I originally published this article in Big Think. The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.
Chatting with your Computer: How the iPhone's Siri Compares with IBM'S Watson
IBM's Watson computer, which defeated the two all-time human champs on the TV quiz show Jeopardy! in 2011, is a glowing example of the heights achievable by predictive analytics. This is a machine that answers questions—about any of a broad, open range of topics. The same core technology that companies use to predict whether you'll buy and which ad you'll click is employed under Watson's hood to predict, given a question, whether a candidate answer is correct. With this capability in place, Watson can "cast a wide net" by collecting thousands of candidate answers for a question, and then narrow down to the correct answer by predicting for each, "Is this the right answer?"
But, given that many of us have Siri, the iPhone's eager-to-please personal assistant, right in our pocket, what's so special about IBM's one-of-a-kind, multi-refrigerator-sized monstrosity that cost tens of millions of dollars to build? How do the two compare?
First introduced as the main selling point to distinguish the iPhone 4S from the preceding model, Siri responds to a broad, expanding range of voice commands and inquiries directed toward your iPhone.
Siri handles simpler language than Watson does: Users tailor requests for Siri knowing that they’re speaking to a computer, whereas Watson fields Jeopardy!’s clever, wordy, information-packed questions that have been written with only humans in mind, without regard or consideration for the possibility that a machine might be answering. Because of this, Siri’s underlying technology is designed to solve a different, simpler variant of the human language problem.