How a computer sees history after "reading" 35 million news stories

[TheWorldNewsOrg]

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So far, humans have relied on the written word to record what we know as history. When artificial intelligence researchers ran billions of those words from decades of news coverage through an automated analysis, however, even more patterns and insights were revealed.

A team from the University of Bristol ran 35 million articles from 100 local British newspapers spanning 150 years through both a simple content analysis and more sophisticated machine learning processes. By having machines “read” the nearly 30 billion words, the simple analysis allowed researchers to easily and accurately identify big events like wars and epidemics.

Similar systems have allowed computers to learn visually about art and even argue a topic.

Perhaps most interesting, the techniques also allowed the researchers to see the rise and fall of different trends during the study range from the years 1800 - 1950. For example, they could track the decline of steam and corresponding rise of electricity – the opposing trajectories crossed each other in 1898. Similarly, they saw when trains overtook horses in popularity in 1902.

World News