Pressing, searching, and hunting for information is a thing of the past. Until recently, employees across industries had to scroll search engines, wait on co-worker responses, and scan through company memos and files just to find the answer to a simple question using NLQA. Specific machine learning and artificial intelligence techniques allow workers to proactively understand their information with the help of natural language question answering (NLQA). NLQA understands spoken or written verbiage to provide on-the-spot question answering. Subsets of NLQA, like natural language processing (NLP) and natural language understanding (NLU), have the ability to extract tone and intent behind all sorts of text.

When Kenneth Wayne Jennings, noted for holding the record for the longest winning streak of 74 games on the U.S. syndicated game show, bowed to IBM's Watson as the new "Jeopardy!" That was probably one small step for a computer but a giant leap for computing. It's ironic to say that Watson's dominance on the game show didn't come out of the blue. The result was a culmination of over a decade of IBM's research. "It opened up a new chapter in information technology called cognitive computing--based on the idea of a natural interaction between systems and people," says Zachary (Zach) Lemnios, vice president of strategy for IBM Research.