Abstract: First sensor data can be received from a first set of IoT devices. Sensor data collection rates can be determined for a first artificial intelligence model by analyzing the first sensor data using a second artificial intelligence model. Based on the sensor data collection rates, sensor control commands can be communicated to a second set of Internet of Things devices. The sensor control commands can specify, to the second set of Internet of Things devices, sensor data communication rates that respective ones of the second set of Internet of Things devices are to implement for communicating, to the first artificial intelligence model, second sensor data generated by sensors of the respective ones of the second set of Internet of Things devices.

Abstract: A processor may receive user data associated with one or more locations of a user in an environment. The processor may receive edge computing data associated with utilization of edge computing resources by the user. The processor may analyze the edge computing data to associate a context with an edge computing resource need. The processor may analyze the user data to associate a context with a location of the user within the environment. The processor may determine a first location of the user in the environment at a first time. The processor may predict a first edge computing need of the user in the first location. The processor may determine an arrangement of one or more edge computing devices configured to meet the first edge computing need of the user at the first time.

Abstract: A processor may receive environment data associated with one or more robotic devices and an environment. The one or more robotic devices may be configured to perform one or more activities in the environment. The processor may analyze the environment data and the one or more activities in the environment. The processor may identify one or more performance factors associated with the environment. The processor may generate one or more simulations associated with the one or more performance factors and the one or more robotic devices in the environment. The processor may apply one or more protective components to the one or more robotic devices. Applying the one or more protective components to the one or more robotic devices may be based, at least in part, on the one or more simulations.

Abstract: Computer technology for performing dynamic code branching of self-adapted code upon successful execution of a contextual scenario by artificial intelligence (AI) enabled edge device (for example, an autonomous vehicle or an industrial robotic device). predicting a second contextual scenario where the AI enabled edge device can perform a predetermined activity, and proactively deploying self-adapted code on the AI enabled edge device.

Abstract: An augmented reality (AR) container orchestration system includes computer hardware including an AR system and a container orchestration platform. The AR system is configured to perform identifying a container to be deployed, displaying, to a user, a grid having a plurality of cells each of which are configured to receive a representation of the container, and redisplaying, using the AR system and to the user, the grid based upon a movement of the representation of the container to a particular position within the grid. The container orchestration platform is configured to perform identifying possible configurations for the container, and configuring the container based upon the movement of the representation of the container to the particular position. The includes a plurality of axes, and each axis of the plurality of axis represents a different configuration-related parameter of the configurations for the container.

Abstract: Identification of people and items in a location using Artificial Intelligence (AI) by detecting, using an AI enabled device, a target entity in a location and receiving data regarding the target entity from one or more devices in the location. The target entity is identified and a perimeter around all or part of the entity can be defined, using the AI enabled device, in response to the receiving data from devices in the location. An analysis can be implemented for data about the target entity, and the received data to determine an action for the AI enabled device. An action can be generated, based on the analysis, where the action can include an AI enabled interaction with the target entity, and the action can be implemented using the AI enabled device.

Abstract: Allocating processing activities among multiple computing devices can include identifying multiple computing activities of a computer-executable process and, for each computing activity identified, estimating in real time the computing resources needed. The identifying can be in response to detecting a computer-executable instruction executed by one multiple communicatively coupled computing devices, and the computer-executable instruction can be associate with the computer-executable process. A current condition and configuration of each of the computing devices can be determined in real time. For each computing device an effect induced by executing one or more of the plurality of activities can be predicted, the predicting based each computing device's current condition and configuration and performed by a machine learning model trained using data collected from prior real-time processing of example process activities.

Abstract: Technology for implementing methods that include the following operations: (i) receiving a container instantiation data set that includes the data needed to instantiate at least a first type of container; (ii) receiving a computing environment context data set including information relevant to computing operations currently being performed in a computer environment, with the computer environment context data set including information indicative of a first internal factor and/or a first external factor; (iii) applying an artificial intelligence algorithm to determine a predicted future status of the computing environment based, at least in part, upon the computer environment context data set; (iv) determining, by machine logic and based, at least in part upon the predicted future status, that the computing environment is likely to operate better if a new container of the first type is instantiated; and (v) automatically instantiating a first container.

Abstract: A processor may receive user data associated with one or more locations of a user in an environment. The processor may receive edge computing data associated with utilization of edge computing resources by the user. The processor may analyze the edge computing data to associate a context with an edge computing resource need. The processor may analyze the user data to associate a context with a location of the user within the environment. The processor may determine a first location of the user in the environment at a first time. The processor may predict a first edge computing need of the user in the first location. The processor may determine an arrangement of one or more edge computing devices configured to meet the first edge computing need of the user at the first time.

Abstract: First sensor data can be received from a first set of IoT devices. Sensor data collection rates can be determined for a first artificial intelligence model by analyzing the first sensor data using a second artificial intelligence model. Based on the sensor data collection rates, sensor control commands can be communicated to a second set of Internet of Things devices. The sensor control commands can specify, to the second set of Internet of Things devices, sensor data communication rates that respective ones of the second set of Internet of Things devices are to implement for communicating, to the first artificial intelligence model, second sensor data generated by sensors of the respective ones of the second set of Internet of Things devices.