I worked on speech recognition with IBM Research for nearly six years. We participated in DARPA-sponsored research projects, fields trials, and actual product development for various applications: dictation, call centers, automotive, even a classroom assistant for the hearing-impaired. The basic story was always the same: get us more data! (data being in this case transcribed speech recordings). There is even a saying in the speech community: “there is no data like more data”. Some researchers have argued that most of the recent improvements in speech recognition accuracy can be credited to having more and better data, not to better algorithms.

This is actually a serious issue in one respect, since it shows just how important technology has become in shaping our impressions of the world around us. And that in turn points to how essential it is that we continue to scrutinize how well search-related technologies work and remain aware of those technologies’ inescapable limitations. Just as you shouldn’t believe everything you read on the Internet, you shouldn’t believe everything Siri tells you.

Siri’s unhelpful and sometimes misleading answers to pressing health questions stand in stark contrast to her prompt and accurate responses to inquiries about nearby escort services,” says Think Progress, while Slate goes even farther off the deep end and says, “many around the Web [are] wondering if Siri is pro-life and whether Apple is attempting to impose its morals upon the rest of us.”

This is a textbook example of sensationalistic media making something from absolutely nothing.

The goal of this article is to help you understand what a neural network is, and how it is used. Most people, even non-programmers, have heard of neural networks. There are many science fiction overtones associated with them. And like many things, sci-fi writers have created a vast, but somewhat inaccurate, public idea of what a neural network is.