Developing an AI system with Markov chains

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Article from Issue 205/2017
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Markov chains model systems that jump from state to state with predetermined probabilities, but can they help write new columns like this one after learning from previously written articles?

Hard to believe, but true: This edition of my programming column marks its 20-year anniversary. Lately, I've been diving into artificial intelligence topics, a field that is increasingly replacing traditional jobs, but I wonder if a machine could possibly one day replace authors like me? An AI system would certainly have a number of advantages over a human writer, because a robot would no doubt deliver the manuscript on time every time, to the amazement of my editors, who are not fond of my bad habit of watching deadlines swoosh by. Also, I could afford to work less hard and have more time to kick back, relax, and spend my days pursuing my surfing hobby at various Pacific beaches (Figure 1).

Moody like the Weather

An algorithm that writes columns automatically could be implemented as a so-called Markov chain, named after the Russian mathematician Andrei Markov. Markov chains are stochastic processes with different states that change according to predetermined probabilities.

The weather forecasting model shown in Figure 2, for example, says the chance of a sunny day following a "Sun" state is 65 percent. At 30 percent, the weather will change to the "Clouds" state, and at 5 percent, it goes directly to the "Rain" state. The probabilities apply only to the current state; a change to the next state does not require knowledge of any event in the past, so the probability of a transition is very easy to calculate.

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