International Business Machines Corporation

Rakesh Rameshrao Pimplikar

Gyana Ranjan Parija

Sudhanshu Shekhar Singh

Kuntal Dey

Sameep Mehta

Biplav Srivastava

Ramasuri Narayanam

Shrihari Vasudevan

Magesh A Narayanan

Didier Denove

Khalid Siddiqui

Jasmina Mohorn

Vijay Arya

Manish Kesarwani

Manish Kataria

Kushal Mukherjee

Atul S Kumar

Ritwik Chaudhuri

Shweta Garg

Udit Sharma

Rohith Dwarakanath Vallam

Karibik Sankaranarayanan

One embodiment provides a computer implemented method, including: receiving information corresponding to a customer of a seller, wherein the information is related to credit information of the customer; generating a credit attribute for the customer with respect to the seller, wherein the generating includes utilizing a plurality of artificial intelligence agents that each analyze at least a subset of the information to each generate an agent version of the credit attribute; and recommending a deferral of at least a portion of a pending invoice of the seller for the customer, wherein a value of the deferral is based upon the credit attribute.

Invoice deferral recommendation

Methods, systems, and computer program products for a collaborative cognition platform for creating and hosting social machines are provided herein. A computer-implemented method includes creating a social machine for collaborative tasks, wherein the social machine comprises (i) one or more human agents, (ii) one or more machine-based agents, (iii) an algorithm, and (iv) a set of rules prescribed for executing the collaborative tasks. The method also includes generating one or more collaborative resolutions for the collaborative tasks by executing, in an automated fashion, the collaborative tasks via implementation of the algorithm, wherein the algorithm facilitates, in accordance with the set of rules, systematic iterations of collaboration among (i) the one or more human agents and (ii) the one or more machine-based agents. Further, the method includes outputting the one or more collaborative resolutions to at least one user.

Collaborative cognition platform for creating and hosting social machines

One embodiment provides a computer implemented method, including: receiving billing information related to a billing contract of a customer of a seller, wherein the billing contract identifies amounts of invoices and an invoice frequency; identifying, utilizing one or more artificial intelligence agents, one or more risk factors associated with generation of a pending invoice based upon the billing information; and recommending, utilizing the one or more artificial intelligence agents, a generation date for the pending invoice based upon the one or more risk factors, wherein the recommending includes selecting a generation date to facilitate timely payment of the pending invoice by the customer.

Invoice generation recommendation

A computer-implemented method is disclosed which includes receiving an initial decision from each of a plurality of agents based on the same criteria, wherein at least a subset of the agents are computing nodes, and wherein the criteria pertain to a resource allocation request; assigning an equal weight to resources specific to each of the agents; generating, for each of the initial decisions, a revised decision based on (i) comparing the initial decisions to each other, (ii) modeling aggregate group behavior represented by the initial decisions of the plurality of agents, and (iii) modeling a behavior of each of the agents in view of the initial decisions; updating the weights assigned to the resources specific to each of the agents based at least in part on the revised decisions; and dynamically allocating resources in response to the resource allocation request based at least in part on the updated weights.

Multi-agent system for efficient decentralized information aggregation by modeling other agents' behavior

One embodiment provides a method, including: assigning a machine learning model signature to a machine learning model, wherein the machine learning model signature is generated using (i) data points and (ii) corresponding data labels from training data; receiving input comprising identification of a target machine learning model; acquiring a target signature for the target machine learning model by generating a signature for the target machine learning model using (i) data points from the assigned machine learning model signature and (ii) labels assigned to those data points by the target machine learning model; determining a stolen score by comparing the target signature to the machine learning model signature and identifying the number of data labels that match between the target signature and the machine learning model signature; and classifying the target machine learning model as stolen based upon the stolen score reaching a predetermined threshold.

Stolen machine learning model identification

A computer-implemented dynamic road stretch dividing method, the method comprising: determining a current lane distribution of partitions of a road stretch; calculating a new lane distribution of the road stretch to ameliorate traffic based on a pragmatic factor; changing an alignment of the partitions of the current lane distribution to obtain the new lane distribution; and updating the pragmatic factor based on at least one of an external policy and a constraint input by a user.

Dynamic road width division for adaptive road space utilization

One embodiment provides a method for delaying malicious attacks on machine learning models that a trained using input captured from a plurality of users, including: deploying a model, said model designed to be used with an application, for responding to requests received from users, wherein the model comprises a machine learning model that has been previously trained using a data set; receiving input from one or more users; determining, using a malicious input detection technique, if the received input comprises malicious input; if the received input comprises malicious input, removing the malicious input from the input to be used to retrain the model; retraining the model using received input that is determined to not be malicious input; and providing, using the retrained model, a response to a received user query, the retrained model delaying the effect of malicious input on provided responses by removing malicious input from retraining input.

Detecting and delaying effect of machine learning model attacks

A dynamic road stretch dividing method, system, and computer program product, include determining a current lane distribution of partitions of a road stretch, calculating a new lane distribution of the road stretch to ameliorate traffic based on a pragmatic factor, and changing an alignment of the partitions of the current lane distribution to obtain the new lane distribution.

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New Delhi, India

Rakesh Rameshrao Pimplikar