Abstract: An information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool may comprise a hardware processor executing computer-readable code instructions to access gathered natural language descriptions of application, firmware, and hardware capabilities, receive an error indicating a software, firmware, or hardware failure or malfunction, determine, via a trained neural network, determine that the error indicates imminent failure of a hard disk drive (HDD), generate a natural language description of a recommendation to replace the HDD, generate an intervention recommendation input intent value for the natural language description, and identify and execute a best match responsive capability for the received natural language description having a capability intent value that generates a highest semantic similarity search score generated through comparison of the intervention recommendation input intent value and a plurality of capability

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: Systems and methods for prioritizing loading of agents and plugins during boot time of an IHS are described. In an illustrative, non-limiting embodiment, a first Information Handling System (IHS) may include: a processor; and a memory coupled to the processor, wherein the memory comprises program instructions store thereon that, upon execution by the processor, cause the IHS to: obtain behavior information of one or more users of the IHS regarding at least one application of the IHS; and launch an agent associated with the application during a boot of the IHS, or load a plugin associated with the application during the boot of the IHS, based, at least in part, on the behavior information.

Abstract: A system and method of undoing capability intent actions of an artificial intelligence (AI) productivity tool chatbot software application comprising processing a received first user-query input and generating a query intent action, correlating a capability intent action with the user-query inputs by invoking an ML model algorithm to correlate the query intent value to a capability intent value for a capability to be executed by an AI productivity tool-enablable software application as a responsive capability intent action, initiating the capability intent action and identifying a negated intent that describes a reversing action that may be executed to reverse the capability intent action that is correlated to the first user query input, and generating a system state defining the state of the information handing system prior to the capability intent action being executed by the AI productivity tool-enablable software application.

Abstract: An information handling system includes a hardware processor to execute computer-readable program code instructions of an AI productivity tool software module to process an initial user query input received from a user and correlate the user query input to a capability intent action and to initiate the capability intent action to respond to the user query input, and execute a sentiment analysis software application to analyze a subsequent user query input and generate feedback sentiment data describing a positive or negative sentiment of the subsequent user query input in a generated transaction log including the initial user query input, the responsive capability intent action, the subsequent user query input and a feedback sentiment score of the subsequent use query input that is transmitted to a remote policy management server for an intent technology decision maker to generate capability intent action policies based on the feedback sentiment data.

Abstract: Systems and methods for Artificial Intelligence (AI) model characterization and offloading in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform including a plurality of devices and a memory, where the memory includes a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables it to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: receive a characterization result from a first device, where the first device is configured to execute a first AI model configured to characterize a second AI model executed by a second device, and, prior to a subsequent execution of the second AI model, assign the second AI model to a third device based, at least in part, upon the characterization result.

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.