Abstract: Systems and methods for deploying instances of Artificial Intelligence (AI) models having different levels of complexity in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS), may include a heterogeneous computing platform comprising a plurality of devices and a memory having a plurality of sets of firmware instructions that, upon execution by a respective device, enable the respective device to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: select an instance of an AI model among a plurality of instances of the AI model based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices, where each of the plurality of instances of the AI model has a different level of complexity, and instruct a device to execute the selected instance of the AI model.

Abstract: An information handling system receives a request for an artificial intelligence service that is associated with an artificial intelligence model and retrieves a manifest associated with the artificial intelligence model. The system also determines a current system capacity of the information handling system and determines a workload segmentation according to the manifest based on the current system capacity of the information handling system.

Abstract: An information handling system stores a multiple machine learning (ML) models. The system receives a job request that includes input parameters and constraints for a job associated with the job request. Based on the input parameters and the constraints, the system identifies first and second ML models available to execute the job. The system also generates a first score for the first ML model and a second score for the second ML model. Based on the first score being greater than the second score, the system provides the job to a compute resource associated with the first ML model.

Abstract: An information handling stores machine learning (ML) models and quality of service (QoS) data. The system performs a registration operation for multiple model management frame (MMF) core contracts and multiple supporting MMF converted modules (MCMs). Each different one of the MMF core contracts is supported by a corresponding different one of the MCMs. The system receives an application programming interface (API) request. In response to the API request, the system performs a learn operation for a first ML model and determines that a first MCM is to be used to respond to the API request. The system determines that the first ML model is associated with the first MCM and executes the first ML model via the first MCM to generate output data for a response to the API request.

Abstract: An information handling system converts an artificial intelligence model to be compatible with an endpoint management service and determine a dependency on the artificial intelligence model. The information handling system also generates a software package that includes a model management framework associated with the artificial intelligence model. The software package is compatible with the endpoint management service, and includes the converted artificial intelligence model and the dependency. In addition, the information handling system deploys the software package to a client device that is managed by the endpoint management service.

Abstract: Systems and methods for filtering profanities during collaboration sessions in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform having a plurality of devices and a memory coupled to the heterogeneous computing platform, where the memory includes a plurality of sets of firmware instructions, where each set of firmware instructions, upon execution by a respective device, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive a policy from an Information Technology Decision Maker (ITDM) or Original Equipment Manufacturer (OEM), and select a profanity filter setting usable during a collaboration session based, at least in part, upon the policy.

Abstract: Systems and methods for usage detection and notification in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices and instruct one or more selected devices to execute one or more Artificial Intelligence (AI) models configured to characterize one or more workloads executed by the IHS based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods provide adaptive collection of telemetry by an Information Handling System (IHS). Telemetry is identified that is ready for transmission by the IHS. A position of the telemetry is determined within a telemetry chain of telemetry generated by the IHS. A signaling pathway is selected for transmission of the telemetry based on the position of the telemetry within the telemetry chain.

Abstract: Systems and methods provide synchronized collection of telemetry by an Information Handling System (IHS) operating in a zero-trust computing environment. Telemetry is identified that is ready for transmission by the IHS. A policy decision point of the zero-trust computing environment is notified of the identified telemetry that is ready for transmission by the IHS. The IHS receives a counter from the policy decision point for ordering the telemetry that is ready for transmission within the telemetry of the zero-trust computing environment. The telemetry is annotated with the counter that is received from the policy decision point. The transmitted telemetry may then be sequenced with all other telemetry generated in the zero-trust computing environment.

Abstract: Systems and methods provide adaptive collection of telemetry. A telemetry orchestrator of a IHS (Information Handling System) collects telemetry related to a session used by the IHS to access a protected resource of a zero-trust environment, where the telemetry is collected based on a telemetry definition received from a policy decision point of the zero-trust environment. The telemetry orchestrator of the IHS monitors for updates to the telemetry definition, where the updates are generated by the policy decision point of the zero-trust environment. The telemetry orchestrator adjusts measurements by one or more of the sensors of the IHS based on updates to the telemetry definition received from the policy decision point. Telemetry that is generated based on the adjusted measurements is transmitted by the telemetry orchestrator to one or more destinations specified in the update telemetry definition.

Abstract: Systems and methods provided adaptive collection of telemetry. A policy decision point of a zero-trust computing environment receives an indication of a change in risk posture within the environment. The policy decision point identifies a telemetry definition specifying telemetry being collected by one or more IHSs that are currently accessing a protected resource of the zero-trust computing environment. The telemetry definition is updated to specify adjusted telemetry to be collected by an IHS that is currently accessing the protected resource and the updated telemetry definition is transmitted to the IHS. Based on the updated telemetry definition received from the policy decision point, the IHS adjust measurements by one or more of the sensors of the IHS. Telemetry generated based on the adjusted measurements is transmitted by the IHS to one or more destinations specified in the updated telemetry definition.

