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: A computer-implemented method of managing online bookings for transportation services inventory is provided. The computer-implemented method establishes a channel interface that provides bidirectional communication between at least one server computing device and a vendor computing device. Furthermore, the computer-implemented method stores, via a channel data structure, a channel record for each of a plurality of distribution channels. Additionally, the computer-implemented method receives, from the vendor computing device through the channel interface, a selection, from the plurality of distribution channels, of one or more participant channels for a transportation service. The computer-implemented method also establishes, via the channel data structure, one or more active channel records based upon the one or more participant channels for the transportation service.

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-implemented method of managing online bookings for transportation services inventory is provided. The computer-implemented method establishes a channel interface that provides bidirectional communication between at least one server computing device and a vendor computing device. Furthermore, the computer-implemented method stores, via a channel data structure, a channel record for each of a plurality of distribution channels. Additionally, the computer-implemented method receives, from the vendor computing device through the channel interface, a selection, from the plurality of distribution channels, of one or more participant channels for a transportation service. The computer-implemented method also establishes, via the channel data structure, one or more active channel records based upon the one or more participant channels for the transportation service.

Abstract: A computer-implemented method of managing online bookings for transportation services inventory is provided. The computer-implemented method establishes a channel interface that provides bidirectional communication between at least one server computing device and a vendor computing device. Furthermore, the computer-implemented method stores, via a channel data structure, a channel record for each of a plurality of distribution channels. Additionally, the computer-implemented method receives, from the vendor computing device through the channel interface, a selection, from the plurality of distribution channels, of one or more participant channels for a transportation service. The computer-implemented method also establishes, via the channel data structure, one or more active channel records based upon the one or more participant channels for the transportation service.

Abstract: A computer-implemented method of managing online bookings for transportation services inventory is provided. The computer-implemented method establishes a channel interface that provides bidirectional communication between at least one server computing device and a vendor computing device. Furthermore, the computer-implemented method stores, via a channel data structure, a channel record for each of a plurality of distribution channels. Additionally, the computer-implemented method receives, from the vendor computing device through the channel interface, a selection, from the plurality of distribution channels, of one or more participant channels for a transportation service. The computer-implemented method also establishes, via the channel data structure, one or more active channel records based upon the one or more participant channels for the transportation service.

Abstract: A computer-implemented method of managing online bookings for transportation services inventory is provided. The computer-implemented method links, via a computerized network, each item in the transportation services inventory with one of a plurality of online distributions channels by allocating the item to the respective online distribution channel. Furthermore, the computer-implemented method receives, from each of a plurality of online distribution channels in real time via the computerized network, sale data pertaining to the online bookings for the items in the transportation services inventory allocated to the respective online distribution channel. The computer-implemented method processes the sale data by carrying out calculations to obtain a performance rating for each of the respective online distribution channels.