WOLFRAM NOTEBOOK

Exploring Digital Information Technologies: Lecture 1- Part 2

The Landscape—Information and Computation

Encoding Digital Information

​​Picturecourtesy:https://pxhere.com/en/photo/1458883
Digital information is data encoded into symbols.
Most common form of digital data is the Binary Digit or Bit (it stores a teeny bit of information).
A bit can either be 0 or 1.

How is information coded?

Question: What is your favorite color?
Answers:

Encoding color information using numbers

The answers are information about us.
Here are a few colors:
Out[]=
We can use a number to represent each color.
There are 9 colors, so the numbers 0-8 can be used:
Out[]=
0
,1
,2
,3
,4
,5
,6
,7
,8

Encoding with bits

However with bits you have only 0 and 1 to encode all the information. Why just 0 and 1?

Information is Stored Physically

How do we store information?

In a digital system, information is stored using physical quantities such as:
◼
  • voltage,
    • ◼
  • crystal structure, or
    • ◼
  • magnetic field.

    • Storing Information Physically

    Physical storage systems typically have two states: off or on OR which we can call 0 and 1.
    That gives us a binary digit, or bit.

    How do we measure information?

    Information is measured by how many bits are needed to store it.

    How is information coded into bits?

    Out[]=
    0
    ,1
    ,2
    ,3
    ,4
    ,5
    ,6
    ,7
    ,8
    How many bits would we need to store 9 different numbers? Let’s start small

    1 Bit

    2 Bits

    With 2 bits we can store 4 numbers (or colors or names or 4 of anything):
    Out[]=

    Numbers

    Colors

    Mickey and Friends

    3 Bits and 4 Bits

    From Bits to Decimal Numbers and Back

    You don't have to do the math but you must understand how it works.

    How many different things can we represent with “n” bits?

    How many bits do we need to represent “n” different things?

    Encoding Images

    What to do with lots of bits?

    Compression

    What is Computation?

    Travel Directions

    Identifying Images

    Social Network Analysis

    Church-Turing Hypothesis

    Terminology You Should Know from this Lecture

    Concepts You Should Know from this Lecture

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