Abstract: Techniques for identifying components (e.g., application program interfaces (APIs)) relevant for a large language model (LLM) prompt are described. The shortlister includes one or more proposers that each select n components for performing an instant task. The n components from each proposer may be merged and reranked to produce a ranked list of K components for inclusion in the LLM prompt. The shortlister proposers can include, among others, a request-based proposer, query-based proposer, and/or rule-based proposer. In particular, the query-based proposer may utilize a LLM to generate a query to search component descriptions to identify n components the proposer determines are usable to perform the instant task.

Abstract: A system facilitates a process for automatically generating artificial intelligence (AI) models. The system receives a first natural language input from a user that includes a set of phrases and an instruction to analyze data associated with the set of phrases using an AI model. The system accesses a metadata graph to determine a node corresponding to the set of phrases, where nodes in the metadata graph indicate internal data objects stored in data silos. The system processes the internal data objects indicated by the determined node to generate a first set of application data. The AI model is applied to the first set of application data to generate one or more outputs. Additional user inputs can be received to modify the first set of application data and apply the AI model to the modified data, until a desired data pipeline for the model has been constructed.

Abstract: Techniques for improving query generation (or generation of an input to another component) by a language model are described. In some embodiments, the generated query is used to retrieve API calls relevant for responding to a user input. The language model can be finetuned using language model generated queries. Results retrieved using the generated query are evaluated against ground truth data to determine performance metrics data, which may be based on a ranking of the ground truth API call in the retrieved results. Based on the performance metrics data, training data including the generated query can be used to update/finetune the language model may be determined. Updating of the language model may be initiated based on feedback corresponding to the generated query.

Abstract: A method includes displaying, on a graphical user interface, a planned position of a virtual implant model relative to a virtual bone model of a bone. The virtual implant model includes a plurality of virtual screw holes offset from a central axis of the virtual implant model. The method also includes determining a planned rotational orientation of the virtual implant model by rotating, on the graphical user interface, the virtual implant model about the central axis of the virtual implant model such that the plurality of virtual screw holes rotate about the central axis. The method also includes controlling a robotic device to guide preparation of the bone to receive a physical implant in the planned position. The method also includes providing computer-assisted navigation configured to guide the physical implant into physical rotational alignment with the planned rotational orientation of the virtual implant model.

Abstract: A method includes obtaining a surgical plan including a removal of a first physical implant from a bone and a replacement of the first physical implant with a second physical implant. The method also includes receiving a first virtual model including a first virtual implant model relative to a first virtual bone surface of a bone. The first virtual bone surface represents a first bone surface to which the first physical implant is coupled. The method also includes identifying a second bone surface resulting from removal of the first physical implant from the bone by tracking a position of a surgical tool relative to the bone. The method also includes detecting a discrepancy between the first bone surface and the second bone surface and displaying, on a graphical user interface, a visualization of the discrepancy.

Abstract: Systems and methods for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph are disclosed. The system receives a user-specified query indicating a request to access a set of data objects. The system then performs natural language processing on the user-specified query to determine a set of phrases corresponding to the user-specified query. The system then accesses a metadata graph to determine a node corresponding to the set of phrases. Using a location identifier corresponding to the determined node, the system determines a data silo storing at least one data object of the set of data objects. The system then generates for display, on a graphical user interface, a visual representation of the at least one data object.

Abstract: A system facilitates a process for automatically deploying artificial intelligence (AI) models. The system receives, for a first artificial intelligence (AI) model used by an entity, a first request to deploy the first AI model to make the first AI model available for use in a production environment to process input data and generate corresponding outputs. A first model deployment location for the first model is selected based on a model deployment engine. The system generates scripts to deploy the first AI model to the selected location, then monitors operations parameters associated with the deployment of the first AI model. Based on the values of the operations parameters, the system updates the model deployment engine. In response to a second request to deploy a second AI model, the system uses the updated model deployment engine to select a second model deployment location for the second model.

Abstract: Systems and methods for performing data analytics on sensitive data, such as deduplication are disclosed herein. A first request to perform a first data analytics operation for a set of sensitive data instances may be received. A first set of image representations for the set of sensitive data instances may be retrieved. A first sensitive data instance and a second sensitive data instance may be clustered into a first cluster. The first data analytics operation may be performed on the first cluster.

Abstract: A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by a third party. The system allows the data to be shared across jurisdiction boundaries with complete auditability, traceability, and compliance. The system allows a first jurisdiction computing device to control the usage of the data that is stored outside of the jurisdiction. The technology allows the first jurisdiction to propagate rules, tokenization protocols, and updates to the system to manage the sensitive data. The system detokenizes the data when the data is to be used for an approved purpose by an approved party. If the original jurisdiction has a change in permissions for sensitive data, the jurisdiction can propagate a tokenization to all data stored in the data management system outside of the jurisdiction.

Abstract: A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by another party. The system may receive data that includes encoded data at a first device from a second device (e.g., associated with a remote datacenter). The system may store the data at the first device. In response to receiving a request from a third entity, the system may request a cryptographic key for decoding one or more data fields of the encoded data. Based on decoding the associated field data, the system may transmit a response to the data request that includes the decoded data.

