IBM researchers published the first major release of the Adversarial Robustness 360 Toolbox (ART). Initially released in April 2018, ART is an open-source library for adversarial machine learning that provides researchers and developers with state-of-the-art tools to defend and verify AI models against adversarial attacks. ART addresses growing concerns about people’s trust in AI, specifically the security of AI in mission-critical applications.
Why do targeted cancer therapies often fail? We have acquired so much more understanding about cancer in the last fifty years than in the last five thousand years. Approaches to patient treatments have dramatically changed, and statistics show significant improvement in patient response and outcomes to therapy in the last half a century . Yet […]
In a new paper published in Nature Electronics, IBM researchers demonstrate the smallest ever built DRAM memory cell, fifty years after its invention. The new DRAM cells feature potentially low power consumption and an unprecedented small footprint. They could be therefore particularly appealing for implementation in mobile devices or as cache memory.
The IBM Q team is committed to making our science more approachable by investing heavily in the education to support this growing community and establishing the emerging technology as the next generation of computing. We need more students, educators, developers, and domain experts with “quantum ready” skills. This is why our team is proud to release new educational resources and tools while also increasing the capacity and capability of our IBM Q systems.
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 […]
Today, IBM is launching the new z15 mainframe, the culmination of four years of collaborative development company-wide, with a focus on meeting crucial customer data security and privacy needs across hybrid multicloud environments. To build this ground-breaking new system to meet these client demands, IBM Research partnered with IBM Systems to help develop a new […]
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.
IBM and Princeton launched the program to provide an experience for aspiring scientists and engineers that connects fundamental research to industrial applications at a time in their career development when they still have considerable latitude to choose a focus for their graduate and post-graduate studies.
Ten months ago we assembled a team from IBM Research in Switzerland and IBM tape developers based in Tucson, Arizona, to try to build something which has never been built before to address a risk that may not materialize for another decade or more. As you can tell, we love a good challenge.