Abstract: A method for implementing an arbiter module includes cryptographically binding a true identity for a particular real-world individual to an arbiter module and cryptographically binding one or more real-world entities to the arbiter module. The one or more real-world entities store third party information associated with the particular real-world individual. The method also includes cryptographically communicating one or more data packages, which include the third-party information, from the one or more real-world entities to the arbiter module. The method also includes cryptographically binding one or more metaidentities to the arbiter module such that the third-party information associated with the particular real-world individual is at least partially attributable to the one or more metaidentities via the arbiter module for facilitating one or more metaverse actions within the one or more metaverses without revealing the true identity that is bound to the arbiter module.

Abstract: Improved multi-modal machine learning networks integrate computer vision systems with language models. In certain embodiments, a computer vision system analyzes at least one image to generate a computer vision output. The language model generates an output based, at least in part, on a consideration of the computer vision output. The outputs of the language model can be generated by jointly considering textual information learned by the language model and visual content extracted by the computer vision system, thereby significantly improving the accuracy, breadth, and comprehensiveness of the outputs.

Abstract: This disclosure relates to improved techniques for personalizing vehicle routes and operator sessions using pre-trained machine learning language models. In certain embodiments, a language model is trained on operator interaction data to learn operator route preferences for vehicle operators. These learned operator route preferences can be leveraged to optimize and personalize vehicle routes and operator sessions in various ways. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to improved techniques for personalizing vehicle routes and operator sessions using pre-trained machine learning language models. In certain embodiments, a language model is trained on operator interaction data to learn operator route preferences for vehicle operators. These learned operator route preferences can be leveraged to optimize and personalize vehicle routes and operator sessions in various ways. Other embodiments are disclosed herein as well.

Abstract: Improved multi-modal machine learning networks integrate computer vision systems with language models. In certain embodiments, a computer vision system analyzes at least one image to generate a computer vision output. The language model generates an output based, at least in part, on a consideration of the computer vision output. The outputs of the language model can be generated by jointly considering textual information learned by the language model and visual content extracted by the computer vision system, thereby significantly improving the accuracy, breadth, and comprehensiveness of the outputs.

Abstract: This disclosure relates to artificial intelligence (AI) and machine learning networks for predicting or determining demand metrics across multiple channels. An analytics platform can receive channel events from multiple channels corresponding to geographic areas, and channel features related to demand conditions in the channels can be extracted from the channel events. During a training phase, the channel features can be accumulated into one or more training datasets for training one or more demand prediction models. The one or more demand prediction models can be trained to predict or determine demand metrics for each of the channels. The demand metrics can indicate or predict demand conditions based on the current conditions in the channels and/or based on future, predicted conditions in the channels. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to decentralized computing networks, architectures and techniques for collecting, analyzing, and processing data over multiple channels. A decentralized computing network comprises a plurality of computing nodes, each of which is dedicated to analyzing and processing events for a particular channel corresponding to a geographic region. Each node of the decentralized computing network can operate independently to process channel analysis data for a corresponding channel. The decentralized configuration of the nodes enables efficient processing of data collected over large geographic areas, increases the reliability of the system, and facilitates easy scaling of the system. Other embodiments are disclosed herein as well.

Abstract: A computer system for accessing a decentralized user controlled social media platform receives, from an user computing device, a user authentication token for the decentralized user controlled social media platform. The computer system determines that the user authentication token is valid for access to a user account at the decentralized user controlled social media platform. Additionally, the computer system requests, through a network connection, content from a plurality of remote servers. Each of the plurality of remote servers requires a unique remote server authentication token to access a portion of the content stored at the respective remote server. Further, the computer system communicates, to the user computing device, the content received from the plurality of remote servers.

Abstract: Improved techniques for adapting the look, feel, and/or behavior of a mobile application based on user attributes, location, and/or environment in which it is used are described herein. A triggering condition is determined to be satisfied between a hand-held device and a beacon. Based on the triggering condition being satisfied, one or more application settings are applied to an adaptive application, where these settings are associated with the beacon. Additionally, in response to fetching or acquiring an application skin that is associated with the beacon, the application skin is also applied to the adaptive application.