Our latest breakthrough in AI training, detailed in a paper presented at this year’s NeurIPS conference, is expected to dramatically cut AI training time and cost. So considerably in fact that it could help completely erase the blurry border between cloud and edge — offering a key technological upgrade for hybrid cloud infrastructures.
As we looked closer at the kinds of jobs our systems execute, we noticed a richer structure of quantum-classical interactions including multiple domains of latency. These domains include real-time computation, where calculations must complete within the coherence time of the qubits, and near-time computation, which tolerates larger latency but which should be more generic. The constraints of these two domains are sufficiently different that they demand distinct solutions.
Building on the foundations of deep learning and symbolic AI, we have developed a software able to answer complex questions with minimal domain-specific training. Initial results are encouraging – the system achieves state-of-the-art accuracy on two datasets with no need for specialized training.
We're excited to announce the IBM Quantum Awards: Open Science Prize, an award totaling $100,000 for any person or team who can devise an open source solution to two important challenges at the forefront of quantum computing based on superconducting qubits: reducing gate errors, and measuring graph state fidelity.
Unfortunately, there are no default scheduler plugins in Kubernetes to consider the actual load in clusters for scheduling. To achieve that goal, we developed a way to optimize resource allocation through load-aware scheduling and submitted our "Trimaran: Real Load Aware Scheduling" Kubernetes enhancement proposal, with the hope of soon merging this feature into the Kubernetes scheduler plugin.
Researchers from our IBM Research labs around the world and from IBM Watson Health have contributed a total of 47 workshops, papers, posters and panels that will be presented at AMIA 2020. These contributions cover a wide range of topics but reflect our overarching goal of driving the usefulness of AI in Healthcare.
Capturing and structuring common knowledge from the real world to make it available to computer systems is one of the foundational principles of IBM Research. The real-world information is often naturally organized as graphs (e.g., world wide web, social networks) where knowledge is represented not only by the data content of each node, but also […]
Our team of researchers recently published paper “Fine-Grained Visual Recognition in Mobile Augmented Reality for Technical Support,” in IEEE ISMAR 2020, which outlines an augmented reality (AR) solution that our colleagues in IBM Technology Support Services use to increase the rate of first-time fixes and reduce the mean time to recovery from a hardware disruption.
The Rensselaer-IBM Artificial Intelligence Research Collaboration advances breakthroughs in more robust and secure AI
Launched in 2018, the Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC) is a multi-year, multi-million dollar joint venture boasting dozens of ongoing projects in 2020-2021 involving more than 80 IBM and RPI researchers working to advance AI.