Google Patente

Implementations described herein relate to receiving user input directed to an automated assistant, processing the user input to determine whether data from a server and/or third-party application is needed to perform certain fulfillment of an assistant command included in the user input, and generating a prompt that requests a user consent to transmitting of a request to the server and/or the third-party application to obtain the data needed to perform the certain fulfillment. In implementations where the user consents, the data can be obtained and utilized to perform the certain fulfillment. In implementations where the user does not consent, client data can be generated locally at a client device and utilized to perform alternate fulfillment of the assistant command. In various implementations, the request transmitted to the server and/or third-party application can be modified based on ambient noise captured when the user input is received.

Implementations relate to determination and visual display of spoken menus for calls. In some implementations, a computer-implemented method includes receiving audio data output in a call between a call device and a device associated with a target entity. The audio data includes speech indicating one or more selection options for a user of the call device to navigate through a call menu provided by the target entity in the call. Text is determined by programmatically analyzing the audio data, the text representing the speech. The selection options are determined based on programmatically analyzing at least one of the text or the audio data. At least a portion of the text is displayed by the call device during the call, as one or more visual options that correspond to the selection options. The visual options are each selectable via user input to cause corresponding navigation through the call menu.

The present disclosure describes apparatuses and techniques of adaptive PLMN management for varying network conditions. In some aspects, a PLMN manager (144) of a user equipment (UE, 102) searches, as part of a registration procedure, a data repository of PLMN information that includes a PLMN (104, 106) blocked from registration for a duration of time and determines that the PLMN is blocked due to a signal-related condition with a cell (110, 112) of the PLMN (e.g., weak signal). The PLMN manager (144) then determines, during the duration of time, that the signal-related condition between the UE (102) and the cell (110, 112) has improved. In response to the improvement, the PLMN manager (144) unblocks the PLMN (104, 106) before expiration of the duration of time. By so doing, the UE (102) may reattempt registration with the unblocked PLMN (104, 106) at least once before the duration of time expires, which may allow the UE to register more quickly and reestablish network services.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a system-level cache to allocate cache resources by a way-partitioning process. One of the methods includes maintaining a mapping between partitions and priority levels and allocating primary ways to respective enabled partitions in an order corresponding to the respective priority levels assigned to the enabled partitions.

Mechanisms for selecting formats for streaming media content items are provided. In some embodiments, methods for selecting formats for streaming media content items are provided that include: receiving, at a server from a user device, a request to begin streaming a video content item on the user device; receiving, from the user device, network information indicating a quality of a network connection of the user device to a communication network used to stream the video content item and device information related to the user device; selecting, by the server, a first format for the video content item, wherein the first format includes a first resolution of a plurality of resolutions based on the network information and the device information; transmitting, from the server, a first portion of the video content item having the first format to the user device; receiving, at the server from the user device, updated network information and updated device information; selecting, by the server, a second format for the video content item, wherein the second format includes a second resolution of the plurality of resolutions based on the updated network information and the updated device information; and transmitting, from the server, a second portion of the video content item having the second format to the user device.

The present disclosure relates to a method, system and non-transitory computer readable storage medium. The method involves operating a machine learning accelerator, comprising steps of: executing, by a scalar core directing a plurality of compute units of the machine learning accelerator, a first process in a first context, wherein the first process is a long-running process; identifying, by a job scheduler of the machine learning accelerator, that a second process is queued, wherein the second process has a higher priority than a priority of the long-running process, and upon reaching a preemption checkpoint: determining, by the scalar core, an amount of available resources and in response to the amount of available resources being sufficient for the second process: pausing, by the scalar core, execution of the first process; allocating available resources to the second process; switching, by the scalar core, to a second context; executing, by the scalar core, the second process; switching, by the scalar core, to the first context; and resuming, by the scalar core, execution of the first process.

Systems and methods of the present disclosure are directed to a computing system. The computing system can obtain a message vector and video data comprising a plurality of video frames. The computing system can process the input video with a transformation portion of a machine-learned watermark encoding model to obtain a three-dimensional feature encoding of the input video. The computing system can process the three-dimensional feature encoding of the input video and the message vector with an embedding portion of the machine-learned watermark encoding model to obtain spatial-temporal watermark encoding data descriptive of the message vector. The computing system can generate encoded video data comprising a plurality of encoded video frames, wherein at least one of the plurality of encoded video frames includes the spatial-temporal watermark encoding data.

A method for on-device artificial intelligence (AI) includes obtaining, at a client device, a request to perform a task. The method includes providing the request as input to a generative AI model operating on the client device. The generative AI model may be trained to identify one or more applications capable of performing the task and to generate instructions related to the task for the one or more applications. The method includes causing the instructions related to the task to be provided to the one or more applications. The method includes receiving an indication of completion of at least a portion of the task. The method includes providing, at the client device, information regarding the completion of the at least a portion of the task.

A method (500) includes obtaining proximity information (250) for each of a plurality of assistant-enabled devices (210) within an environment (32) of a user device (200). Each assistant-enabled device is controllable by an assistant application (106) to perform a respective set of available actions (120) associated with the assistant-enabled device. For each assistant-enabled device, the method also includes determining a proximity score (260) based on the proximity information indicating a proximity estimation of the corresponding assistant-enabled device relative to the user device. The method further includes generating, using the proximity scores determined for the assistant-enabled devices, a ranked list (310) of candidate assistant-enabled devices, and for each corresponding assistant-enabled device in the ranked list, displaying, in a graphical user interface (GUI) (400), a respective set of controls (220) for performing the respective set of actions associated with the corresponding assistant-enabled device.

