International Business Machines Corporation

Deepak Vijaykeerthy

Diptikalyan Saha

Nishtha Madaan

Pranay Kumar Lohia

Naveen Panwar

Sameep Mehta

Kuntal Dey

Krishnasuri Narayanam

Dakuo Wang

Evelyn Duesterwald

Karthik Sankaranarayanan

Vijay Arya

Manish Kesarwani

Sijia Liu

Horst Cornelius Samulowitz

Samiulla Zakir Hussain Shaikh

Gregory Bramble

Aniya Aggarwal

Parikshit Ram

Vaibhav Saxena

Shashank Mujumdar

Alexander Gray

Suranjana Samanta

Anupama Murthi

Philips George John

Ganesh Venkataraman

Swagatam Haldar

Arunima Chaudhary

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate testing a cascaded pipeline. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an input component, a cascaded pipeline, and an evaluation component. The input component can receive a test case associated with a label from labeled speech data represented by waveform. The evaluation component can feed the test case to the cascaded pipeline to obtain an output of the cascaded pipeline. The evaluation component can evaluate a robustness of the cascaded pipeline by comparing the output of the cascaded pipeline and the label. The cascaded pipeline can include a first model and a second model, and the first model can be different than the second model.

Testing cascaded deep learning pipelines comprising a speech-to-text model and a text intent classifier

The exemplary embodiments disclose a method, a computer program product, and a computer system for determining that one or more model pipelines satisfy one or more constraints. The exemplary embodiments may include detecting a user uploading data, one or more constraints, and one or more model pipelines, collecting the data, the one or more constraints, and the one or more model pipelines, and determining that one or more of the model pipelines satisfies all of the one or more constraints based on applying one or more algorithms to the collected data, constraints, and model pipelines.

Machine learning with multiple constraints

Methods, systems, and computer program products for detecting contextual bias in text are provided herein. A computer-implemented method includes identifying, by a machine learning network, a protected attribute in one or more data samples; processing the identified data samples using a first sub-network of the machine learning network, wherein the first sub-network is configured to determine a plurality of contexts of the protected attribute across the identified data samples; determining an impact of each of the plurality of contexts on a second sub-network of the machine learning network, wherein the second sub-network of the machine learning network is configured to classify a given data sample into one of a plurality of classes; and adjusting the second sub-network of the machine learning to account for the impact of at least one of the plurality of contexts on the second sub-network.

Contextual impact adjustment for machine learning models

One embodiment provides a method, comprising: receiving an input sentence for a classification by a machine-learning model, where the classification is based upon a sentiment of the input sentence; splitting the input sentence into a plurality of tokens, each of the plurality of tokens corresponding to a term within the input sentence; creating a causal subgraph from the plurality of tokens, wherein the creating is based upon a causal relationship identified between tokens of the plurality of tokens; identifying, using the causal subgraph, tokens of the plurality of tokens influencing the classification; and generating, based upon the tokens of the plurality of tokens, a causal explanation for the classification, wherein the causal explanation identifies at least one portion of the input sentence resulting in the classification.

Generation of causal explanations for text models

Methods, systems, and computer program products for automatically modifying responses from generative models using artificial intelligence techniques are provided herein. A computer-implemented method includes obtaining data pertaining to at least one conversation involving at least one automated conversation exchange software program and at least one user; identifying, among words proposed by the at least one automated conversation exchange software program in connection with the at least one conversation, words qualifying as belonging to one or more predetermined categories by processing the obtained data using artificial intelligence techniques; determining, by processing the identified words and at least one word-based data source, one or more alternate words; modifying at least a portion of the proposed words by replacing at least a portion of the identified words with at least a portion of the one or more alternate words; and performing at least one automated action based on the modifying.

Automatically modifying responses from generative models using artificial intelligence techniques

Methods, systems, and computer program products for active explanation guided learning are provided herein. A computer-implemented method includes identifying a subset of training examples, from a set of training examples, based on at least one of (i) an uncertainty metric computed for each one of the training examples and (ii) an influence metric computed for each one of the training examples; outputting said subset of training examples to a user; obtaining, from the user, a user explanation for each training example in said subset of training examples, wherein each of the user explanations identifies at least one part of the corresponding training example; and training a machine learning model based at least in part on the user explanations, wherein said training comprises prioritizing the identified parts of the training examples in the subset.

User explanation guided machine learning

One embodiment provides a method, including: receiving a dataset and a model corresponding to a bias checker, wherein the bias checker detects bias within both the dataset and the model, based upon a bias checking algorithm and a bias checking policy, wherein the dataset comprises a plurality of attributes; testing the bias checking algorithm of the bias checker by (i) generating test cases that modify the dataset by introducing bias therein and (ii) running the bias checker against the modified dataset; testing the bias checking policy of the bias checker by generating a plurality of test cases and running the bias checker against the plurality of test cases; and providing a notification to a user regarding whether the bias checker failed to indicate bias for one or more of the plurality of attributes.

Testing bias checkers

In an embodiment, a method for inspecting and transforming a machine learning model includes receiving a request that includes the machine learning model and a configuration object that provides an indication of a selected strategy. In the embodiment, the method includes creating a partially specified task graph that includes a first placeholder node for a future expanded task node. In the embodiment, the method includes performing a dynamic expansion and execution phase that includes, repeatedly (a) using a cognitive engine to evaluate whether to revise the partially specified task graph based at least in part on the selected strategy, and (b) using a processor-based execution engine to perform an action specified by the complete node. In an embodiment, the dynamic expansion and execution phase repeats until after the cognitive engine adds a consolidated results node.

System for operationalizing high-level machine learning training enhancements from low-level primitives

One embodiment provides a method, including: deploying a machine learning model, wherein the deployed machine learning model is used in responding to queries from users; receiving, at the deployed machine learning model, input from a user; identifying a type of machine learning model attack corresponding to the received input; computing, responsive to receiving the input, a resiliency score of the machine learning model, wherein the resiliency score indicates resistance of the machine learning model against the identified type of attack; and performing an action responsive to the computed resiliency score.

Resiliency of machine learning models

One embodiment provides a method, including: receiving, from an application, information for storage in a storage management system, wherein the storage management system comprises a plurality of storage layers, each storage layer having a different performance and a different cost than the other storage layers; labeling the information with one of a plurality of labels relevant to the application; assigning the information a performance tolerance value based upon the label of the information, wherein the performance tolerance value comprises an estimate of the performance requirement required by the application storing the information; determining a storage layer for storage of the information, wherein the determining comprises identifying one of the plurality of storage layers corresponding to the label of the information and updating metadata of a logical volume corresponding to the information with the performance tolerance value; and sending the information to the determined storage layer.

Growing community of inventors

Chennai, India

Deepak Vijaykeerthy