Abstract: Machine learning model re-training based on distributed feedback received from a plurality of edge computing devices is provided. A trained instance of a machine learning model is transmitted, via one or more communications networks, to the plurality of edge computing devices. Feedback data is collected, via the one or more communications networks, from the plurality of edge computing devices. The feedback data includes labeled observations generated by the execution of the trained instance of the machine learning model at the plurality of edge computing devices on unlabeled observations captured by the plurality of edge computing devices. A re-trained instance of the machine learning model is generated from the trained instance using the collected feedback data. The re-trained instance of the machine learning model is transmitted, via the one or more communications networks, to the plurality of edge computing devices.

Abstract: Machine learning (ML) is provided at edge computing devices based on distributed feedback received from the edge computing devices. A trained instance of an ML model is received at the edge computing devices via communications networks from an ML model manager. Feedback data including labeled observations is generated by the execution of the trained instance of the ML model at the edge computing devices on unlabeled observations captured by the edge computing devices. The feedback data is transmitted from the edge computing devices to a machine learning model manager. A re-trained instance of the machine learning model is generated from the trained instance using the collected feedback data. The re-trained instance of the machine learning model is received at the edge computing devices from the machine learning model manager. The re-trained instance of the machine learning model is executed at the edge computing devices.

Abstract: Machine learning model re-training based on distributed feedback received from a plurality of edge computing devices is provided. A trained instance of a machine learning model is transmitted, via one or more communications networks, to the plurality of edge computing devices. Feedback data is collected, via the one or more communications networks, from the plurality of edge computing devices. The feedback data includes labeled observations generated by the execution of the trained instance of the machine learning model at the plurality of edge computing devices on unlabeled observations captured by the plurality of edge computing devices. A re-trained instance of the machine learning model is generated from the trained instance using the collected feedback data. The re-trained instance of the machine learning model is transmitted, via the one or more communications networks, to the plurality of edge computing devices.

Abstract: Machine learning (ML) is provided at edge computing devices based on distributed feedback received from the edge computing devices. A trained instance of an ML model is received at the edge computing devices via communications networks from an ML model manager. Feedback data including labeled observations is generated by the execution of the trained instance of the ML model at the edge computing devices on unlabeled observations captured by the edge computing devices. The feedback data is transmitted from the edge computing devices to a machine learning model manager. A re-trained instance of the machine learning model is generated from the trained instance using the collected feedback data. The re-trained instance of the machine learning model is received at the edge computing devices from the machine learning model manager. The re-trained instance of the machine learning model is executed at the edge computing devices.

Abstract: A device disclosed herein comprises a touch screen interface configured to display a plurality of different keyboard configurations, each of the keyboard configurations representing a plurality of keys, receive an input from a user to select one of a plurality of keyboard selection inputs, and in response to the selection of the one of a plurality of keyboard selection inputs, displaying a selected keyboard configuration from the plurality of different keyboard configurations, wherein each of the plurality of keys related to the selected keyboard configuration represents a word.

Abstract: An innovative language system and global/mobile network-based platforms for social networking and messaging is built on a vocabulary of symbols holding a universal meaning that transcends barriers of language and regional dialect through a complete system of cross-referencing and evolution. Individuals can contribute to the language and absorb new aspects of the language as it evolves globally. Furthermore, the symbol vocabulary is faster than standard text-based communications and is intended to communicate broader concepts with fewer keystrokes. The new language and communication system also presents opportunities for commercial benefit. As system use proliferates, commercial entities can sponsor their own symbols, collect real time symbol use data (whether geographically-based or not), and provide promotional benefits to consumers based on the use data.