IEDM 2020: Advances in memory, analog AI and interconnects point to the future of hybrid cloud and AI
At this year’s IEEE International Electron Devices Meeting, IBM researchers will describe a number of breakthroughs aimed at advancing key hardware infrastructure components, including: Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM), analog AI hardware, and advanced interconnect scaling designed to meet those hardware infrastructure demands.
Today, we are announcing a challenge for the computer vision community to develop robust models for object recognition, demonstrating accurate predictions on ObjectNet images.
Deep learning may have revolutionized AI – boosting progress in computer vision and natural language processing and impacting nearly every industry. But even deep learning isn’t immune to hacking.
IBM researchers have created an AI-powered software to help doctors develop personalized treatments for different patients with the exact same diagnosis.
Enter microcontrollers of the future – the simplest, very small computers. They run on batteries for months or years and control the functions of the systems embedded in our home appliances and other electronics.
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.
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.
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 […]
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.