Here Be Monsters

How does a computer learn to speak with emotion and conviction? Language is hard to express as a set of firm rules.  Every language rule seems to have exceptions and the exceptions have exceptions etcetera.  Typical, “if this then that” approaches to language just don’t work.  There’s too much nuance. But each generation of algorithms gets closer and closer. Markov chains were invented in the 1800’s and rely on nothing more than basic probabilities.  It’s a simple idea, just look at an input (like a book), and learn the order in which words tend to appear.  With this knowledge, it’s possible to generate new text in the same style of the input, just by looking up the probability of words that are likely to follow each other.  It’s simple and sometimes half decent, but not effective for longer outputs as this approach tends to lack object permanence and generate run-on sentences. Markov models are  used today in predictive text phone keyboards, but can also be used t