The Wolfram Language provides tools for programmatic handling of free-form input. For example, Interpreter, which was introduced in Version 10.0, converts snippets of text into computable Wolfram Language expressions. In smart form fields, this functionality can automatically translate input like "forty-two" into a Wolfram Language expression like "42." But what does it take to perform more complicated operations or customize responses and actions? For that you need a grammar. The grammar indicates the structure that should be matched and the action that should be taken using information extracted from the match. A grammar gives you natural language control over your computer so that you can process language snippets to yield functions that perform commands. For example, telling your computer to "open a website" requires mapping snippets like "open" and "a website" to the Open command and the URL of a website.

The first gauge I remember was a blue wrist watch I received from my parents as a child. Their hope was probably to correct my tardiness, but it proved valuable for more important tasks such as timing bicycle races. Today digital gauges help us analyze a variety of data on smart phones and laptops. Battery level, signal strength, network speed, and temperature are some of the common data elements constantly monitored.

Since the inception of Wolfram|Alpha, Wikipedia has held a special place in its development pipeline. We usually use it not as a primary source for data, but rather as an essential resource for improving our natural language understanding, particularly for mining the common and colloquial ways people refer to entities and concepts in various domains. We've developed a lot of internal tools to help us analyze and extract information from Wikipedia over the years, but now we've also added a Wikipedia "integrated service" to the latest version of the Wolfram Language---making it incredibly easy for anyone to incorporate Wiki content into Wolfram Language workflows.