International Business Machines Corporation

David John Piorkowski

Evelyn Duesterwald

John Thomas Richards

Julian Timothy Dolby

Harold Leon Ossher

Qingzi Vera Liao

Stephanie Houde

Rachel Katherine Emma Bellamy

Guillaume A Baudart

Sameep Mehta

Robert G Farrell

Ramya Raghavendra

Martin J Hirzel

Kalapriya Kannan

Kush Raj Varshney

Aleksandra Mojsilovic

Darrell Christopher Reimer

Rachel K E Bellamy

Manish Kesarwani

Matthew R Arnold

Kaoutar El Maghraoui

Michael Desmond

Peri L Tarr

Michael James Hind

Yunfeng Zhang

Jason Tsay

Yiyun Chen

Austin Zachary Henley

An autonomous agent operating method, system, and computer program product, including launching a first autonomous agent for a task with an initial update interval and adjusting the initial update interval for a second autonomous agent based on a second task for the second autonomous agent being similar to the task over time in relation to a trust level of a human user in a performance of the first autonomous agent.

Increasing trust formation and reduce oversight costs for autonomous agents

Performance of a computer implementing a machine learning system is improved by providing, via a graphical user interface, to an annotator, unlabeled corpus data to be labeled; obtaining, via the interface, labels for the unlabeled corpus data; and detecting, with a consistency calculation routine, concurrent with the labeling, internal inconsistency and/or external inconsistency in the labeling. Responsive to the detection, intervene in the labeling with a reactive intervention subsystem until the inconsistency is addressed. The labeling is completed subsequent to the intervention; the system is trained to provide a trained machine learning system, based on results of the completed labeling; and classification of new data is carried out with the trained system. Proactive intervention schemes are also provided.

Framework of proactive and/or reactive strategies for improving labeling consistency and efficiency

A method, a computer program product, and a computer system determine when to transfer a communication session from an agent to a bot. The method includes monitoring the communication session between the agent and a user. The method includes determining a continuing utility value indicating a predicted continuing cost to maintaining the communication session with the agent. The continuing utility value is indicative of a predicted continuing benefit to maintaining the communication with the agent. The method includes determining a transferring utility value indicating a predicted transferring cost to transferring the communication session from the agent to the bot. The transferring utility value is indicative of a predicted transferring benefit to transferring the communication session from the agent to the bot. The method includes, as a result of the predicted transferring benefit being greater than the predicted continuing benefit, transferring the communication session from the agent to the bot.

Agent to bot transfer

A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Generation and management of an artificial intelligence (AI) model documentation throughout its life cycle

Aspects of the present invention disclose a method, computer program product, and system for detecting and mitigating adversarial virtual interactions. The method includes one or more processors initiating a mitigation protocol on interactions between the user and the virtual agent, wherein the mitigation protocol is based on the actions performed by the user while interacting with the virtual agent. The method further includes one or more processors, in response to initiating the mitigation protocol on interactions between the user and the virtual agent, generating a lower fidelity response from the virtual agent to the user, wherein the lower fidelity response is a progressive dilution of the precision of language of an original response from the virtual agent to the user prior to the user exceeding the risk level threshold.

Cognitive virtual detector

A computing method receives a labeled sample from an annotator. The method may determine a plurality of reference model risk scores for the first labeled sample, where each reference model risk score corresponds to an amount of risk associated with adding the first labeled sample to a respective reference model of a plurality of reference models. The method may determine an overall risk score for the first labeled sample based on the plurality of reference model risk scores. The method may further determine a probe for confirmation of the first labeled sample and a trust score for the annotator by sending the probe to one or more annotators. In response to determining a trust score for the annotator the method may add the labeled sample to a ground truth or reject the labeled sample.

Filter for harmful training samples in active learning systems

Systems and methods to generate a cognitive model are described. A particular example of a system includes a memory including program code having an application programming interface and a user interface, and a processor configured to access the memory and to execute the program code to generate a cognitive model, to run analysis on the cognitive model to determine a factor that is impacting a performance of the cognitive model, to determine an action based on the factor, to report at least one of the factor and the action to a user, and to use the action to generate a second cognitive model.

Conversational optimization of cognitive models

Aspects of the present invention disclose a method, computer program product, and system for detecting and mitigating adversarial virtual interactions. The method includes one or more processors detecting a user communication that is interacting with a virtual agent. The method further includes one or more processors determining a risk level associated with the detected user communication based on one or more actions performed by the detected user while interacting with the virtual agent. The method further includes one or more processors in response to determining that the determined risk level associated with the detected user communication exceeds a risk level threshold, initiating, a mitigation protocol on interactions between the detected user and the virtual agent, where the mitigation protocol is based on the actions performed by the detected user while interacting with the virtual agent.

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White Plains, NY, United States of America

David John Piorkowski