Abstract: Methods for centralized access control for cloud relational database management system resources are performed by systems and devices. The methods utilize a central policy storage, managed externally to database servers, which stores external policies for access to internal database resources at up to fine granularity. Database servers in the processing system each receive external access policies that correspond to users of the system by push or pull operations from the central policy storage, and store the external access policies in a cache of the database servers for databases. For resource access, access conditions are determined via policy engines of database servers based on an external access policy in the cache that corresponds to a user, responsive to a resource access request from a device of the user specifying the internal resource. Data associated with the resource is provided to the user based on the access condition being met.

Abstract: Grippers, such as robotic grippers, and associated methods and systems are disclosed herein. One disclosed gripper includes a first compliant gripper jaw, a first push-pull assembly having a first distal end fixed on the first compliant gripper jaw, a second compliant gripper jaw, a second push-pull assembly having a second distal end fixed on the second compliant gripper jaw, and an actuator connected to both the first push-pull assembly and the second push-pull assembly.

Abstract: Provided is a knowledge distillation technique for training a student language model that, relative to a larger teacher language model, has a significantly smaller vocabulary, lower embedding dimensions, and/or hidden state dimensions. Specifically, aspects of the present disclosure are directed to a dual-training mechanism that trains the teacher and student language models simultaneously to obtain optimal word embeddings for the student vocabulary. In some implementations, this approach can be combined with learning shared projection matrices that transfer layer-wise knowledge from the teacher language model to the student language model. Example experimental results have also demonstrated higher compression efficiency and accuracy when compared with other state-of-the-art compression techniques, including the ability to compress the BERTBASE model by more than 60×, with only a minor drop in downstream task metrics, resulting in a language model with a footprint of under 7 MB.

Abstract: Provided is a knowledge distillation technique for training a student language model that, relative to a larger teacher language model, has a significantly smaller vocabulary, lower embedding dimensions, and/or hidden state dimensions. Specifically, aspects of the present disclosure are directed to a dual-training mechanism that trains the teacher and student language models simultaneously to obtain optimal word embeddings for the student vocabulary. In some implementations, this approach can be combined with learning shared projection matrices that transfer layer-wise knowledge from the teacher language model to the student language model. Example experimental results have also demonstrated higher compression efficiency and accuracy when compared with other state-of-the-art compression techniques, including the ability to compress the BERTBASE model by more than 60×, with only a minor drop in downstream task metrics, resulting in a language model with a footprint of under 7 MB.

Abstract: In one embodiment, a method is illustrated as including defining a set of perspective objects capable of being placed onto a modified web page, monitoring parameters of a web page, the parameters including a number of times a current object is executed on the web page, using an Artificial Intelligence (AI) algorithm to determine a perspective object with a preferred Return On Investment (ROI), and selecting the perspective object to be placed onto the modified web page.

Abstract: The present invention provides a system and method to create a multi attribute based data synthesis. The method includes obtaining an input image, where the input image includes a human wearing an apparel in a foreground and a scene in a background, detecting the human in the input image, detecting a face region of the human in the input image, generating a body-ratio-heuristics for the input image, generating a set of mask labels associated with the input image, and performing a domain randomization of the input image to generate a plurality of images to create the image database.

Abstract: A system to provide image processing services responsive to requests including image data includes a system layer that forwards a request to an image application processing interface. Image processing provides an image comparison, barcode recognition, and optical character recognition. The image processing compares the image data to products in a database in order to identify a matching product. The system layer receives the matching information and forwards to a user.

Abstract: In one embodiment, a method is illustrated as including defining a set of perspective objects capable of being placed onto a modified web page, monitoring parameters of a web page, the parameters including a number of times a current object is executed on the web page, using an Artificial Intelligence (AI) algorithm to determine a perspective object with a preferred Return On Investment (ROI), and selecting the perspective object to be placed onto the modified web page.

Abstract: In an example embodiment, a method of identifying an item depicted in an image is provided. In this method, the image depicting the item is accessed; in addition, other images and their item identifiers are also accessed. A match of the image with one of the other images is identified. With a match, the image is then associated with an item identifier of the matched image.

Abstract: Generally, the present disclosure is directed to systems and methods for performing task-oriented response generation that can provide advantages for artificial intelligence systems or other computing systems that include natural language processing for interpreting user input. Example implementations can process natural language descriptions of various services that can be accessed by the system. In response to a natural language input, systems can identify relevant values for executing one of the service(s), based in part on comparing embedded representations of the natural language input and the natural language description using a machine learned model.