Abstract: A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by a third party. The system allows the data to be shared across jurisdiction boundaries with complete auditability, traceability, and compliance. The system allows a first jurisdiction computing device to control the usage of the data that is stored outside of the jurisdiction. The technology allows the first jurisdiction to propagate rules, tokenization protocols, and updates to the system to manage the sensitive data. The system detokenizes the data when the data is to be used for an approved purpose by an approved party. If the original jurisdiction has a change in permissions for sensitive data, the jurisdiction can propagate a tokenization to all data stored in the data management system outside of the jurisdiction.

Abstract: A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by another party. The system may receive data that includes encoded data at a first device from a second device (e.g., associated with a remote datacenter). The system may store the data at the first device. In response to receiving a request from a third entity, the system may request a cryptographic key for decoding one or more data fields of the encoded data. Based on decoding the associated field data, the system may transmit a response to the data request that includes the decoded data.

Abstract: A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by another party. The system may receive data that includes encoded data at a first device from a second device (e.g., associated with a remote datacenter). The system may store the data at the first device. In response to receiving a request from a third entity, the system may request a cryptographic key for decoding one or more data fields of the encoded data. Based on decoding the associated field data, the system may transmit a response to the data request that includes the decoded data.

Abstract: A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by a third party. The system allows the data to be shared across jurisdiction boundaries with complete auditability, traceability, and compliance. The system allows a first jurisdiction computing device to control the usage of the data that is stored outside of the jurisdiction. The technology allows the first jurisdiction to propagate rules, tokenization protocols, and updates to the system to manage the sensitive data. The system detokenizes the data when the data is to be used for an approved purpose by an approved party. If the original jurisdiction has a change in permissions for sensitive data, the jurisdiction can propagate a tokenization to all data stored in the data management system outside of the jurisdiction.

Abstract: A system facilitates a process for automatically deploying artificial intelligence (AI) models. The system receives, for a first artificial intelligence (AI) model used by an entity, a first request to deploy the first AI model to make the first AI model available for use in a production environment to process input data and generate corresponding outputs. A first model deployment location for the first model is selected based on a model deployment engine. The system generates scripts to deploy the first AI model to the selected location, then monitors operations parameters associated with the deployment of the first AI model. Based on the values of the operations parameters, the system updates the model deployment engine. In response to a second request to deploy a second AI model, the system uses the updated model deployment engine to select a second model deployment location for the second model.

Abstract: Systems and methods for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph are disclosed. The system receives a user-specified query indicating a request to access a set of data objects. The system then performs natural language processing on the user-specified query to determine a set of phrases corresponding to the user-specified query. The system then accesses a metadata graph to determine a node corresponding to the set of phrases. Using a location identifier corresponding to the determined node, the system determines a data silo storing at least one data object of the set of data objects. The system then generates for display, on a graphical user interface, a visual representation of the at least one data object.

Abstract: Systems and methods for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph are disclosed. The system receives a user-specified query indicating a request to access a set of data objects. The system then performs natural language processing on the user-specified query to determine a set of phrases corresponding to the user-specified query. The system then accesses a metadata graph to determine a node corresponding to the set of phrases. Using a location identifier corresponding to the determined node, the system determines a data silo storing at least one data object of the set of data objects. The system then generates for display, on a graphical user interface, a visual representation of the at least one data object.

Abstract: A system facilitates a process for automatically deploying artificial intelligence (AI) models. The system receives, for a first artificial intelligence (AI) model used by an entity, a first request to deploy the first AI model to make the first AI model available for use in a production environment to process input data and generate corresponding outputs. A first model deployment location for the first model is selected based on a model deployment engine. The system generates scripts to deploy the first AI model to the selected location, then monitors operations parameters associated with the deployment of the first AI model. Based on the values of the operations parameters, the system updates the model deployment engine. In response to a second request to deploy a second AI model, the system uses the updated model deployment engine to select a second model deployment location for the second model.

Abstract: A system facilitates a process for automatically generating artificial intelligence (AI) models. The system receives a first natural language input from a user that includes a set of phrases and an instruction to analyze data associated with the set of phrases using an AI model. The system accesses a metadata graph to determine a node corresponding to the set of phrases, where nodes in the metadata graph indicate internal data objects stored in data silos. The system processes the internal data objects indicated by the determined node to generate a first set of application data. The AI model is applied to the first set of application data to generate one or more outputs. Additional user inputs can be received to modify the first set of application data and apply the AI model to the modified data, until a desired data pipeline for the model has been constructed.

Abstract: Aspects disclosed in the detailed description include power saving techniques in computing devices. In particular, as data is received by a modem processor in a computing device, the data is held until the expiration of a modem timer. The data is then passed to an application processor in the computing device over a peripheral component interconnect express (PCIe) interconnectivity bus. On receipt of the data from the modem processor, the application processor sends data held by the application processor to the modem processor over the PCIe interconnectivity bus. The application processor also has an uplink timer. If no data is received from the modem processor before expiration of the uplink timer, the application processor sends any collected data to the modem processor at expiration of the uplink timer. However, if data is received from the modem processor, the uplink timer is reset.