Our team of researchers from IBM Haifa and Dublin has developed software to help assess privacy risk of AI as well as reduce the amount of personal data in AI training. This software could be of use for fintech, healthcare, insurance, security – or any other industry relying on sensitive data for training.
A new solution for the textile industry use blockchain allows users to track the entire spectrum of fabric manufacturing.
The 45th International Conference on Acoustics, Speech, and Signal Processing is taking place virtually from May 4-8. IBM Research AI is pleased to support the conference as a bronze patron and to share our latest research results, described in nine papers that will be presented at the conference.
To help advance data security in the cloud, IBM Research has initiated and currently leads joint work with the Apache Parquet community to address critical issues in securing confidentiality and integrity of sensitive data.
Recent advances in deep learning are dramatically improving the development of Text-to-Speech systems through more effective and efficient learning of voice and speaking styles of speakers and more natural generation of high-quality output speech.
The latest work on computational argumentation from the IBM Project Debater research team group is being presented at the ACL 2019 conference. Three papers will be presented at the main conference and one more paper will be presented in the co-located Argument Mining Workshop.
Data augmentation is one of the leading methods to tackle the problem of few-shot learning, but current synthesis approaches only address the scenario of a single label per image, when in reality real life images may contain multiple objects. The IBM team came up with a novel technique for synthesizing samples with multiple labels.
Our team of IBM researchers published research in Radiology around a new AI model that can predict the development of malignant breast cancer in patients within the year, at rates comparable to human radiologists.
Deep neural networks have demonstrated good results for few-shot learning. However, very few works have investigated the problem of few-shot object detection. A team of IBM researchers developed a novel approach for Distance Metric Learning (DML).
Medical imaging creates tremendous amounts of data: many emergency room radiologists must examine as many as 200 cases each day, and some medical studies contain up to 3,000 images. Each patient’s image collection can contain 250GB of data, ultimately creating collections across organizations that are petabytes in size. Within IBM Research, we see potential in […]
Our team at IBM Research recently developed a new approach for automated video scene detection. Videos are used today for everything from entertainment and marketing to knowledge-sharing, news, and social journaling. Automated scene detection can help consumers and enterprises utilize this video content in new ways. Video scene detection is the task of temporally dividing […]