1. A multi-modal multi-lingual mobile device that facilitates automating an action, comprising: a detection component that employs a plurality of integrated sensors to obtain at least one criterion from an auxiliary act through passive observation, the auxiliary act is a conversation of a user with an entity that is not the mobile device, wherein the at least one criterion is an environmental context that relates to a weather condition, and a schedule manipulation action is performed at least partially in view of at least one of expected travel complications or venue incompatibility with the weather condition; and an analyzer component that evaluates the at least one criterion to infer a user intent and automatically implements the action based at least in part upon operation of a rules-based logic component, wherein the rules based logic component automatically allows execution of the action based at least in part upon satisfaction of a defined rule, and based at least in part upon operation of an implementation component configured to identify an individual, the implementation component using an algorithm together with a desired degree of certainty, and based at least in part upon operation of an artificial intelligence component that comprises a classifier function that maps an input attribute vector x=(x 1 , x 2 , x 3 , x 4 , x n ) to a confidence that input associated with the vector belongs to a class, wherein the x i , are input attributes, wherein the confidence that the input belongs to the class is expressed as f(x)=confidence(class), and wherein the class to which an input belongs infers the action that the user desires to be automatically performed; wherein the auxiliary act is not for an explicit purpose of implementing the action.
13. The system of claim 1 , the detection component comprises: an audio recorder component that captures speech of the user interaction with the another user; a speech analysis component that analyzes the captured speech of the user and the another user and determines an identification of the user and the another user, the identification is part of the contextual data; a location detection component that determines a physical location of the user during interaction with the another user, the physical location is part of the contextual data; and a date/time component that determines when the user interacts with the another user, a result of the date/time component determination is part of the contextual data.