Researchers from the IBM AI Hardware Center will showcase at IEDM and NeurIPS new analog devices, algorithmic and architectural solutions, a novel model training technique, and a full custom design.
In our new paper, to be presented at NeurIPS 2019, we develop a new knowledge representation, which we call “quantum embedding”, that represents conceptual knowledge using a vector space representation that preserves its logical structure and allows reasoning tasks to be solved accurately and efficiently.
IBM researchers from our labs around the world will present more than 100 papers across regular sessions and workshops at NeurIPS. They are all focused on different core technologies and use cases of AI. And a number of them will be on display in booth #111 with demos scientists will be presenting throughout the week.
Two teams, sparring on a controversial topic — whether artificial intelligence would bring more harm than good — the Thursday night debate in front of 300-strong audience seemed rather typical for Cambridge Union, the world’s oldest debating society. Except it wasn’t.
IBM Research is the first founding corporate partner of the Stanford Institute for Human-Centered Artificial Intelligence.
IBM Research AI's contributions at CSCW 2019 reflect its participation in defining the emerging academic sub-discipline of Human-Centered Data Science (HCDS).
New empirical work from the MIT-IBM Watson AI Lab uncovers how jobs will transform as AI and new technologies continue to scale across business and industries. We created a novel dataset using machine learning techniques on 170 million U.S. job postings. The dataset and research, The Future of Work: How New Technologies Are Transforming Tasks, allow us to extract key insights into how AI is shaping the future of work.
IBM researchers are extending IBM RXN for Chemistry, a cloud-based app that takes the idea of relating organic chemistry to a language, by training the model to determine the chemicals needed to create a target molecule.
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.
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 […]
Convex optimization problems, which involve the minimization of a convex function over a convex set, can be approximated in theory to any fixed precision in polynomial time. However, practical algorithms are known only for special cases. An important question is whether it is possible to develop algorithms for a broader subset of convex optimization problems that are efficient in both theory and practice.