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: 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, methods, and devices detect audio signals. Methods may include receiving an audio input at an audio front end circuit, starting, using a low power circuit, one or more buffers in response to receiving the audio input, and identifying, using the low power circuit, speech included in the audio input. Methods may also include identifying, using the low power circuit and a high performance circuit, a wake word based, at least in part, on the identified speech, the high performance circuit being configured to verify the wake word identified by the low power circuit.

Abstract: Methods and systems for communication and sensing systems. The disclosed method includes, among other things, responsive to receiving a plurality of channel frequency response (CFR) measurements from an antenna operating at a low-bandwidth range, generating a channel state information (CSI) across a high-bandwidth range based on the plurality of CFR measurements and determining, based on the CSI, a Doppler shift across the high-bandwidth range.

Abstract: The present disclosure provides an approach that captures one or more wireless signals in a geographic area. Each one of the one or more wireless signals includes channel state information (CSI) data. The present disclosure produces a channel state information (CSI) representation based on the CSI data that indicates multiple channel responses corresponding to the one or more wireless signals. The present disclosure filters the CSI representation to remove at least one of the channel responses that correspond to a stationary object within the geographic area to produce a filtered CSI representation. The present disclosure predicts a presence of a moving object within the geographic area based on the filtered CSI representation.

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: Systems and methods for generating a platform-agnostic data pipeline via a low code transformation layer are disclosed. The system receives one or more user selections of (i) nodes and (ii) links linking the nodes, indicating a data pipeline architecture of transfer/management/flow of data via a GUI. In response to receiving a user selection to implement the data pipeline, the system automatically identifies/generates a set of code portions, based on one or more software objects (e.g., JSON objects) associated with the user selections indicating the data pipeline architecture. The system then identifies a platform identifier associated with a remote server and generates a set of executable instructions (e.g., a script, executable program, or other file) associated with the data pipeline architecture by using a transformation component. The system then provides the executable instructions to the remote server to host the data pipeline.

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: A method includes receiving a first signal, wherein the first signal is an audio signal. The method further includes providing the first signal to a first comparison circuit. The method further includes providing the first signal to a second comparison circuit. The method further includes receiving, from the first comparison circuit, a first comparison signal. The method further includes receiving, from the second comparison circuit, a second comparison signal. The method further includes providing a wake-up signal to a processing device based on the first comparison signal and the second comparison signal.

Abstract: Implementations disclosed describe methods and systems to perform the methods of deploying and executing machine learning models on target-specific computational platforms. Optimization techniques include but are not limited to alignment of kernel operations with hardware instructions of a target processing device, reduction of kernel dimensions near boundaries of data, efficient reuse of a small number of memory components during neural network operations, run-time quantization of data and neural network parameters, and other methods.

Abstract: Systems and methods for reducing data retrieval times when accessing siloed data across disparate locations by generating a unified metadata graph via a Retrieval-Augmented Generation (RAG) framework are disclosed. The system selects a first Large Language Model (LLM) prompt corresponding to a first metadata identifier of the set of metadata identifiers. The system then augments the first LLM prompt with the first metadata identifier to be provided to an LLM, where the LLM is configured to generate a first intermediate output. The system then augments the first LLM prompt with the second set of metadata identifiers corresponding to the first metadata identifier to be provided to the LLM, where the LLM generates a second intermediate output. The system then generates a domain-specific unified metadata graph. Responsive to determining that first performance metrics fail to satisfy a performance criteria, the system performs an update process on the domain-specific unified metadata graph.

Abstract: A method for use in a computing system, comprising: receiving a first input identifying a current product configuration, the current product configuration being specified by a user; generating a signature for the current product configuration; executing, based on the signature, a first machine learning model to obtain one or more past product configurations that have been offered to one or more customers by one or more colleagues of the user; and outputting an indication of the one or more past product configurations.

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: Systems and methods for facilitating updates to data pipelines using modularly-generated platform-agnostic data pipeline portions are disclosed. The system receives a user selection of a portion of a data pipeline comprising (i) a set of nodes each indicating a data pipeline component and (ii) a set of links linking the set of nodes. The system then generates a modular-portion of the data pipeline architecture, via a transformation component, based on the user selection and stores the modular-portion of the data pipeline architecture in a remote database. The system then receives an update to at least one node of the set of nodes of the modular-portion of the data pipeline architecture. The system then updates at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes.

Abstract: Systems and methods for generating a platform-agnostic data pipeline via a low code transformation layer are disclosed. The system receives one or more user selections of (i) nodes and (ii) links linking the nodes, indicating a data pipeline architecture of transfer/management/flow of data via a GUI. In response to receiving a user selection to implement the data pipeline, the system automatically identifies/generates a set of code portions, based on one or more software objects (e.g., JSON objects) associated with the user selections indicating the data pipeline architecture. The system then identifies a platform identifier associated with a remote server and generates a set of executable instructions (e.g., a script, executable program, or other file) associated with the data pipeline architecture by using a transformation component. The system then provides the executable instructions to the remote server to host the data pipeline.

Abstract: Systems and methods for generating a platform-agnostic data pipeline via a low code transformation layer are disclosed. The system receives one or more user selections of (i) nodes and (ii) links linking the nodes, indicating a data pipeline architecture of transfer/management/flow of data via a GUI. In response to receiving a user selection to implement the data pipeline, the system automatically identifies/generates a set of code portions, based on one or more software objects (e.g., JSON objects) associated with the user selections indicating the data pipeline architecture. The system then identifies a platform identifier associated with a remote server and generates a set of executable instructions (e.g., a script, executable program, or other file) associated with the data pipeline architecture by using a transformation component. The system then provides the executable instructions to the remote server to host the data pipeline.

Abstract: Implementations disclosed describe methods and systems to perform the methods of deploying and executing machine learning models on target-specific computational platforms. Optimization techniques include but are not limited to alignment of kernel operations with hardware instructions of a target processing device, reduction of kernel dimensions near boundaries of data, efficient reuse of a small number of memory components during neural network operations, run-time quantization of data and neural network parameters, and other methods.

Abstract: Systems, apparatuses and methods provide technology that identifies a plurality of data samples of a data set. In response to a trigger, the technology identifies a first subset of samples of the plurality of data samples that are outside a range associated with a decision boundary of a machine learning model, and removes the first subset of samples from the data set to generate a modified training set. The technology train the machine learning model based on the modified training set.

Abstract: Systems, methods, and devices enhance data throughput in wireless devices. Methods may include determining, using a processing device comprising processing elements, a plurality of wireless parameters representing wireless data features on a wireless communications channel, and determining, using the processing device, a plurality of interference parameters based, at least in part, on the plurality of wireless parameters, the plurality of interference parameters identifying interference events on the wireless communications channel. The methods may also include generating one or more data transmission pattern modifications based, at least in part, on the plurality of interference parameters.