A Brief History of Markov Chains
One of the most common simple techniques for generating text is a Markov chain. The algorithm takes an input text or texts, divides it into tokens (usually letters or words), and generates new text based on the statistics of short sequences of those tokens. If you’re interested in the technical details, Allison Parrish has a good tutorial on implementing Markov chains in Python.
This technique has been around for a very long time, but it’s slightly unclear when it was first developed. The following is a slightly sketchy work-in-progress timeline of what I’ve been able to work out.
My goal here is to work out when was the first person to:
- implement Markov chains on a computer
- with probabilities derived from a source text
- to generate new text
- for creative purposes
