Accenture Global Solutions Limited

Accenture Global Services Gmbh

Teresa Sheausan Tung

Colin Anil Puri

Qian Zhu

Owen Edward Richter

Karthik Rajagopal Gomadam

Zhijie Wang

Qing Xie

Vipul Savjani

Jaeyoung Christopher Kang

Srinivas Yelisetty

Jean-Luc Chatelain

Matthew Bernard Kujawinski

Kishore S Swaminathan

Jagaran Das

Mohamad Mehdi Nasr-Azadani

Joseph F Tobolski

Desmond R Duggan

Tiffany Cecilia Dharma

Zaid Tashman

Karthik Gomadam

Jurgen Albert Weichenberger

Ishmeet Singh Grewal

Carl Matthew Dukatz

Reymonrod Geli Vasquez

Sonali Parthasarathy

Andrew Nam

Anuraag Chintalapally

William Richard Gatehouse

Jeffrey Catania

Louis Gerald Farfan

Wenjia Sun

Aaron Pang

Neda Abolhassani

David Baurle Treat

Kunal Verma

John Velissarios

Dana Le

Kirby James Linvill

Narendra Anand

Kunal Taneja

Shaw-Yi Chaw

Malek Ben Salem

Andrew John Truscott

Yao A Yang

Jan Andre Nicholls

Kevin P Lee

Chia Jung Chang

Fabio Bucci

Jaeyoung C Kang

Benjamin Grimmer

Nikki Ann G Guevarra

Daniel Corin

Fausto A Inestroza

Luiz Pizzato

Deborah Garand

Atieh Ranjbar Kermany

Michael Giba

Michael Thomas Giba

Mohamad Mehdi Nasr-azadani

Steven Robert Roberts

Arjun Sitaraman Krishnan

Jürgen Albert Weichenberger

Sara L Renberg

Sameer Farooqui

Farzana Badruddoja

Rajul Agarwal

Nicholas Akiona

Maziyar Baran Pouyan

Allan Enemark

Milad Bourhani

Jacob O Wenegrat

Mijung Kim

Michael Nai-An Chen

Nigel Robert Markey

Trinidy Ray Combs

Nitin Bajaj

Matin Momeni

Ruiwen Li

Aritomo Shinozaki

Jonathan Tipper

Mark Joseph Portelli

Mohamed Aftkhar

Venkata Narasimhan Koduvayur Raghuram

Bepeta Mallikarjun

Thien Quang Nguyen

Rachael Victoria Phillips

Stephen Zoegall

Amirreza Abdolrashidi

Scott C Kelly

Yao Yang

Elizabeth Ann Shay

Jennifer Mohrig

Unnati Amin

Paul Hofmann

Ophelia Min Zhu

Nirmalya Chakraborty

Analiz Cabrera Hernandez

Brandon Winful

Mallikarjun Bepeta

Daniel A Corin

A causal inference stack implements a targeted maximum likelihood scheme to conduct causal analysis of the observational data. At a data-handling layer, the causal inference stack obtains one or more memory locations for a dataset and establishes analysis nodes to setup localized data handling for the memory locations. At a data classification layer, the causal inference stack characterizes the missingness of the dataset. At a pipeline layer, the causal inference stack obtains a data element dependency query from a user and sets up an end-to-end solution path to determine the presence of a causal relationship between data elements identified in the data element dependency query.

Complex system for end-to-end causal inference

A resource prediction system for executing machine learning models and method are provided. The system includes non-transitory memory storing instructions and a processor configured to execute the instructions to obtain input data including a targeted objective and the constraints, select a deployable machine learning model having an evaluation score that meets a predetermined criterion from among candidate machine learning models, virtually execute the deployable machine learning model on each of candidate hardware platforms according to the constraints, generate an assessment report of the virtual performance metrics set of the deployable machine learning model executed on each of the candidate hardware platforms, and select the suggested hardware platform meeting the predetermined criterion from among the candidate hardware platforms.

Resource prediction system for executing machine learning models

This disclosure is directed to a generalizable machine learning model production environment and system with a defense mechanism that facilitates safe execution of machine learning models in production by effectively detecting potential known and new adversarial attacks. The disclosed exemplary systems and architectures gather data from the online execution of the machine learning models and communicate with an on-demand pipelines for further inspection and/or correction of vulnerabilities in the production machine learning model to the detected attacks. These systems and architectures provide an automatable process for continuous monitoring of model performance and correction of the production machine learning model to guard against current and future adversarial attacks.

Machine learning model robustness against adversarial attacks in production

In some implementations, an application programming interfaces (API) manager may receive, at a set of artificial intelligence (AI) APIs, a set of inputs from a set of on-site devices. Accordingly, the API manager may route the set of inputs to a corresponding set of remote servers and may receive, from at least one server of the corresponding set of remote servers, at least one response based on at least one input, from the set of inputs, routed to the at least one server. The API manager may transmit the at least one response to a corresponding device from the set of on-site devices. Further, the API manager may modify at least one API, of the set of AI APIs, based on a traffic pattern associated with the set of inputs and the at least one response.

