Abstract: A method includes selecting a subset of models from a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes performing at least one genetic operation of the genetic algorithm with respect to at least one model of the subset to generate a trainable model. The method includes determining a rate of improvement associated with prior backpropagation iterations. The method includes selecting, based on the rate of improvement, one of the trainable model or a prior trainable model as a selected trainable model. The method includes generating the trained model including training the selected trainable model. The method includes adding the trained model as input to a second epoch of the genetic algorithm that is subsequent to the first epoch.

Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

Abstract: Embodiments of the invention provide a “tool-kit” of processing techniques which can be employed in different combinations depending on the circumstances. For example, flow speed can be found using eddy tracking techniques, or by using speed of sound measurements. Moreover, composition can be found by using speed of sound measurements and also by looking for turning points in the k-w curves, particularly in stratified multi-phase flows. Different combinations of the embodiments can therefore be put together to provide further embodiments, to meet particular flow sensing requirements, both on the surface and downhole. Once the flow speed is known, then at least in the case of a single phase flow, the flow speed can be multiplied by the interior cross-sectional area of the pipe to obtain the flow rate. The mass flow rate can then be obtained if the density of the fluid is known, once the composition has been determined.

Abstract: Logistics and transportation technologies are disclosed. A globally unique identifier (GUID) of a user is used to access a rideshare router that determine which one or multiple modes of transport should be used for various segments of a planned journey. The modes can include both terrestrial and unmanned aerial options. The GUID of the user is used to post information to a distributed ledger, such as a blockchain or hyperledger. A smart key fob of a vehicle provides a user with access to mobility services and a full-on computer experience. Logistics and transportation data, including on-board diagnostics data, can be used to predict vehicle maintenance, calculate a vehicle health score, determine natural language generation of explanations regarding vehicle health, and even calculate a carbon footprint score.

Abstract: Methods, systems, apparatuses, and computer program products for utilizing visualization for managing an unmanned aerial vehicle (UAV) are disclosed. In a particular embodiment, utilizing visualization for managing a UAV includes providing to a user, by a management controller, a visualization that displays an environment and representations of one or more UAVs associated with a user; receiving from the user, by the management controller, data indicating the user applying one or more management controls within the visualization; and in response to receiving the data indicating the user applying the one or more management controls within the visualization, initiating, by the management controller, an event that modifies the one or more UAVs.

Abstract: Uses of artificial intelligence in battery technology including a method that includes receiving a trained model, receiving sensor data from at least one sensor associated with a battery, and executing the trained model by a processor. Executing the trained model includes providing the sensor data as input to the trained model to generate a model output. The method also includes sending, from the processor to a charge controller coupled to the battery, a control signal that is based on the model output and automatically, by the charge controller, initiating or terminating charging of the battery based on the control signal.

Abstract: An architecture for data analysis of geographic data and associated context data. The data analysis of the geographic data and the associated context data includes receiving a query and determining a data model to output information requested by the query. The data analysis of the geographic data and the associated context data also includes accessing the geographic data and the associated context data from a data repository, providing the geographic data and the associated context data as input to the data model, and generating output including the information in response to the query.

Abstract: A device includes communication circuitry configured to receive a message indicating an observation of an agent device. The device further includes a processor coupled to the communication circuitry and a memory. The memory stores instructions that are executable by the processor to cause the processor to perform operations. The operations include accessing a blockchain data structure. The blockchain data structure includes one or more blocks including data descriptive of observations of a plurality of agent devices, where the plurality of agent devices including the agent device. The operations also include determining, based on one or more blocks of the blockchain data structure, a behavior of the agent device. The operations also include determine whether the behavior satisfies a behavior criterion associated with the agent device.

Abstract: Embodiments of the invention provide a “tool-kit” of processing techniques which can be employed in different combinations depending on the circumstances. For example, flow speed can be found using eddy tracking techniques, or by using speed of sound measurements. Moreover, composition can be found by using speed of sound measurements and also by looking for turning points in the k-? curves, particularly in stratified multi-phase flows. Different combinations of the embodiments can therefore be put together to provide further embodiments, to meet particular flow sensing requirements, both on the surface and downhole. Once the flow speed is known, then at least in the case of a single phase flow, the flow speed can be multiplied by the interior cross-sectional area of the pipe to obtain the flow rate. The mass flow rate can then be obtained if the density of the fluid is known, once the composition has been determined.

Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

Abstract: A device includes communication circuitry configured to receive a message indicating an observation of an agent device. The device further includes a processor coupled to the communication circuitry and a memory. The memory stores instructions that are executable by the processor to cause the processor to perform operations. The operations include accessing a blockchain data structure. The blockchain data structure includes one or more blocks including data descriptive of observations of a plurality of agent devices, where the plurality of agent devices including the agent device. The operations also include determining, based on one or more blocks of the blockchain data structure, a behavior of the agent device. The operations also include determine whether the behavior satisfies a behavior criterion associated with the agent device.

