The Science of Information: From Language to Black Holes

Accompany the young Claude Shannon to the Massachusetts Institute of Technology, where in 1937 he submitted a master's thesis proving that Boolean algebra could be used to simplify the unwieldy analog computing devices of the day. Drawing on Shannon's ideas, learn how to design a simple electronic circuit that performs basic mathematical calculations.

How is information measured and how is it encoded most efficiently? Get acquainted with a subtle but powerful quantity that is vital to the science of information: entropy. Measuring information in terms of entropy sheds light on everything from password security to efficient binary codes to how to design a good guessing game.

Intuition says we measure information by looking at the length of a message. But Shannon's information theory starts with something more fundamental: how surprising is the message? Through illuminating examples, discover that entropy provides a measure of the average surprise.

Probe the link between entropy and coding. In the process, encounter Shannon's first fundamental theorem, which specifies how far information can be squeezed in a binary code, serving as the basis for data compression. See how this works with a text such as Conan Doyle's The Return of Sherlock Holmes.