Google’s neural network wants to compose email responses for you

Gmail users are set to benefit from Google’s machine learning research with Smart Reply. The system will use a deep neural network to not only analyze incoming emails for what information is required to form an appropriate response, but to propose likely replies, with the end result enabling mobile users to respond quickly to emails.
Google’s research blog details the initial challenge and the science that went into creating the technology. Crucial is a concept called sequence-to-sequence learning, already used in Google translation and a chatbot the search giant released earlier this year.
In sequence-to-sequence learning, two neural networks fuse both understanding a language and synthesizing language. The decoding network creates a thought vector by transcribing each word individually into a number, based on its context within the rest of the text. This grants the network the "idea" of the email.
The encoding network then generates potential responses, obviously not knowing which the human user might be partial to, but all of them making sense in the context of the decoded message and presenting suitable alternatives.