Everyone wants to predict who will win the 2020 presidential election. Here are 2 misconceptions to bust so people don't proclaim the death of data like they did in 2016.
Predictive policing introduces a scientific element to law enforcement decisions, such as whether to investigate or detain, how long to sentence, and whether to parole.
Prediction is reinventing industries and running the world. More and more, predictive analytics drives commerce, manufacturing, healthcare, government, and law enforcement.
You have been predicted — by companies, governments, law enforcement, hospitals, and universities. In this lesson excerpt from Big Think Edge, Eric Siegel, author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, explains why these entities not only have the power to predict the future "but also to influence the future." \r\n
As with the anecdotally rich discoveries in Freakonomics, practitioners of predictive analytics constantly stumble upon insightful gems such as,vegetarians miss fewer flights.
Siri’s underlying technology is designed "to solve a different, simpler variant of the human language problem" than Watson.
My opinion is that IBM’s Watson computer is able to answer questions, and so, in my subjective view, that qualifies as intelligence.
Today, predictive analytics' all-encompassing scope already reaches the very heart of a functioning society. Several mounting ingredients promise to spread prediction even more pervasively: bigger data, better computers, wider familiarity, and advancing science.
The president won reelection with the help of the science of mass persuasion, a very particular, advanced use of predictive analytics.
Eric Siegel, Ph.D. is the founder of the Predictive Analytics World conference series—which includes events for business, government, healthcare, workforce, manufacturing, and financial services—the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die—Revised and Updated Edition (Wiley, January 2016), executive editor of The Predictive Analytics Times, and a former computer science professor at Columbia University. For more information about predictive analytics, see the Predictive Analytics Guide.