Abstract: A computing system can facilitate an on-demand delivery service by receiving menu item requests and transmit corresponding order requests to menu item preparers. The system can execute one or more trained predictive models using a set of current predictive metrics for the menu item preparer to generate probability curves for driver wait time and menu item sit time against a logical cost to the on-demand delivery service. The system may then utilize the curves to determine an optimal arrival time for a selected delivery provider to pick up the menu items for delivery.

Abstract: A system to process navigation information includes a receiving module to receive a data request from a network entity. The data request includes a network entity identifier that uniquely identifies the network entity. The system further includes an application to generate a network event based on the data request, the network event including the navigation information. Finally, the system includes a publisher module to communicate the network event to a first server that is distinguished from a first plurality of servers based on the network entity identifier.

Abstract: In one embodiment, a method is illustrated as including defining a set of perspective objects capable of being placed onto a modified web page, monitoring parameters of a web page, the parameters including a number of times a current object is executed on the web page, using an Artificial Intelligence (AI) algorithm to determine a perspective object with a preferred Return On Investment (ROI), and selecting the perspective object to be placed onto the modified web page.

Abstract: In one embodiment, a method is illustrated as including defining a set of perspective objects capable of being placed onto a modified web page, monitoring parameters of a web page, the parameters including a number of times a current object is executed on the web page, using an Artificial Intelligence (AI) algorithm to determine a perspective object with a preferred Return On Investment (ROI), and selecting the perspective object to be placed onto the modified web page.

Abstract: Provided is a knowledge distillation technique for training a student language model that, relative to a larger teacher language model, has a significantly smaller vocabulary, lower embedding dimensions, and/or hidden state dimensions. Specifically, aspects of the present disclosure are directed to a dual-training mechanism that trains the teacher and student language models simultaneously to obtain optimal word embeddings for the student vocabulary. In some implementations, this approach can be combined with learning shared projection matrices that transfer layer-wise knowledge from the teacher language model to the student language model. Example experimental results have also demonstrated higher compression efficiency and accuracy when compared with other state-of-the-art compression techniques, including the ability to compress the BERTBAsE model by more than 60×, with only a minor drop in downstream task metrics, resulting in a language model with a footprint of under 7 MB.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for dialogue systems. A transcription of a user utterance is obtained. The transcription of the utterance is tokenized to identify multiple tokens for the utterance. Token-level utterance encodings corresponding to different tokens of the transcription are generated. A system action encoding from data indicating system actions previously performed by the dialogue system are generated. A dialogue context vector based on the utterance encoding and the system action encoding are generated. The token-level utterance encodings, the system action encoding, and the dialogue context vector are processed using a slot tagger to produce token-level output vectors. A limited set of candidate token classifications for the tokens of the user utterance are determined based on the token-level utterance encodings. A response for output is provided in response to the user utterance.

Abstract: Grippers, such as robotic grippers, and associated methods and systems are disclosed herein. One disclosed gripper includes a first compliant gripper jaw, a first push-pull assembly having a first distal end fixed on the first compliant gripper jaw, a second compliant gripper jaw, a second push-pull assembly having a second distal end fixed on the second compliant gripper jaw, and an actuator connected to both the first push-pull assembly and the second push-pull assembly.

Abstract: Fill stations, cup dispensers and faucet clips configured for robotic interaction and associated methods and systems are disclosed herein. One disclosed system includes a robot with a visible light sensor and an end effector, a mechanical scale for weighing a vessel, and a visual flag translated by the mechanical scale. The visual flag is hidden when the vessel is below a target weight and is revealed when the vessel is above a target weight. The robot is programmed to detect the visual flag using the visible light sensor and disengage a dispenser of the fill station in response to the detecting of the visual flag.

Abstract: Generally, the present disclosure is directed to systems and methods for performing task-oriented response generation that can provide advantages for artificial intelligence systems or other computing systems that include natural language processing for interpreting user input. Example implementations can process natural language descriptions of various services that can be accessed by the system. In response to a natural language input, systems can identify relevant values for executing one of the service(s), based in part on comparing embedded representations of the natural language input and the natural language description using a machine learned model.