Think about a steam engine. You can't do the process backwards, any more than you could make a broken glass jump off the floor and reassemble itself on a table again. In information theory, the more possible messages you could be receiving, the more uncertainty you have about what actual message you're getting. If you had to track the motion of each atom in the piston of an engine, you could just say they're all moving in the same direction. If you had to track the motion of each atom in the hot exhaust, you would be stuck with lots more information.
More information means more entropy.
Like Boltzmann arond the turn of the century, Shannon took the broad observations people had had about information exchange and put them on a solid mathematical, statistical footing. Information theory has been important for the statistical analysis of languages, and especially for cryptography. Shannon's precisely defined entropy allows you to measure how many possible decodings there could be for a message. If you are designing a program to encrypt secret messages, you want to get more entropy, since that means anyone who wanted to break the encryption would have to work through that many more possible messages. Researchers have tried to apply information theory in many other fields.