The developments in data science are very important, but it’s just as important to recognize the limits and the lags involved. The field will be damaged if people overhype it too much, or expect too much too soon. That has often happened with artificial intelligence (see Nils Nilsson’s history of the field, The Quest for Artificial Intelligence.)
People usually get overconfident about new techniques. As one Stanford expert put it in the MIT Technology Review a few weeks ago,
For the most part, the AI achievements touted in the media aren’t evidence of great improvements in the field. The AI program from Google that won a Go contest last year was not a refined version of the one from IBM that beat the world’s chess champion in 1997; the car feature that beeps when you stray out of your lane works quite differently than the one that plans your route. Instead, the accomplishments so breathlessly reported are often cobbled together from a grab bag of disparate tools and techniques. It might be easy to mistake the drumbeat of stories about machines besting us at tasks as evidence that these tools are growing ever smarter—but that’s not happening.