This Scientist Uses The New York Times Archive To Eerily, Accurately Predict The Future

Kira Radinsky has written an algorithm that dissects old news stories and other Internet postings to look for past cause and effect, and then can alert us to possible disasters, geopolitical events, and disease outbreaks.

The New York Times might be a widely respected chronicler of past events, but can we use it to divine the future? Kira Radinsky, a 27-year-old Israeli computer prodigy dubbed the “web prophet” says yes.

Radinsky, who appeared this year on MIT’s prestigious list of top 35 inventors under the age of 35 (previous winners include the likes of Mark Zuckerberg, Larry Page, and Sergey Brin), and who started university at the age of 15 and received her Ph.D. in computer science at 26, has developed a unique system which she claims has already predicted the first cholera epidemic in Cuba in many decades, many of the riots that started the Arab Spring, and other important world events.

The complex computer algorithms she wrote collect immense volumes of electronic data–most notably several decades of New York Times archives but also anything from Twitter feeds to Wikipedia entries–and processes it to extract little-known cause and effect patterns that can be used to predict future events.

For example, she says, “If a storm comes two years after a drought, a few weeks [after the storm] the probability of a cholera outbreak is huge, especially in countries with low GDP and low concentration of clean water.”

This may seem fairly intuitive–people have been making similar predictions for thousands of years–but getting a computer to do it, and to analyze accurately the massive amounts of electronic data present on the web, is another matter. Even simple examples present complex challenges.

As an example, she points to a hypothetical headline that says there is an earthquake in Australia. “You want to predict what’s going to happen [next] so you look up your database and see that there was an earthquake in Turkey,” she said. “You need [your program] to understand that Turkey and Australia are [different] countries. So after an earthquake in Turkey, the Red Cross sent help to Ankara. But to predict that an earthquake in Australia would result in the Red Cross sending help to Ankara is incorrect … so [you] have to build a different function from the data to fix that.”

It all started as a game, Radinsky says, when she was playing with Google Trends in 2007 (Google Trends a public web facility that analyses the volume of searches for a particular query). She quickly found out that she could predict some of what people would search for–such as hurricanes–based on news reports of recent world events. Then she asked herself if she could adapt this mechanism to predict future developments.

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