![]() ![]() For instance, Google’s PageRank algorithm relies on a really complex system of Markov chains, according to Hayes.īut Markov chains aren’t just essential to the internet: they’re on the internet for entertainment purposes as well. They “help identify genes in DNA and power algorithms for voice recognition and web search,” he writes. “Markov chains are everywhere in the sciences today,” writes Brian Hayes for American Scientist. That’s partly because the user, not the algorithm, picks the next step in the chain.Ī "true" Markov chain would calculate what you are going to type next based on the last thing you typed, without any human input (kind of like when you play the " middle-button game," hitting the next suggested prediction mindlessly until the computer generates a "sentence" of sorts). The phone knows what you just typed and makes an educated guess about what you want to say next based on the probability of certain words appearing next to each other.Īlthough the algorithm that powers cell phone predictive text relies on some of the ideas behind Markov chains, it’s more complex than what’s being discussed here. As an example, consider how your iPhone can predict what you’re going to type next. The brainchild of Andrey Markov–who was himself born on this day in 1856–Markov chains are a way of calculating probability. ![]() Some of the algorithms that underlie commonplace technology today have their roots in the nineteenth century–like the Markov chain. Thank Andrey Markov for your smartphone's predictive text feature-and also somewhat sillier uses. ![]()
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