What is the minimal description that captures a space? Asking a mathematician’s basic question of a biological dataset reveals interesting answers about biology itself. This summarizes our underlying approach to subtyping hematological cancer. Disease subtyping is a central tenet of precision medicine, and is the challenging task of identifying and classifying patients with similar presentations […]
It is no surprise that following the massive success of deep learning technology in solving complicated tasks, there is a growing demand for automated deep learning. Even though deep learning is a highly effective technology, there is a tremendous amount of human effort that goes into designing a deep learning algorithm.
At IJCAI'19, IBM researchers present new results on causal knowledge extraction from large amounts of text for applications in enterprise risk management.
At ACL 2019, IBM researchers will present a demonstration of HEIDL, a model that makes it easier and much faster for people to review the effectiveness of natural language labels generated by a deep learning model trained on human-labeled data.
IBM Research AI and IBM Watson worked together to develop a promising approach that achievies state-of-the-art performance on relation extraction. This work is being presented at ACL 2019.
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.
The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) takes place July 28 – August 2nd in Florence, Italy. There, IBM Research AI will present technical papers describing the latest results in our long-term push to help AI systems master language.
IBM announced the fourth, 2019 edition of the Science for Social Good program, designed to address social and humanitarian challenges with data and AI. Since the program launched in 2016, we have executed 28 projects with more than 110 volunteer researchers and 36 student fellows. We've also contributed 47 scientific papers.
At CVPR 2019, IBM researchers introduce techniques to interpret visually descriptive language to generate compositional scene representations from textual descriptions.
At CVPR 2019, IBM researchers introduce an improved method to bridge the semantic gap between visual scenes and language to produce diverse, creative and human-like captions.
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.