On-site artificial intelligence application programming interfaces for routing and adapting traffic

A model retraining tool is provided for utilizing a knowledge graph to retrain analytical models used in production. The model retraining tool retrains the analytical models to improve performance of the analytical models in an efficient and resource conserving manner.

Automated analytical model retraining with a knowledge graph

Knowledge graph systems are disclosed for implementing multiple approaches, including stand alone or combined approaches, for knowledge graph pruning. The approaches are based on graph sampling work such as, for example, information gain theory. The approaches are applied by a knowledge graph system to perform schema pruning, automatic graph pruning, and query correlation for improving query performance.

Approaches for knowledge graph pruning based on sampling and information gain theory

A lifecycle platform for creation, ingestion, version control, and contextual query of knowledge graph is disclosed. Such a platform may be used to create and deploy a knowledge graph by reusing and merging knowledge defined in existing and validated data models. The platform tracks changes made to the knowledge graph after being deployed and provides version tracking of the knowledge graph and its underlying namespaces. The platform further provides a subscribable service for contextual viewing and query of portions and/or subset versions of the knowledge graph. Such a platform may be provided as an agnostic plugin to a specific vendor knowledge graph solution space.

Agnostic creation, version control, and contextual query of knowledge graph

A system provides feedback driven end-to-end state control of a data model. A data model may be used to model the behavior of a petrochemical refinery to predict future events. The system may be used to ensure proper operation of the data model. Contingency data models may be executed when a failure is detected. Further, when the system detects accuracy that is out of tolerance, the system may initiate retraining of the data model being currently used.

Feedback loop driven end-to-end state control of complex data-analytic systems

The present disclosure relates to a system and a method for model control platform stack. The method includes, at an input layer of a model control platform stack, receiving input data. At a governance layer of the model control platform stack, the method includes maintaining a probe and model inventories; selecting a model, a monitoring location point, and a probe; and deploying, based on the selections of the probe and the model, a container to an orchestration layer of the model control platform stack. At the orchestration layer of the model control platform stack, the method includes accessing the container; using the container to deploy the probe and the model; scheduling an execution of the model to determine inference associated with the input data; during the execution, extracting probe data, using the probe, from the monitoring location point; and adjusting, based on the probe data and the inference, the model.

Model control platform

A system maintains a knowledge layout to support the building of event response recommendations. Meta-graph patterns may be used to determine semantic relatedness between events and actions in response. Event-action node pairs are then constructed.

Complex system for meta-graph facilitated event-action pairing

A system maintains a knowledge layout to support the building of event and analytics models in parity. The system uses the event models to provide a snapshot of the relevant conditions present when a challenge event occurs. The system uses the analytics models to select one or more actions (which may include robotic tasks) to respond to the challenge condition. In some cases, the system may render continued response compulsory until a successful response to the challenge event is achieved.

Complex system for knowledge layout facilitated analytics-based action selection

A device may receive content data identifying content created by users and metadata associated with the content. The device may receive rules data identifying rules associated with utilization of the content. The device may utilize the metadata to generate digital DNA signatures for the content in near-real time. The device may store, in a repository, the rules data, the content, the digital DNA signatures, and relationships between the digital DNA signatures. The device may receive, from a client device, new content that is generated based on particular content of the content data and new metadata associated with the new content. The device may utilize the new metadata to generate a new digital DNA signature for the new content. The device may process the new digital DNA signature, the rules data, and the digital DNA signatures to determine whether the new content violates one or more rules of the rules data.

Utilizing machine learning to prevent intellectual property issues for content during different stages of a project lifecycle

A systems implements a gradient descent calculation, regression calculation, or other machine learning calculation on a dataset (e.g., a global dataset) using a coordination node including coordination circuitry that coordinates multiple worker nodes to create a distributed calculation architecture. In some cases, the worker nodes each hold a portion of the dataset and operate on their respective portion. In some cases, the gradient descent calculation, regression calculation, or other machine learning calculation is used to implement a targeted maximum likelihood scheme for causal inference estimation. The targeted maximum likelihood scheme may be used to conduct causal analysis of the observational data.

Coordinated multiple worker node causal inference framework

The present disclosure relates to a system, a method, and a product for optimizing hyper-parameters for generation and execution of a machine-learning model under constraints. The system includes a memory storing instructions and a processor in communication with the memory. When executed by the processor, the instructions cause the processor to obtain input data and an initial hyper-parameter set; for an iteration, to build a machine learning model based on the hyper-parameter set, evaluate the machine learning model based on the target data to obtain a performance metrics set, and determine whether the performance metrics set satisfies the stopping criteria set. If yes, the instructions cause the processor to perform an exploitation process to obtain an optimal hyper-parameter set, and exit the iteration; if no, perform an exploration process to obtain a next hyper-parameter set, and perform a next iteration with using the next hyper-parameter set as the hyper-parameter set.

Resource-aware automatic machine learning system

Growing community of inventors

San Jose, CA, United States of America

Teresa Sheausan Tung