Amazon Technologies, Inc.

Sindhu Chejerla

Siddharth Vivek Joshi

Stefano Stefani

Warren Vincent Barkley

Fedor Zhdanov

Anuj Gupta

Prateek Sharma

Kriti Bharti

Jon I Turow

Jonathan Thomas Greenlee

Rahul Sharma

Pietro Perona

Jonathan Hedley

Mark Chien

James Andrew Trenton Lipscomb

Pranav Sachdeva

Sankalp Srivastava

A request is received associated with a review. Within first content, a first field of interest and a second field of interest are identified and within second content, a third field of interest and a fourth field of interest are identified. A review is generated that includes a first indication of the first field of interest and a second indication of the second field of interest within the first content, as well as a third indication of the third field of interest and a fourth indication of the fourth field of interest within the second content. The review is transmitted to a device of a reviewer for reviewing the content.

Providing content reviews based on AI/ML output

A request is received associated with reviewing content. As part of the request, one or more conditions are received and the content is analyzed to identify a first field of interest and a second field of interest. The first field of interest and the second field of interest represent fields of interest associated with the review of the content. At least one of the first field of interest or the second field of interest may not satisfy the one or more conditions and the content, or a portion thereof, may be sent for review.

Determining content to present for human review

Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The process may continue iteratively until the model converges. This enables a dataset to be labeled without requiring each item in the dataset to be individually and manually labeled by human labelers.

Active learning loop-based data labeling service

Growing community of inventors

Seattle, WA, United States of America

Sindhu Chejerla