Abstract: An information handling system may include a memory and a processor communicatively coupled to the memory, and configured to collect contextual information associated with a first artificial intelligence model executing on a compute node and execute a second artificial intelligence model to generate a new model deployment suggestion inference for the first artificial intelligence model based at least on the contextual information, the new model deployment suggestion inference setting forth parameters for a redeployment of the first artificial intelligence model.

Abstract: Systems and methods for deploying Artificial Intelligence (AI) models in AI appliances are described. In an illustrative, non-limiting embodiment, an AI appliance may include: a Systems-on-Chip (SoC); an AI accelerator coupled to the SoC; and a memory coupled to, or integrated into the SoC, the memory having program instructions stored thereon that, upon execution by the SoC, cause the AI appliance to: detect a client Information Handling System (IHS) in proximity with respect to the AI appliance; and at least in part in response to the detection, prepare the AI accelerator for use by the client IHS.

Abstract: An information handling system may include a memory and a processor communicatively coupled to the memory and configured to collect accuracy measurements for an artificial intelligence model executing on a compute node to determine an accuracy for the artificial intelligence model and generate an alert with a recommendation for optimizing execution of the artificial intelligence model based at least on the accuracy.

Abstract: Systems and methods for accessing Artificial Intelligence (AI) appliances are described. In an illustrative, non-limiting embodiment, an AI appliance may include: a Systems-on-Chip (SoC); an AI accelerator coupled to the SoC; and a memory coupled to, or integrated into the SoC, the memory having program instructions stored thereon that, upon execution by the SoC, cause the AI appliance to: communicate with a client Information Handling System (IHS); and determine whether to change a configuration of the AI accelerator based, at least in part, upon whether the communication is over: (a) a local port, or (b) a network port.

Abstract: An information handling system may include a processor and a workflow observer comprising a program of instructions embodied in non-transitory computer-readable media communicatively coupled to the processor. The workflow observer may be configured to, when read and executed by the processor, monitor interactions by a user with the information handling system, create periodic snapshots of the interactions, based on the periodic snapshots, generate a representation of a user workflow on the information handling system, calculate a semantic similarity between the user workflow and a feature, and if the semantic similarity exceeds a threshold, recommend the feature to a user of the information handling system.

Abstract: Systems and methods for managing Artificial Intelligence (AI) accelerators in AI appliances are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive, from a plurality of AI appliances over a network, data related to a plurality of AI appliances, where the data indicates whether a client IHS is directly coupled to an AI appliance; and select one of a plurality of AI appliances for execution of a workload on behalf of another client IHS based, at least in part, upon the data.

Abstract: An information handling system may include a memory and a processor communicatively coupled to the memory, and configured to model an artificial intelligence model as a graph, select a cost function for assigning a cost value for each edge of the graph, and select a demand function for assigning a demand value between each pair of nodes of the graph. The processor may also be configured to, for each cut of a plurality of possible cuts to the graph, calculate a sparsity of each cut based on the cost value and the demand value corresponding to such cut, determine a sparsest cut of the plurality of cuts as a cut having a smallest sparsity among the sparsities of the plurality of possible cuts, and split the artificial intelligence model into a plurality of fragments based on the sparsest cut.

Abstract: An information handling system may include a memory and a processor communicatively coupled to the memory, and configured to collect telemetry relating to execution of an artificial intelligence model on at least one configuration of a compute node and a quantization level, based on the telemetry collected, compute a power matrix setting forth average power consumption for execution of the artificial intelligence model on a plurality of configurations of compute nodes and quantization levels, and prioritize the plurality of configurations for execution of the artificial intelligence model based on the average power consumption for each configuration.

Abstract: Systems and methods described herein may provide a system that enables game play or other application sessions to consumption devices of a user's choice as a reward upon completion of tasks assigned to the user. A method may include monitoring, by an information handing system, a task assigned to be completed by a user; determining, by the information handling system, completion of the task by the user based, at least in part, on a physical location tracking of the user, wherein the physical location tracking of the user comprises: monitoring, by the information handling system, a controller; and comparing, by the information handling system, monitored controller movements to a known map stored on the information handling system; and granting, by the information handling system, a reward to the user. Other aspects are also disclosed.

Abstract: Systems and methods described herein may provide a system that enables hybrid local and global processing of voice requests from a user. A computing device may receive audio data containing a voice request from a user and may determine, using a local intent engine, a local intent prediction based on the voice request. The computing device may determine that the local intent prediction has a confidence measure that is less than a predetermined threshold and may, in response, transmit data containing the voice request to a global intent engine. The computing device may receive a global intent prediction from the global intent engine and may determine a corresponding computing process based on the global intent prediction.

Abstract: An information handling system may host a single, common interface through which users may browse, select, purchase, and use software services offered on more than one cloud computing platform. An information handling system configured as such may receive a user request to perform an action; determine a list of software functions configured to perform that action offered on a plurality of cloud computing platforms using an application programing interface (API) configured to integrate with the platforms; retrieve licensing term options covering the use of each software function to perform the action; transmit previews of each software function performing the action; receive a selection from the user of a selected software function, an associated licensing term option, and payment details in accordance with the selected licensing term option; and cause the action to be completed by the selected software function using the API in response to receiving the payment details.

Abstract: Systems and methods described herein may provide a system that enables hybrid local and global processing of voice requests from a user. A computing device may receive audio data containing a voice request from a user and may determine, using a local intent engine, a local intent prediction based on the voice request. The computing device may determine that the local intent prediction has a confidence measure that is less than a predetermined threshold and may, in response, transmit data containing the voice request to a global intent engine. The computing device may receive a global intent prediction from the global intent engine and may determine a corresponding computing process based on the global intent prediction.

Abstract: Systems and methods described herein may provide a system that enables game play or other application sessions to consumption devices of a user's choice as a reward upon completion of tasks assigned to the user. A method may include monitoring, by an information handing system, a task assigned to be completed by a user; determining, by the information handling system, completion of the task by the user based, at least in part, on a physical location tracking of the user, wherein the physical location tracking of the user comprises: monitoring, by the information handling system, a controller; and comparing, by the information handling system, monitored controller movements to a known map stored on the information handling system; and granting, by the information handling system, a reward to the user. Other aspects are also disclosed.

Abstract: Systems and methods described herein may provide a system that enables execution of workloads within a distributed computing environment that includes one or more edge computing devices and/or one or more user computing devices. A computing device may determine a workload for execution within a distributed computing environment that includes a plurality of computing devices. The computing device may determine valid execution slots within the distributed computing environment and may select a first execution slot of the valid execution slots. A first edge computing device may be assigned to execute the workload during the first execution slot. The first execution slot may be selected based on scores computed for the valid execution slots. The scores may be determined based on at least one criteria selected from the group consisting of (i) workload durations, (ii) carbon emissions, (iii) utility costs, and (iv) a disruption penalty.