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.

Abstract: In some implementations, a device may identify a use of artificial intelligence by an entity to reach a decision in connection with a user. The device may determine, using a machine learning model, that the decision in connection with the user is erroneous. The machine learning model may be trained to determine whether the decision is erroneous based on first information relating to the use of artificial intelligence by the entity and second information relating to one or more historical decisions in connection with the user or one or more other users. The device may provide a notification indicating that the decision in connection with the user is erroneous.

Abstract: In some implementations, a device may obtain data indicating reparations issued by an entity that uses artificial intelligence to provide artificial intelligence outputs in connection with users, the reparations being issued for one or more of the artificial intelligence outputs being erroneous. The device may determine, using a machine learning model, an artificial intelligence reparation characterization for the entity. The artificial intelligence reparation characterization determined using the machine learning model may be indicative of an amount of reparations predicted for the entity in connection with uses of artificial intelligence by the entity. The machine learning model may be trained to determine the artificial intelligence reparation characterization based on the data. The device may transmit information indicating the artificial intelligence reparation characterization.

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: 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: The present disclosure provides systems for encoding transforms based on intent to be useable by developers. The system receives input datasets, generates output datasets, and identifies characteristics of the input or output datasets. Using these characteristics, the system can encode transforms associated with the datasets based on intent. The system stores and/or associates the intent-encoded transforms in data profiles, so that the system enables the transforms to be searched, recommended, and/or combined.

Abstract: Methods and systems described herein relate to the creation of a digital repository of artificial intelligence models that allows users to determine their individual fairness metric. More specifically, the methods and systems provide this digital repository by storing it on a blockchain network and tracking any changes made to the model and/or its fairness metric.

Abstract: A method includes sending a data selection parameter to a client computing device, wherein the client computing device stores a distributed instance of a machine learning model, and the machine learning model includes at least a first subset of parameters and a second subset of parameters. In response to a determination that a transmission criterion is satisfied, the client computing device is caused to report the first subset of parameters based on the data selection parameter. The method further comprises obtaining the parameters of the first subset of parameters of the distributed instance from the client computing device and updating the federated learning model based on the first subset of parameters of the distributed instance from the client computing device.