Abstract: In accordance with examples of the present disclosure, a collaborative platform provides a digital collaboration assistant that continuously monitors and analyzes shared meeting contents (e.g., voice, text chat messages, shared links and documents, presentation materials, and the like) by participants during a collaborative meeting in near real-time, periodically updates a structure summary log of the meeting contents that are deemed important during the collaborative meeting, and interacts with the participants throughout the collaborative meeting in near real-time, for example, to answer questions or provide additional information.

Abstract: In accordance with examples of the present disclosure, a collaborative platform provides a digital collaboration assistant that continuously monitors and analyzes shared meeting contents (e.g., voice, text chat messages, shared links and documents, presentation materials, and the like) by participants during a collaborative meeting in near real-time, periodically updates a structure summary log of the meeting contents that are deemed important during the collaborative meeting, and interacts with the participants throughout the collaborative meeting in near real-time, for example, to answer questions or provide additional information.

Abstract: In accordance with examples of the present disclosure, a collaborative platform provides a digital collaboration assistant that continuously monitors and analyzes shared meeting contents (e.g., voice, text chat messages, shared links and documents, presentation materials, and the like) by participants during a collaborative meeting in near real-time, periodically updates a structure summary log of the meeting contents that are deemed important during the collaborative meeting, and interacts with the participants throughout the collaborative meeting in near real-time, for example, to answer questions or provide additional information.

Abstract: Aspects of the present application relate to machine learning (ML) structured result generation. In examples, an instruction of programmatic code that invokes an ML model indicates a result interface in which model output is to be stored. The result interface is processed to generate a data format description for the result interface, such that the input to the ML model further includes the data format description. As a result of providing the data format description as input to the ML model, the ML model is induced to generate structured model output that corresponds to the result interface. The resulting model output is processed to generate an instance of the result interface, for example having one or more corresponding properties from the structured model output. Accordingly, the programmatic code is able to reliably perform subsequent processing based on the generated instance of the result interface.

Abstract: A memory system and method for storing data in one or more storage chips includes: one or more memory cards each having a plurality of storage chips, and each chip having a plurality of dies having a plurality of memory cells; a memory controller comprising a translation module, the translation module further comprising: a logical to virtual translation table (LVT) having a plurality of entries, each entry in the LVT configured to map a logical address to a virtual block address (VBA), where the VBA corresponds to a group of the memory cells on the one or more memory cards, wherein each entry in the LVT further includes a write wear level count to track the number of writing operations to the VBA, and a read wear level count to track the number of read operations for the VBA mapped to that LVT entry.

Abstract: A memory system and method for storing data in one or more storage chips includes: one or more memory cards each having a plurality of storage chips, and each chip having a plurality of dies having a plurality of memory cells; a memory controller comprising a translation module, the translation module further comprising: a logical to virtual translation table (LVT) having a plurality of entries, each entry in the LVT configured to map a logical address to a virtual block address (VBA), where the VBA corresponds to a group of the memory cells on the one or more memory cards, wherein each entry in the LVT further includes a write wear level count to track the number of writing operations to the VBA, and a read wear level count to track the number of read operations for the VBA mapped to that LVT entry.

Abstract: A memory system and method for storing data in one or more storage chips includes: one or more memory cards each having a plurality of storage chips, and each chip having a plurality of dies having a plurality of memory cells; a memory controller comprising a translation module, the translation module further comprising: a logical to virtual translation table (LVT) having a plurality of entries, each entry in the LVT configured to map a logical address to a virtual block address (VBA), where the VBA corresponds to a group of the memory cells on the one or more memory cards, wherein each entry in the LVT further includes a write wear level count to track the number of writing operations to the VBA, and a read wear level count to track the number of read operations for the VBA mapped to that LVT entry.

Abstract: A computer-implemented method (and associated computing system and computer program product) comprises distributing computing workloads among a plurality of processing units of a computing system. The method further comprises, responsive to detecting a reduced power supply capacity of the computing system: determining, based on historical workload information, a first workload of the computing workloads to prioritize the completion thereof, and prioritizing power delivery to a first processing unit of the plurality of processing units to which the first workload is distributed. The method further comprises powering down the first processing unit responsive to completion of the first workload.

Abstract: A computer-implemented method (and associated computing system and computer program product) comprises distributing computing workloads among a plurality of processing units of a computing system. The method further comprises, responsive to detecting a reduced power supply capacity of the computing system: determining, based on historical workload information, a first workload of the computing workloads to prioritize the completion thereof, and prioritizing power delivery to a first processing unit of the plurality of processing units to which the first workload is distributed. The method further comprises powering down the first processing unit responsive to completion of the first workload.