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“When it comes to machine learning, things can get a little spooky: We know an algorithm can solve a problem, but we often don’t know exactly how.
Today’s machine-learning algorithms are considered a form of artificial intelligence, but it’s more helpful to think of them as prediction algorithms: Based on movies that this customer has rated highly, how much do we think he will like this other movie? Based on people we have hired in the past, how likely are we to hire this job candidate? Based on a list of past Halloween costumes, what might humans dress up as this year?
Hoping to find an answer to that last question, we turned to a machine-learning algorithm called textgenrnn that can learn to imitate text. Its author, Max Woolf, designed it as a blank slate ready to learn any kind of text; the text we gave it to imitate was a list of 7,182 costumes that people sent to aiweirdness.com over a year. Here are some examples of what that algorithm produced.
The algorithm learned to spell all of these words and phrases without human intervention, just by looking at the costume examples we gave it. It starts by making predictions about which letters should be used in which order to make a Halloween costume, and then it checks its own predictions by looking at the data used to train it. If it’s wrong (and at first, it almost always is), it refines its internal structure. Gradually, its predictions get better.”
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