Abstract: Systems as described herein may include detecting live browser information that a user navigates to a first website displayed in a first open browser tab and a second website displayed in a second open browser tab. A data sharing server may provide the live browser information to a machine learning model as input. Based on feedback from the machine learning model, one or more similar products displayed in the first website and the second website may be determined. The data sharing server may detect an update on the one or more similar products, and cause a user device to display an alert indicating the update on the similar products.

Abstract: Systems and methods for detecting anomalous data updates in federated learning. In some aspects, the system retrieves a plurality of data updates and a corresponding plurality of data profiles during a first interval of time. The system generates a first data profile trend based on the plurality of data profiles, including an expectation value and a measure of variance. The system receives a subsequent data update and a corresponding subsequent data profile. The system determines a measure of deviation based on the expectation value of the first data profile trend and the subsequent data profile. The system generates an anomaly score based on the measure of deviation and the measure of variance. Based on the anomaly score, the system determines whether to label the subsequent data update as acceptable for inclusion in training data to generate a local model.

Abstract: Systems and methods for detecting anomalous data updates in federated learning. In some aspects, the system receives a plurality of data updates. Each data update contains data in a first real-valued space. The system selects a first function to project the plurality of data updates into a second real-valued space. The system selects a second function to partition the second real-valued space into a plurality of sectors. The system generates a plurality of sector datasets associated with the plurality of sectors. The system processes the plurality of sector datasets to generate a relational data structure. The system determines outliers in the relational data structure corresponding to anomalous data updates.

Abstract: Methods and systems for scalable dataset content embedding for improved searchability. For example, the system may retrieve a first dataset from a first data source. The system may generate a first data profile of the first dataset. The system may generate a latent index of the first data profile based on processing the first data profile using a first embedding algorithm. The system may receive, via a user interface, a first request for a first text string. The system may generate an embedded request corresponding to the first request based on processing the first text string using the first embedding algorithm. The system may process the embedded request using the latent index. The system may generate for display, in the user interface, a result based on processing the embedded request using the latent index.

Abstract: Systems and methods for novel uses and/or improvements to data labeling applications, particularly data labeling applications involving sensitive data. As one example, systems and methods are described herein for preventing sensitive data leakage, using weak learner libraries, during label propagation.

Abstract: Systems and methods for deploying machine learning models trained on synthetic data generated based on a predicted future data drift are described herein. In some aspects, the system analyzes data profiles that include snapshots of data from different points in time from the same data stream to predict a future data drift. The system generates synthetic data based on the previous data profiles. The system determines, based on a first and second data profile, that a first data drift exceeds a threshold and updates a machine learning model. The system determines a predicted data drift and generates a synthetic snapshot of the data stream corresponding to a time in the future. The system generates a speculative machine learning model from updating the first updated machine learning model based on the synthetic snapshot. The system deploys the speculative machine learning model to replace the first updated machine learning model.

Abstract: Systems and methods for federated learning including validation of training data on client devices before training models based on the training data. In some aspects, the system accesses local data to be used for training a local model on a client device. The system generates event metadata corresponding to events in the local data. The event metadata is based on validation data received from an external server. The validation data is inaccessible to a central server. The system determines to exclude a portion of the local data that is not validated by the event metadata and generates validated local data. The system trains the local model based on the validated local data. The system processes, using a data de-identification function, the event metadata to generate de-identified event metadata. The system transmits the local model and the de-identified event metadata to the central server.

Abstract: Systems and methods are described herein for increasing the efficiency of generating training data. Specifically, the systems and methods relate to increasing the efficiency and accuracy of data labeling, particularly in instances of dynamically updated datasets of unlabeled data. For example, many applications of artificial intelligence models require real-time processing in order to generate usable results, which itself creates a technical hurdle. To further exacerbate this problem, many of the artificial intelligence models that provide this real-time processing require real-time or near-real-time training as new data is received in order to maintain their accuracy. These processing requirements create fundamental challenges in generating training data in instances of dynamically updated datasets of unlabeled data.

Abstract: The invention relates to executing an action for a cryptographic storage application by comparing a movement dataset, which may contain positional and inertial datasets, with a user profile dataset. The system may receive a first cryptographic storage application address, retrieve a first movement dataset corresponding to a first time period, retrieve a user profile dataset, compare the first movement dataset with the user profile dataset, and execute an action for the first cryptographic storage application.

