Predictive text uses machine learning to guess a writer's next word based on their writing style. How predictive text uses machine learning The feature is disabled by default on Windows computers. When enabled, it is applied to applications running on the OS. Windows comes with a built-in predictive text function. Microsoft Outlook has a similar text suggestion feature. Gmail Smart Compose helps users quickly respond to emails and inserts stock phrases, like greetings and sign offs into emails. The search engine will guess commonly searched phrases and then autocomplete the search query with either a previously searched phrase or suggestions based on commonly searched queries. Google and other search engines use predictive text to guess a search query as the user types it in. Microsoft Word and other word processing applications make suggestions as a person types in a document. There are also many applications of predictive text outside of mobile messaging apps. Typewise also offers an AI writing assistant.
This predictive text application is compatible with both the Apple iOS and Android operating systems (OSes). Android first included a predictive text bar with Android 4.1 Jelly Bean in 2012. Gboard uses federated learning to train its prediction model on user behavior.
Gboard is sometimes also called the predictive text suggestion strip, word suggestion or auto-suggest. Google's keyboard application comes preinstalled on most Android devices. This allows the software to remember things such as whether the user uses slang when communicating with specific people and adjust its text predictions accordingly. Apple's predictive text feature has a machine learning component that enables the software to build custom dictionaries. Some examples of predictive text systems for mobile devices are the following:
The name was inspired by the way early spell checkers would change the word cooperation to Cupertino, which just happens to be the city in California where Apple has its corporate headquarters. Erroneous predictive text input on Apple devices is sometimes referred to as the Cupertino effect. Software in the device correlated sequences of key presses with words in the dictionary and prioritized predictions by frequency of use.Īlthough predictive text technologies are becoming increasingly sophisticated, the software is still notoriously error-prone. T9 improved the mobile user experience by associating groups of letters on each phone key with words in a dictionary. Prior to T9, typing on a mobile device required multitapping, where the user had to tap numbers up to four times to register letter characters. T9 was invented in 1995 and made typing on mobile phones and other small devices easier. One of the earliest applications for predictive text was T9 (text on 9 keys).
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