Abstract: Apparatuses, systems, and techniques to use one or more neural networks to predict one or more computing resources to perform one or more workloads are described.

Abstract: Systems and methods herein address power for one or more processing units, using one of a plurality of power profiles during execution of a group of real-time instructions, the one of the plurality of power profiles determined based in part on a relationship determined between the one of the plurality of power profiles and a power profile of the group of real-time instructions, the relationship limited by a threshold, and the plurality of power profiles are associated with a plurality of groups of reference instructions.

Abstract: Systems and methods herein address power for one or more processing units, using one of a plurality of power profiles during execution of a group of real-time instructions, the one of the plurality of power profiles determined based in part on a relationship determined between the one of the plurality of power profiles and a power profile of the group of real-time instructions, the relationship limited by a threshold, and the plurality of power profiles are associated with a plurality of groups of reference instructions.

Abstract: A method includes receiving, using a processing device, a first condition associated with an operation at a data center, where the operation at the data center pertains to a first location at the data center, the first location corresponding to a first parameter value. The method further includes providing the first condition as an input to a machine learning model. The method also includes performing one or more reinforcement learning techniques using the machine learning model to cause the machine learning model to output an indication of a final location associated with the operation, where the final location corresponds to a final parameter value that is closer to a target than the first parameter value corresponding to the first location at the data center.

Abstract: A virtual representation of a physical environment can be generated through simulation, which can include one or more virtual agents to represent robots, or at least semi-automated devices, that can operate and perform various tasks in the physical environment. Various component failures, or other potential problems, can be simulated that can be analyzed by one or more deep learning models associated with the virtual agents. These deep learning models can attempt to diagnose the simulated problem, as well as determine one or more potential solutions. The virtual agents can help to gather information for these determinations, as well as to perform tasks for these potential solutions. Once these deep learning models are trained in this simulated environment, these models can be used by one or more robots to perform tasks that may relate to maintenance or operation of a physical environment.

Abstract: A system and method for controlling cooling in a server is provided. The method includes receiving one or more server indicators relating to a task to be executed by at least one component of the server. The method also includes determining an expected cooling demand for the at least one component based on the one or more server indicators. The method further includes adjusting a cooling amount provided by a cooling mechanism based on the expected cooling demand of the at least one component. Various embodiments are described herein.

Abstract: Apparatuses, systems, and techniques to monitor health data from components and predict needs for maintenance. In at least one embodiment, monitoring health data of cables having one or more known characteristics ands analyzing the health data to determine the health metrics of the one or more cable to generate profiles of the cables used to predict future health metrics of the cables and related cables sharing known characteristics.

Abstract: Various embodiments of the present disclosure provide for generating and managing a digital ledger access system and its associated objects. An example method is configured for securing objects in a digital ledger of objects by identifying an object from amongst a plurality of objects in the digital ledger of objects and generating a quantum token for attachment with the object. The method includes deriving one or more classical public keys associated with the quantum token and determining an attempt to access the object. The method provides access to the object in response to a validation of the classical public key based on the quantum token, and the method precludes access to the object in response to an invalidation of the classical public key based on the quantum token.

Abstract: A robot device determines an error associated with equipment included in a data center environment. The robot device may compare the error to candidate errors for which the robot device is already trained to resolve. Based on a result of the comparison, the robot device may perform, in a control environment, candidate maintenance operations in association with resolving the error. The robot device may learn a set of actions associated with successfully resolving the error, based on performing the candidate maintenance operations. The robot device may perform maintenance operations associated with the error. Performing the maintenance operations may include applying the learned set of actions.

Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example system for heat dissipation may include a memory. The example system may also include a processor configured to receive at least one visual representation of at least a portion of the sky, determine, based on the at least one visual representation (e.g., using artificial intelligence), a mask distinguishing between clouded and cloudless portions of the sky, based on the mask, determine a direction in which to point a heat dissipation panel toward one or more aim portions of the sky, and generate a signal to cause one or more heat dissipation surfaces or panels to be moved (e.g., using a robotic arm) such that heat is radiated toward the one or more aim portions of the sky.

Abstract: A computing entity obtains product information corresponding to a product and defines a multi-region metric space vector based on the product information. The multi-region metric space vector is a vector within a multi-region metric space that comprises a first region corresponding to supply chain/component information, a second region corresponding to assembly/fabrication information, and a third region corresponding to usage/usage environment information. Each of the first, second, and third regions are multi-dimensional.

Abstract: A computing entity is described that obtains at least one inspection image of an at least partially fabricated product and causes the at least one inspection image to be processed by a product inspection engine. The product inspection engine includes a machine learning-trained model. The computing entity obtains an inspection result determined based on the processing of the at least one inspection image by the product inspection engine; identifies one or more training images stored in an image database based at least in part on the at least one inspection image; associates automatically generated labeling data with the one or more training images based at least in part on the inspection result determined by the processing of the at least one inspection image; and causes training of the product inspection engine using the one or more training images and the associated labeling data.

Abstract: Systems and methods for datacenter are disclosed. In at least one embodiment, a system or method herein causes a process for a structure that includes a component tracking system, where such a system includes individual components within individual servers or individual racks, the process being based in part on at least location information and configuration information from one or more of an optical sensor or a radio sensor associated with a motile-support that is adapted for at least three dimensional (3D) movement in a space having the individual servers or the individual racks.

Abstract: In various examples, a 3D representation of an environment may be generated from sensor data, with objects being detected in the environment using the sensor data and stored as items that can be tracked and located within the 3D representation. The 3D representation of the environment and item information may be used to determine (e.g., identify or predict) a location or position of an item within the 3D representation and/or recommend a storage location for the item within the 3D representation. Using a determined location or position, one or more routes to the location through the 3D representation may be determined. Data corresponding to a determined route may be provided to a user and/or device. User preferences, permissions, roles, feedback, historical item data, and/or other data associated with a user may be used to further enhance various aspects of the disclosure.

Abstract: A computing entity is described that obtains at least one inspection image of an at least partially fabricated product and causes the at least one inspection image to be processed by a product inspection engine. The product inspection engine includes a machine learning-trained model. The computing entity obtains an inspection result determined based on the processing of the at least one inspection image by the product inspection engine; identifies one or more training images stored in an image database based at least in part on the at least one inspection image; associates automatically generated labeling data with the one or more training images based at least in part on the inspection result determined by the processing of the at least one inspection image; and causes training of the product inspection engine using the one or more training images and the associated labeling data.

Abstract: Apparatuses, systems, and techniques to cause one or more directions of travel to be indicated to a user in order to improve wireless signal strength. In at least one embodiment, one or more directions of travel are indicated to a device in order to improve wireless signal strength, based on, for example, wireless signal strength values obtained by said device at one or more locations.

Abstract: Systems and methods of presenting interrupting content during human speech are disclosed. The proposed systems offer improved duplex communications in conversational AI platforms. In some embodiments, the system receives speech data and evaluates the data using linguistic models. If the linguistic models detect indications of linguistic irregularities such as mispronunciation, a smart feedback assistant can determine that the system should interrupt the speaker in near-real-time and provide feedback regarding their pronunciation. In addition, conversational irregularities may also be detected, causing the smart feedback assistant to interrupt with presentation of moderating guidance. In some cases, emotion models may also be utilized to detect emotional states based on the speaker's voice in order to offer near-immediate feedback. Users can also customize the manner and occasions in which they are interrupted.