Abstract: An information handling system includes a hardware processor executing code instructions of an artificial intelligence (AI) productivity tool subagent to gather system/component context telemetry data and to invoke a telemetry data-to-vectorized telemetry intent machine learning (ML) model algorithm to vectorize the system/component context telemetry data and normalize each dimension in the vectorized system/component context telemetry data to a telemetry intent value using a hardware-specific normalization factor. The hardware processor executes code instructions of a query input-to-intent ML model algorithm to generate a query intent value for received user query input and concatenate the telemetry intent value with the query intent value to produce a unified telemetry and query intent value.

Abstract: A system and method for operating an on the box (OTB) artificial intelligence (AI) productivity tool comprising an embedded controller executing code instructions of a platform level capability gathering module to receive a notice of functional adjustment for a hardware component communicating with the embedded controller using one of several available communication protocols and the embedded controller to transmit an updated firmware capability to the OTB AI productivity tool pursuant to the notice of functional adjustment.

Abstract: A system and method of providing an intent action feedback summary at an information handling system comprising a hardware processor executing computer-readable program code instructions of an artificial intelligence (AI) productivity tool software module to receive a user query input and identify a plurality of responsive capabilities associated with AI productivity tool-enablable software applications and execute a transaction software module to determine that a capability intent action responsive to one of the identified plurality of responsive capabilities requires user approval or additional user chat input.

Abstract: An information handling system executing computer readable code instructions for an on the box artificial intelligence productivity tool comprising a hardware processor receiving a user query input within a current chat session with a user, executing computer-readable code instructions to perform a semantic similarity search at a main operating system (OS) level comparing a generated query input intent value to capability intent values generated from natural language descriptions of secondary OS capabilities and other software capabilities to identify a best match capability for the received user query input having a highest semantic similarity search score, storing the best match secondary OS capability in a non-volatile shared memory mailbox location, rebooting into a secondary lightweight OS with a secondary OS AI productivity tool retrieving the best match secondary OS capability for execution by a secondary lightweight OS and continuing the current chat session with a secondary OS conversational interfac

Abstract: A system and method comprising a hardware processor to execute computer-readable program code instructions of a diagnostic subagent to detect damage to a first port at the information handling system via port testing and generate diagnostic port data describing the first port that is damaged among a plurality of ports at the information handling system and execute a port identification and guidance module to gather baseline port mapping data describing mapping of hardware components including the ports within the information handling system and capabilities of those hardware components and the plurality of ports. Execute the port identification and guidance module to input the diagnostic port data and baseline port mapping data into a baseline mapping-to-text machine learning (ML) to generate user-guided text, audio, or image describing which port to use in lieu of the first port that is damaged.

Abstract: An information handling system executing computer readable code instructions for an on the box artificial intelligence (AI) productivity tool comprising a hardware processor receiving a user query input requesting erasing a drive memory of the information handling system, executing computer-readable code instructions to perform a semantic similarity search at a main operating system (OS) level comparing a generated query input intent value to capability intent values generated from natural language descriptions of secondary OS capabilities and other software capabilities to identify a best match secondary OS capability for the received user query input having a highest semantic similarity search score, storing the best match secondary OS capability in a non-volatile shared memory mailbox location, rebooting into a secondary lightweight OS executing a secondary OS AI productivity tool to retrieve the best match secondary capability for execution by the secondary lightweight OS to erase the drive memory.

Abstract: An information handling system includes a hardware processor to execute computer-readable program code instructions of an information technology decision maker (ITDM) capability management system to receive assigned artificial intelligence (AI) productivity scores correlated to how executed capability intent actions at a subset of a plurality of node information handling systems in an enterprise have improved user productivity at those plurality of node information handling systems based on detected changes resource telemetry data. The ITDM capability management system to generate a ranked listing of the plurality of responsive capability intent actions based on the assigned AI productivity scores and determine an action payload comprising one or more highest AI productivity score responsive capability intent actions and propagate that action payload to each of the plurality of node information handling systems within the enterprise for implementation.

Abstract: An information handling system operating an on-the-box artificial intelligence (AI) productivity tool comprising a hardware processor executing machine readable code instructions to receive an initial application capabilities list for AI productivity tool enableable software applications at a capabilities database and receive a general issued configuration policy with alternate settings from a management server dictating controlled operational modes of the information handling system.

Abstract: An information handling system managed by an enterprise management system and operating an on-the-box artificial intelligence productivity tool may comprise a hardware processor executing machine readable code instructions to generate vectorized capability intent values from natural language descriptions of capabilities including a capability-with-reboot requiring reboot to a basic input/output system (BIOS) and a vectorized query input intent value for a user query input requesting action from information handling system, compare the query input intent value to the capability intent values to identify a best match responsive capability-with-reboot for the received user query input having a capability intent value that generates a highest semantic similarity search score and requiring authorization for execution of pre-boot machine readable code instructions for the best match capability-with-reboot received from the enterprise management system, store it in a pre-boot memory partition, reboot, and execute th