Abstract: A method includes receiving, at a server, first sensor data from a first vehicle. The method includes receiving, at the server, second sensor data from a second vehicle. The second sensor data includes condition data indicating a road condition, engine data indicating an engine problem, booking data indicating an intended route, or a combination thereof. The method includes aggregating, at the server, a plurality of sensor readings to generate aggregated sensor data. The plurality of sensor readings include the first sensor data and the second sensor data. The method further includes transmitting a first message based on the aggregated sensor data to the first vehicle, wherein the first message causes the first vehicle to perform a first action, the first action comprising avoiding the road condition, displaying an indicator corresponding to the engine problem, displaying a booked route, or a combination thereof.

Abstract: A method includes obtaining route information at an unmanned aerial vehicle (UAV). The route information indicates a plurality of zones between an origin and a destination and a corresponding set of mobile devices for each zone. The method includes receiving first control data at the UAV from a first mobile device while in a first zone of the plurality of zones. The first mobile device is included in a first set of mobile devices corresponding to the first zone. The method further includes receiving second control data at the UAV from a second mobile device while in a second zone of the plurality of zones. The second mobile device is included in a second set of mobile devices corresponding to the second zone.

Abstract: A computer system includes a memory storing a data structure representing a neural network. The data structure includes a plurality of fields including values representing topology of the neural network. The computer system also includes one or more processors configured to perform neural network classification by operations including generating a vector representing at least a portion of the neural network based on the data structure. The operations also include providing the vector as input to a trained classifier to generate a classification result associated with at least the portion of the neural network, where the classification result is indicative of expected performance or reliability of the neural network. The operations also include generating an output indicative of the classification result.

Abstract: An aspect of the present invention is to provide a system and method for predicting the remaining useful time of mechanical components such as bearings. Another aspect of the present invention is to provide a system and method for predicting the remaining useful time of bearings based on available condition monitoring data. Another aspect of the present invention is to provide a system and method for automatically deciding which columns of input information are the most significant for predicting the remaining useful life of bearings. Another aspect of the present invention is to provide a system and method for performing an analysis of both test bearings and training bearings and determining which training bearings are most similar to a given test bearing. Another aspect of the present invention is to provide a system and method for training an artificial neural network.

Abstract: A method includes determining, by a processor of a computing device, an expected performance or reliability of a first neural network of a first plurality of neural networks. The expected performance or reliability is determined based on a vector representing at least a portion of the first neural network, where the first neural network is generated based on an automated generative technique (e.g., a genetic algorithm) and where the first plurality of neural networks corresponds to a first epoch of the automated generative technique. The method also includes responsive to the expected performance or reliability of the first neural network failing to satisfy a threshold, adjusting a parameter of the automated generative technique. The method further includes, during a second epoch of the automated generative technique, generating a second plurality of neural networks based at least in part on the adjusted parameter.

Abstract: An unmanned aerial vehicle (UAV) system comprises a hangar structure configurable to mount on a host platform. The hangar structure comprises electrical circuits comprising a charging circuit and a communications circuit. The UAV system further comprises a plurality of stackable UAVs. The plurality of stackable UAVs comprise respective batteries and control circuits. The plurality of stackable UAVs are configured to cooperate with the charging circuit to charge the batteries and to cooperate with the communications circuit to communicate with the control circuits while the plurality of stackable UAVs are in a stacked configuration within the hangar structure.

Abstract: A device includes communication circuitry configured to receive a message indicating an observation of an agent device. The device further includes a processor coupled to the communication circuitry and a memory. The memory stores instructions that are executable by the processor to cause the processor to perform operations. The operations include accessing a blockchain data structure. The blockchain data structure includes one or more blocks including data descriptive of observations of a plurality of agent devices, where the plurality of agent devices including the agent device. The operations also include determining, based on one or more blocks of the blockchain data structure, a behavior of the agent device. The operations also include determine whether the behavior satisfies a behavior criterion associated with the agent device.

Abstract: A method includes determining, by a processor of a computing device, an expected performance or reliability of a first neural network of a first plurality of neural networks. The expected performance or reliability is determined based on a vector representing at least a portion of the first neural network, where the first neural network is generated based on an automated generative technique (e.g., a genetic algorithm) and where the first plurality of neural networks corresponds to a first epoch of the automated generative technique. The method also includes responsive to the expected performance or reliability of the first neural network failing to satisfy a threshold, adjusting a parameter of the automated generative technique. The method further includes, during a second epoch of the automated generative technique, generating a second plurality of neural networks based at least in part on the adjusted parameter.

Abstract: A computer system includes a memory storing a data structure representing a neural network. The data structure includes a plurality of fields including values representing topology of the neural network. The computer system also includes one or more processors configured to perform neural network classification by operations including generating a vector representing at least a portion of the neural network based on the data structure. The operations also include providing the vector as input to a trained classifier to generate a classification result associated with at least the portion of the neural network, where the classification result is indicative of expected performance or reliability of the neural network. The operations also include generating an output indicative of the classification result.