Abstract: Disclosed embodiments may include a system for executing programmable macros based on external device interactions. The system may include one or more processors, and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to execute programmable macros based on external device interactions. The system may receive a device identification of an external device. The system may receive a rule associated with the external device, wherein the rule comprises a triggering event and transfer instructions. The system may receive, from the external device, device data. Responsive to determining, from the device data, that the triggering event has occurred, the system may execute the transfer instructions.

Abstract: Systems described herein may provide responses to chatbot prompts that correspond to both a user's preferences and accepted views of society. A chat recommendation server may receive a prompt from a user device. The chat recommendation server may determine a general Overton window and a user-specific Overton window associated with the prompt. The chat recommendation server may generate a plurality of candidate response using the first machine learning model, input the prompt and the plurality of candidate responses to a second machine learning model, and receive, as output from the second machine learning model, a polarization score for each of the plurality of candidate responses. Based on the polarization scores, a recommended response may be selected which minimizes a distance between the user-specific Overton window and the general Overton window. Accordingly, the recommended response may be displayed on the user device.

Abstract: A method and related system for training a machine learning model using a federated learning structure by selectively sharing condensed data between devices includes operations to obtain datasets from client devices comprising a first client device and a second client device and updating a datasets subset to comprise a first dataset. The method further includes updating the datasets subset to comprise a second dataset based on a result indicating that a feature space distance between the first dataset and the second dataset satisfies a set of criteria and sending, to a third client device, the datasets subset comprising the first dataset and the second dataset. The method further includes obtaining, from the third client device, a set of model parameters that is derived from training based on the datasets subset and updating a server version of a machine learning model based on the set of model parameters.

Abstract: The present inventions is directed to systems and methods for automated stress testing of data models and comprises steps of generating, from an initial training dataset used for training a data model, a first data profile comprising a descriptive summary of the initial training dataset, generating a stress profile from an analysis of the data model and the initial training dataset used for training the data model, and modifying the first data profile by the stress profile to generate an altered data profile. A synthetic dataset may then be generated from the altered data profile. A stress performance output of the data model may then be used to identify weak points in the performance of the data model and improve the model's stress performance response using synthetic data.

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: 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: 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: 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: 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: Systems and methods for triggering token alerts. In some aspects, the system, after determining that the probability that an authentication request from an authentication token is associated with a malicious activity is above a threshold, determines whether a user device associated with the authentication token is within a threshold distance of the authentication token. In response to determining that the authentication token is not within the threshold distance of the user device, the system declines the authentication request and transmits an alert request to the authentication token to emit an audio signal from a speaker included in the authentication token.

Abstract: Disclosed embodiments may include a system for executing programmable macros based on external device interactions. The system may include one or more processors, and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to execute programmable macros based on external device interactions. The system may receive a device identification of an external device. The system may receive a rule associated with the external device, wherein the rule comprises a triggering event and transfer instructions. The system may receive, from the external device, device data. Responsive to determining, from the device data, that the triggering event has occurred, the system may execute the transfer instructions.

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: 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.