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: Devices, apparatuses, and systems for thermal management in networking and computing systems are provided. An example in-rack thermal management system includes a cooling distribution unit (CDU) that includes a housing that defines a fluid inlet and a fluid outlet, thermal management components supported by the housing, and direct mechanical connections coupled with the fluid inlet and the fluid outlet. The in-rack thermal management system includes a fluid distribution system that includes a primary fluid channel directly coupled with the direct mechanical connection of the fluid inlet, and a secondary fluid channel directly coupled with the direct mechanical connection of the fluid outlet. In operation, the thermal management components dissipate heat of a fluid received by the CDU via the fluid inlet. The direct mechanical connections directly interface with the fluid distribution system to provide fluid communication between the CDU and the fluid distribution system to maximize dimensions of the housing.

Abstract: Systems and methods are directed toward evaluating auditory inputs against a range of tolerance to provide feedback regarding pronunciation. An auditory input may be evaluated using a trained machine learning system and evaluated for similarity against a target word. Similarity may be scored and then evaluated to determine whether the similarity falls within a range of tolerance, wherein the range of tolerance may be adjusted or modified for particular uses. A score within the range of tolerance is indicative of a word that has been pronounced such that it would be perceptible.

Abstract: Methods are described herein for manufacturing multi-component objects using artificial intelligence. The present invention may be directed to a method that includes determining an actual value of a first attribute of a first component of an object and determining, using a machine learning model and based on the actual value of the first attribute, an optimized value for a second attribute of a second component that is functionally interrelated to the first component in the object. The method may include selecting, from a plurality of second components each having a value for the second attribute within a tolerance range, a second component having the optimized value for the second attribute. The method may further include manufacturing the object using the first component and the selected second component.

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: Systems, methods, and computer program products are provided for datacenter visualization. An example method includes receiving a request for datacenter visualization that is associated with a plurality of datacenter computing components of a physical datacenter installation. The method includes determining one or more installation characteristics associated with the physical datacenter installation and determining one or more performance parameters associated with the physical datacenter installation based at least in part on the one or more installation characteristics. The method further includes generating the datacenter visualization for presentation to a user associated with the request. The datacenter visualization is a digital representation of the physical datacenter installation that further includes a visual representation of the performance parameters associated with the plurality of datacenter computing components.

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: Systems, computer program products, and methods are described herein for intelligent intelligently designing and reconfiguring data centers. For example, a system may be an artificial-intelligence-based system for designing and reconfiguring data centers based on user inquiries. The system may receive inquiries from users requesting a design for a data center having particular performance characteristics (e.g., selected performance characteristics), changes to an existing design of a data center, information about a data center, and/or the like. The inquiries may be text-based or audio and in the form of natural language questions or commands. The system may use two machine learning models, namely a knowledge base model and a ranking model. The system may use the knowledge base model to determine solutions to user inquiries and then use the ranking model to determine rankings for the solutions.

Abstract: Systems, computer program products, and methods are described herein for intelligent data compression, in accordance with an embodiment of the invention. The present invention may be configured to receive a plurality of files for storage in a database and perform a series of steps iteratively, for each file of the plurality of files, and until each file of the plurality of files is represented in the database. The series of steps may include identifying one or more data points in the respective file, where each identified data point was previously unidentified in the database and adding the identified one or more data points to the database. The series of steps may also include identifying one or more features of the respective file for storage in the database and storing the identified one or more features in the database as a surrogate for the respective file.

Abstract: A digital avatar system can process video streams and generate synthetic video with a digital avatar. The digital avatar provides the appearance of a participant from the video stream talking and one or more of performing various behaviors or actions consistent with the participant's behavior when they are live streamed. A digital avatar system can detect triggering events during a live stream and automatically switch to an avatar mode.

Abstract: Some embodiments described herein provide intelligent movable racks for a data center and a central system for monitoring and directing the positioning of such racks within the data center. For example, a rack may include computing equipment as well as a power system, a cooling system, and a cabling system (e.g., for data communication). The rack may include a controller in communication with the computing equipment, the power system, the cooling system, and the cabling system. The rack may also include a rack interface for physically supporting the rack and operatively connecting the systems of the rack to power, cooling, and cabling infrastructure of the data center. The rack interface may receive an autonomous robot for moving the rack within the data center. The controller may control the power system and the cooling system based in part on the autonomous movement of the rack.

Abstract: Methods are described herein for artificial-intelligence-based manufacturing. The present invention may include performing an initial step in a series of steps to achieve a target value of an attribute, where the series of steps is for manufacturing an object, and, after performing the initial step, measuring an actual value of the attribute achieved by the initial step. The method may include, for each subsequent step in the series, providing actual values of attributes achieved by preceding steps in the series to a respective machine learning model to determine a respective target value for a respective attribute to be achieved by the respective step. The method may also include, for each step, performing the respective step to achieve the respective target value of the respective attribute and, after performing the respective step, measuring a respective actual value of the respective attribute achieved by the respective step.

Abstract: Methods, apparatuses, and computer program products for proactive thermal management and processing core selection are provided. An example method includes receiving a job packet that is associated with a packet profile and determining one or more performance parameters associated with a performance of the job packet as defined by the packet profile. The method further includes generating a proactive thermal management procedure based upon the one or more performance parameters and associating the proactive thermal management procedure with the job packet. The method may further include determining a selected processing core from amongst a plurality of processing cores for the performance of the job packet based upon one or more operating characteristics of the processing cores and/or one or more performance parameters of the job packet.

Abstract: Systems, computer program products, and methods are described herein for intelligent data compression, in accordance with an embodiment of the invention. The present invention may be configured to receive a plurality of files for storage in a database and perform a series of steps iteratively, for each file of the plurality of files, and until each file of the plurality of files is represented in the database. The series of steps may include identifying one or more data points in the respective file, where each identified data point was previously unidentified in the database and adding the identified one or more data points to the database. The series of steps may also include identifying one or more features of the respective file for storage in the database and storing the identified one or more features in the database as a surrogate for the respective file.

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 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: 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.