International Business Machines Corporation

William Ogallo

Aisha Walcott

Sekou Lionel Remy

Victor Abayomi Akinwande

Celia Cintas

Charles Muchiri Wachira

Girmaw Abebe Tadesse

Skyler Speakman

Samuel Osebe

Fiona Mugure Matu

Julian Bertram Kuehnert

Described are techniques for A/B testing including a computer-implemented method of identifying, in an A/B testing database, a set of feature values with a statistically significant difference in A/B testing outcomes above a threshold. The method further includes partitioning records in the A/B testing database into a plurality of population strata according to the set of feature values. The method further includes performing A/B testing, and identifying heterogeneous outcomes of the A/B testing for respective strata of the plurality of population strata.

Subgroup analysis in A/B testing

A computer system may create a language model corpus including multilingual alignment for training a combined language model and train (or pre-train) the combined language model. The computer system may create an adverse medication reaction corpus to include adverse medication reaction utterances and label an N-gram of an utterance in the adverse medication reaction utterances as a response to query, for multiple N-grams. The computer system may generate a code-mixed utterance model to perform code-mixed utterances in a turn by turn dialogue, by at least adding additional output layer including at least a start vector, language vector, and a query vector including at least the labeled N-gram, which are additional to the combined language model's predicted next words.

Goal-oriented conversation with code-mixed language

A comfortability-tension threshold for a user is determined and a current pose of the user and a determined optimal pose are compared. The current pose and the determined optimal pose are mapped to an angle for adjusting on-screen content of a display of the computer system and the on-screen content of the display of the computer system is adjusted based on the angle.

System and method for training a lazy eye

A translation-engine suggestion method, system, and computer program product include identifying probes for third-party translation-engines for an input text, segmenting sections of the input text into a plurality of segments according to the identified probes, fragmenting the input text into fragments according to the segments, applying each fragment to the identified probe using the corresponding third-party translation-engine, and outputting a translation by combining each fragment.

Translation engine suggestion via targeted probes

A bias detection method, system, and computer program product include creating a context of an applicant based on a profile of the applicant and a context of a reviewer based on a profile of the reviewer, predicting a probability of overlapping data points between the applicant and the reviewer, building enriched embeddings for a deep learning model based on the context of the applicant, the context of the reviewer, the overlapping data points, and text from a review and a final decision by the reviewer, and calculating a bias score via a deep learning model run over the enriched embeddings.

Growing community of inventors

Nairobi, Kenya

William Ogallo