Generally disclosed herein is a mechanism to mitigate power fluctuations of a data center by dynamically charging and discharging a battery energy storage system (BESS). According to some examples, a BESS control system can be configured to monitor power demand fluctuations and initiate charging of the BESS during periods of low power demand. One example of a period of low power demand includes active idle, wherein the workloads of the server machines frequently decrease by a small magnitude. Another example of a period of low power demand includes deep idle, wherein the workloads of the server machines decrease by a larger magnitude than the active idle for a longer duration. The system may discharge the power from the BESS during the peak power demand. The BESS control system may stabilize the data center power system through fast-acting voltage and frequency control at a system level.

A system for repair of an accelerator architecture including a plurality of accelerator host having a plurality of accelerator elements in each host and high-speed interconnects between accelerator elements and including a link health monitor service in the host determining a status of each interconnect internal and external to the host. The link health monitor service detects link status of the interconnects and communicates this to a pod manager responsible for managing a number of interconnected hosts. The pod manager communicates with a scheduler for scheduling processing tasks to each accelerator element or group of elements. Upon detection of an interconnect problem, the pod manager flags a least common ancestor in a binary tree of elements containing elements adjacent to the failed interconnect and flags the ancestor and its parent nodes as unavailable. Pod manager further generates a corrective action repairing the problem interconnect communicating the action to a technician.

The technology is directed to systems and methods of high-bandwidth memory allocation. High-bandwidth memory may include a plurality of data channels and shareable memory that can be selectively allocated to particular data channels. In addition, bandwidth may be selectively allocated to the data channels independent of the shareable memory. The allocation of memory and bandwidth to particular data channels may be based on identified attributes of workloads that are to be associated with each data channel.

Generally disclosed herein is a mechanism to mitigate power fluctuations of a data center by dynamically charging and discharging a capacitor-based energy storage device. According to some examples, a supercapacitor control system can be configured to monitor power demand fluctuations and initiate charging of the supercapacitor during a workload ramp-up period for training machine learning (ML) models with massive training data. As the system starts discharging the supercapacitors during the ramp-up period, the system is capable of reducing the rate of the power demand increase. Similarly, during a ramp-down period, the system is configured to charge the supercapacitor as the training of the ML model is being finished and the power demand decreases. The power stored in the supercapacitors may be used to reduce the rate of the power demand decrease when the training of the ML model is being completed, thereby maintaining the stability of the power utility.

A liquid-cooled rack for liquid-cooling trays of computing equipment includes an inlet manifold including an inlet for receiving a coolant at a first temperature and at least one outlet interface for discharging the coolant to one of the liquid-cooled trays; and an outlet manifold including at least one inlet interface for receiving the coolant at a second temperature higher than the first temperature from one of the liquid-cooled trays and an outlet for discharging the coolant. At least one of the inlet manifold or the outlet manifold includes at least one coolant drain port to facilitate the draining and purge drying process. The drain ports can also be added to each tray. The draining and purge drying process can be enhanced by using high pressure air (desiccated and/or heated), flushing with volatile fluid, tilting, vibration, vacuuming.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for updating a display of a client device. In one aspect, a probabilistic determination is performed as to how a particular content item was visually represented at a client device over multiple different presentations of the particular content item on multiple different devices and multiple different online resources. It is counted how many times the particular content item was presented using a visual representation when presented on a particular type of client device. A particular visual representation of the content item is determined that was presented on the particular type of client device most often. A given display card that includes the particular visual representation of the particular content item is generated. A visualization of a dashboard user interface is generated, including inserting the given display card into the dashboard user interface, and wherein the visualization of the dashboard interface is selected based the type of client device and the given display card presents the particular content item using the particular visual representation. A display of the client device is updated to present the visualization of the dashboard user interface.

One example aspect is directed to a computer-implemented method (400) for performing model decoding with reduced latency. The method includes obtaining (402) a pre-trained sequence processing model comprising a plurality of layers. The method includes modifying (404) the sequence processing model to contain an adapter layer (106) that is configured to receive and process an intermediate representation generated by a particular intermediate layer of the plurality of layers to predict an output token. The method includes training (406) the adapter layer while holding the plurality of layers of the sequence processing model frozen. The method includes deploying (408) the sequence processing model for speculative decoding in which the adapter layer, the particular intermediate layer, and the plurality of layers (104) that precede the particular intermediate layer perform speculative token decoding and the plurality of layers (108) that are subsequent to the particular intermediate layer perform token verification.

An insect storage and release device includes suitably rigid container that may be used to store and transport insects. The insect storage and release device may hold compressed insects or non-compressed insects. The insect storage and release device includes a removable enclosure and a reticulated foam for adding compliance during packing of the insects.

Various implementations include reducing latency when interacting with a generative model system based on generating predicted complete text based on natural language (NL) text input, where the NL text input is a portion of a user query. In many implementations, predicted completion text can be generated by processing NL text input using a language model. In several implementations, the system can perform initial processing of the NL input text and the predicted completion text (e.g., preform initial preprocessing of the NL input text and predicted completion text for processing using the generative model, performing an initial limited decoding of output using the generative model, etc.). The user can confirm the predicted completion text before the system continues processing the NL input and predicted completion text using the generative model to generate output.

A media application receives user input that indicates one or more objects to be erased from a media item. The media application translates the user input to a bounding box. The media application provides a crop of the media item based on the bounding box to a segmentation machine-learning model. The segmentation machine-learning model outputs a segmentation mask for one or more segmented objects in the crop of the media item and a corresponding segmentation score that indicates a quality of the segmentation mask.

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …