. order you wanted to do it, you end uo
  127. evaluating arbitrary logical expressions...
  128. NOT X and Z AND X or Y... whatever
  129. that means, I have no idea what it means.
  130. But it is what it is, it means what it is.
  131. So, if we talk about digital computation,
  132. all digital computers are is taking
  133. information and processing it.
  134. And if we put together computation
  135. and communication,
  136. and probabilities,
  137. what we find is that taking together
  138. the idea of information, processing
    information as computation,
  139. sending information reliably from
  140. one place to another is communication
  141. this information refers at bottom to the
  142. probabilities of events... being sunny,
  143. being rainy. Probability that a photon
  144. going into a channel makes it out the
  145. other side. Probability of 0,
  146. probability of 1, probability of heads,
  147. probability of tails... but when we put
    together these three pieces
  148. interlocking, what we get is the theory
  149. of information.
  150. And I hope that in the course
  151. of these brief lectures here, I've been
  152. able to convince you that these remarkable
  153. processes that are going on all
  154. around us, the fault, or result of the
  155. information processing revolution that began
  156. in the mid-twentieth century and continues
  157. in fact, continues at an accelerating rate
  158. to this day, can be understood with
  159. a simple set of mathematical ideas that
  160. are interlinked with each other, and give
  161. a set of ideas of very profound richness
  162. and impact on human society with
  163. implications for... I don't know what!
  164. Thank you for your attention,
    Do well on the homework,
  165. Exam will be multiple choice, I am sure
  166. you will all do well.