We have developed an AI-driven assistive smartphone app dubbed LineChaser, presented at CHI 2021, that navigates a blind or visually impaired person to the end of a line. It also continuously reports the distance and direction to the last person in the line, so that the blind user can follow them easily.
To tackle bias in AI, our IBM Research team in collaboration with the University of Michigan has developed practical procedures and tools to help machine learning and AI achieve Individual Fairness. The key idea of Individual Fairness is to treat similar individuals well, similarly, to achieve fairness for everyone.
This year's Qiskit Global Summer School, from July 12-23, will feature two weeks of live lectures and hands-on laboratory sessions where students can apply what they've learned using Qiskit code using the new Qiskit machine learning application module.
Our study "Comparison of methods to reduce bias from clinical prediction models of postpartum depression” examines healthcare data and machine learning models routinely used in both research and application to address bias in healthcare AI.
At the 2021 virtual edition of the ACM International Conference on Intelligent User Interfaces (IUI), researchers at IBM will present five full papers, two workshop papers, and two demos.
Using novel deep learning architectures, we have developed an AI that could help organizations, enterprises, and data scientists to easily extract data from vast collections of documents. Our technology allows users to quickly customize high-quality extraction models. It transforms the documents, making it possible to use the text they contain for other downstream processes such as building a knowledge graph out of the extracted content.
Unveiled at the two-year anniversary of the IBM Research AI Hardware Center, AI Hardware Composer for analog AI hardware enables one to master and accelerate the AI hardware technology to power more sustainable AI models. It’s one of many upcoming developments of the AI Hardware Center, launched in 2019 to innovate across materials, devices, architecture and algorithms.
In our paper “Extraction of organic chemistry grammar from unsupervised learning of chemical reactions,” published in the peer-reviewed journal Science Advances, we extract the "grammar" of organic chemistry's "language" from a large number of organic chemistry reactions. For that, we used RXNMapper, a cutting-edge, open-source atom-mapping tool we developed.
IBM's quantum systems powered 46 non-IBM presentations in order to help discover new algorithms, simulate condensed matter and many-body systems, explore the frontiers of quantum mechanics and particle physics, and push the field of quantum information science forward overall.
Founded in March 2020 just as the pandemic’s wave was starting to wash over the world, the Consortium has brought together 43 members with supercomputing resources. Private and public enterprises, academia, government and technology companies, many of whom are typically rivals. “It is simply unprecedented,” said Dario Gil, Senior Vice President and Director of IBM Research, one of the founding organizations. “The outcomes we’ve achieved, the lessons we’ve learned, and the next steps we have to pursue are all the result of the collective efforts of these Consortium’s community.” The next step? Creating the National Strategic Computing Reserve to help the world be better prepared for future global emergencies.