With billions of books, news stories, and documents online, there's never been a better time to be reading -- if you have time to sift through all the options. "There's a ton of text on the internet," says Justin Solomon, an assistant professor at MIT. "Anything to help cut through all that material is extremely useful." With the MIT-IBM Watson AI Lab and his Geometric Data Processing Group at MIT, Solomon recently presented a new technique for cutting through massive amounts of text at the Conference on Neural Information Processing Systems (NeurIPS). Their method combines three popular text-analysis tools -- topic modeling, word embeddings, and optimal transport -- to deliver better, faster results than competing methods on a popular benchmark for classifying documents. If an algorithm knows what you liked in the past, it can scan the millions of possibilities for something similar.

A sharp uptake in technology designed to automate jobs and replace human workers is bringing new voices to this old debate. Today's society could be disastrously affected by artificial intelligence and growing automation, and scientists and technologists are looking for ways to stop that damage before it happens. The eyes of the tech industry are turning towards basic income, and the entire conversation is changing.