Abstract: Methods and systems for generating federated learning models. In some aspects, the system receives, from each client device, user data profiles that are anonymized with respect to users associated with user data stored locally at a client device. The system processes the user data profiles to generate a plurality of clusters. For each cluster, the system transmits, to one or more client devices corresponding to a cluster, a first instruction to train a machine learning model on user data corresponding to user data profiles included in the cluster and a second instruction to validate the machine learning model with respect to user data corresponding to one or more clusters of the plurality of clusters other than the cluster to generate a prediction accuracy metric. The system determines, from the plurality of clusters, a first cluster based on associated prediction accuracy metrics.

Abstract: A system and method for preventing erroneous data entry is disclosed. When presented with a form, such as in a website or an app, users are often requested to submit private information that should not be made public. However, form fields are often not protected, and users commonly erroneously enter private information into forms requesting non-private information. Therefore, an alternative entry display is provided to the user to allow the user to separately and securely submit form field entries. This display analyzes the information provided to the user to determine the type of data being requested by a selected form field. This information is used not only to prompt the user in the alternative entry display, but also to verify that the information provided by the user matches the type being requested for security purposes.

Abstract: In some implementations, a device may obtain a first document set associated with a first code repository. The device may generate a first embedding set of one or more embeddings for respective documents included in the first document set. The device may obtain a second embedding set of one or more embeddings for respective documents included in a second document set associated with a second code repository. The device may compare the first embedding set to the second embedding set. The device may generate a code repository similarity score that indicates a similarity between the first code repository and the second code repository. The device may perform, based on the code repository similarity score satisfying a threshold, an action associated with the first code repository and/or the second code repository.

Abstract: Methods and systems are described for novel uses and/or improvements to federated learning. As one example, methods and systems are described for improving the applicability of federated learning across various applications and increasing the efficiency of training a global model through federated learning. As another example, methods and systems are described for ensuring comprehensive training data is available to models assigned by the federated learning server. Additionally, methods and systems are described for improving the rate of training a global model through federated learning.

Abstract: Methods and systems are described herein for novel uses and/or improvements to artificial intelligence applications for data-sparse environments. As one example, methods and systems are described herein for overcoming the problem of determining an appropriate digital asset to recommend to a user in real-time based on training data that may be shared between multiple intents (e.g., as would be found in responses to textual, verbal, and/or other real-time communications). In particular, the methods and systems overcome this technical problem by using an artificial intelligence trained to identify specific verbiage of a user to display digital assets corresponding to a user's intent thereby increasing productivity, organization, and collaboration, and reducing frustration.

Abstract: Systems and methods for a profile-based model selector are described. In some aspects, the system receives an input dataset and a corresponding input data profile and determines a similarity metric for the input data profile with respect to each of a plurality of data profiles. Based on the similarity metric for the input data profile being highest with respect to a first data profile, the system processes the input dataset using a first model associated with the first data profile. Based on determining that performance of the first model when applied to the input dataset is above a threshold, the system verifies a separating hyperplane is placed such that the first data profile and the input data profile are included in a first profile domain and a second data profile is included in a second profile domain.

Abstract: Methods and systems are described for novel uses and/or improvements to federated learning. As one example, methods and systems are described for improving the applicability of federated learning across various applications and increasing the efficiency of training a global model through federated learning. As another example, methods and systems are described for ensuring comprehensive training data is available to models assigned by the federated learning server. Additionally, methods and systems are described for improving the rate of training a global model through federated learning.

Abstract: Methods and systems described herein for validating machine learning models in federated machine learning model environments. More specifically, the methods and systems relate to unloading training and validation techniques to client devices using newly collected data to improve accuracy of federated machine learning models.

Abstract: Methods and systems are described for novel uses and/or improvements to federated learning. As one example, methods and systems are described for improving the applicability of federated learning across various applications and increasing the efficiency of training a global model through federated learning. As another example, methods and systems are described for ensuring comprehensive training data is available to models assigned by the federated learning server. Additionally, methods and systems are described for improving the rate of training a global model through federated learning.

Abstract: Methods and systems are described for novel uses and/or improvements to federated learning. As one example, methods and systems are described for improving the applicability of federated learning across various applications and increasing the efficiency of training a global model through federated learning. As another example, methods and systems are described for ensuring comprehensive training data is available to models assigned by the federated learning server. Additionally, methods and systems are described for improving the rate of training a global model through federated learning.

Abstract: In some implementations, a device may obtain data indicating reparations issued to a user by one or more entities. The device may determine, using at least one machine learning model, an output in connection with the user. The at least one machine learning model may be trained to determine the output based on the data, and the at least one machine learning model may be trained to determine the output with a bias based on a probability, indicated by the data, of the user obtaining reparations for the output being erroneous. The device may transmit, to a user device, information based on the output.