Citibank, N.a.

James Myers

Miriam Silver

Daniel Lewandowski

Philip Davies

William Franklin Cameron

Stuart Murray

Biraj Krushna Rath

Payal Jain

Tariq Husayn Maonah

Mariusz Saternus

Chamindra Desilva

Vishal Mysore

Shardul Malviya

Samantha Cory

Deepak Jain

Wayne Liao

Sofia Rahman

Prithvi Narayana Rao

Pramod Goyal

Ramkumar Ayyadurai

Manjit Rajaretnam

Jason Ryan Engelbrecht

John E Ortega, Jr

Alberto Cetoli

Minjeong Cho

Yael Man

Ines Teixeira

Prashant Praveen

Ganesh Prasad Bhat

Ramee S Karthikeyan

Subramanian Sankaran

Wei Jie Ng

Christopher Tucker

Alex Michael Eng

Cameron Paul Lim

Matthew Ryan Mitsui

Julisia Jackson

Ryan Bergeron

The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Remediation of unstructured data using artificial intelligence

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Identifying and remediating gaps in artificial intelligence use cases using a generative artificial intelligence model

Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.

Robust artifacts mapping and authorization systems and methods for operating the same

The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

Detecting data anomalies using artificial intelligence

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

The systems and methods disclosed herein relate to querying data using artificial intelligence models. A generalized model receives an output generation request and partitions it into segments mapped to specific domains, where each domain indicates associated databases and guidelines. The segments are routed to domain-specific models trained on domain-specific data, which generate query fragments by comparing performance metrics and system resource usage metrics. The query fragments are aggregated into an overall query that satisfies guidelines across domains. The systems and methods can include a feedback loop to adjust the domain-specific models using user interactions and performance metrics to dynamically adapt to a skill level or experience of the user.

Querying data using specialized and generalized artificial intelligence models

Described herein are systems and methods for discovering and proactively mitigating previously unknown security vulnerabilities. The systems and methods herein can utilize security vulnerability information to discover potential security threats and can utilize this information to generate an attack using a machine learning model, such as a large language model. Generated attacks can be carried out to assess impact of a security vulnerability. An output can be provided that represents the assessed impact. In some implementations, the systems and methods herein generate patches or other mitigations for security vulnerabilities, which can be tested and deployed to address security vulnerabilities.

Generative cybersecurity exploit discovery and evaluation

Systems and methods are disclosed comprising instructions to access audio data of a captured interaction involving one or more participating users of a digital conference tool, receive a first narrative summary of the captured interaction comprising a set of first component narratives, generate a second narrative summary comprising a set of second component narratives, identify at least one component narrative among the set of second component narratives corresponding to the participating users, display the second component narratives of the second narrative summary at a user interface of the participating users, receive user feedback data comprising an adjustment to the displayed text content of the at least one second component narrative, generate a third narrative summary using the user feedback data received from each participating user of the captured interaction, and display the third narrative summary for the captured interaction at the user interface of each participating user.

Multi-speaker speech signal to text signal validation

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Dynamic multi-model monitoring and validation for artificial intelligence models

The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and/or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).

System and methods for detecting required rule engine updates using artificial intelligence models

Systems and methods for measuring, grading, evaluating, and comparing AI models via a graphical user interface are disclosed. The technology obtains a set of application domains of the AI model in which an AI model will be used. The application domains are mapped to one or more guidelines to determine a set of guidelines that define operational boundaries of the AI model. The guidelines are used to generate assessment domains, each associated with specific benchmarks that include indicators of a degree of satisfaction with the guidelines. For each assessment domain, assessments are constructed to evaluate the AI model's degree of satisfaction with the corresponding guidelines. The AI model is then evaluated against the assessments. Based on these comparisons, grades are assigned to the AI model for each assessment domain. The application-domain-specific grades are generated and displayed at a GUI, reflecting the AI model's degree of satisfaction with the guidelines.

End-to-end measurement, grading and evaluation of pretrained artificial intelligence models via a graphical user interface (GUI) systems and methods

Systems and methods for detecting required rule engine updated using artificial intelligence models

Systems and methods provide a first deployment criterion for deploying modified decision engines. A first existing decision engine is accessed, as well as a first modified decision engine that includes rule data generated by an artificial intelligence model based on the first existing decision engine. A first difference between a first output and a first modified output is determined, where the first output is generated by the first existing decision engine and the first modified output is generated by the first modified decision engine. A first selected decision engine is deployed to process subsequent data items to produce subsequent outputs, based on whether the first difference satisfies first deployment criterion. When metric generated based on the subsequent outputs satisfies a criterion modification condition, the artificial intelligence model is used to generate a second deployment criterion, wherein a second selected rule-based decision engine is deployed based on the second deployment criterion.

Systems and methods for modifying decision engines during software development using variable deployment criteria

Systems and methods for updating rule engines during software development using generated proxy models with predefined model deployment criteria

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New York, NY, United States of America

James Myers