Google Translate generates a translation proposal, it looks for patterns in hundreds of millions of documents to help decide on the best translation. By detecting patterns in documents that have already been translated by human translators, Google Translate makes informed guesses (AI) as to what an appropriate translation should be. Before October 2007, for languages other than Arabic, Chinese and Russian, Google Translate was based on SYSTRAN, a software engine which is still used by several other online translation services such as Babel Fish (now defunct). From October 2007, Google Translate used proprietary, in-house technology based on statistical machine translation instead, before transitioning to neural machine translation. Google Translate Community Google has crowdsourcing features for volunteers to be a part of its "Translate Community", intended to help improve Google Translate's accuracy. Volunteers can select up to five languages to help improve translation; users can verify translated phrases and translate phrases in their languages to and from English, helping to improve the accuracy of translating more rare and complex phrases. In August 2016, a Google Crowdsource app was released for Android users, in which translation tasks are offered. There are three ways to contribute. First, Google will show a phrase that one should type in the translated version. Second, Google will show a proposed translation for a user to agree, disagree, or skip. Third, users can suggest translations for phrases where they think they can improve on Google's results. Tests in 44 languages show that the "suggest an edit" feature led to an improvement in a maximum of 40% of cases over four years. Statistical machine translation Although Google deployed a new system called neural machine translation for better quality translation, there are languages that still use the traditional translation method called statistical machine translation. It is a rule-based translation method that utilizes predictive algorithms to guess ways to translate texts in foreign languages. It aims to translate whole phrases rather than single words then gather overlapping phrases for translation. Moreover, it also analyzes bilingual text corpora to generate statistical model that translates texts from one language to another. Google Neural Machine Translation In September 2016, a research team at Google announced the development of the Google Neural Machine Translation system (GNMT) to increase fluency and accuracy in Google Translate and in November announced that Google Translate would switch to GNMT. Google Translate's neural machine translation system uses a large end-to-end