A new AI model, developed by IBM Research and Pfizer, has used short, non-invasive and standardized speech tests to help predict the eventual onset of Alzheimer’s disease within healthy people with an accuracy of 0.7 and an AUC of 0.74 (area under the curve).
AI’s unprecedented demand for data, power and system resources poses the greatest challenge to realizing this optimistic vision of the future. To meet that demand, we’re developing a new class of inherently energy-efficient AI hardware accelerators that will increase compute power by orders of magnitude, in hybrid cloud environments, without the demand for increased energy.
IBM Research’s Project Debater team worked with the producers of “That’s Debatable” to integrate AI into the traditional debate format. With the help of a new natural language processing (NLP) feature called key point analysis, IBM Watson AI can synthesize thousands of submissions from the general public prior to each episode via ibm.com/debatable.
This year’s IBM "5 in 5" predictions focus on accelerating the discovery of new materials to enable a more sustainable future. In line with the United Nation’s global call-to-action through its Sustainable Development Goals, IBM researchers are working to speed up the discovery of new materials that will address significant worldwide problems.
Today, we are announcing the roadmap that we think will take us from the noisy, small-scale devices of today to the million-plus qubit devices of the future. We are currently developing a suite of increasingly larger and better chips, with a 1,000-qubit-plus chip, called IBM Quantum Condor, targeted for the end of 2023.
I believe one of the most promising areas for AI to make an impact is in the field of medical imaging. Through advancements in AI that allow for more intelligent and accurate analysis of video and still images, there is hope that clinicians will soon be able to widely augment the data and information they […]
State-of-the-Art Results in Conversational Telephony Speech Recognition with a Single-Headed Attention-Based Sequence-to-Sequence Model
Powerful neural networks have enabled the use of “end-to-end” speech recognition models that directly map a sequence of acoustic features to a sequence of words. It is generally believed that direct sequence-to-sequence speech recognition models are competitive with traditional hybrid models only when a large amount of training data is used. However, in our recent […]
The 21st INTERSPEECH Conference will take place as a fully virtual conference from October 25 to October 29. INTERSPEECH is the world’s largest conference devoted to speech processing and applications, and is the premiere conference of the International Speech Communication Association. The current focus of speech technology research at IBM Research AI is around Spoken […]
Today we make a great move on AI research in Brazil by announcing that IBM, University of São Paulo (USP) and FAPESP (São Paulo State Research Foundation) are inaugurating the Center for Artificial Intelligence (C4AI) in Brazil, dedicated to developing cutting-edge studies and research on AI to address topics of great social and economic impact. […]
IBM Quantum will sponsor 5,000 students to attend an eight-month intensive quantum computing course from The Coding School (and you could be one of them).
In Collaboration with the National Institutes of Health, IBM Research Dives Deep into Biomarkers of Schizophrenia
In collaboration with researchers from Harvard Medical School, Mt. Sinai School of Medicine, Stanford University and the Northern California Institute for Research and Education, IBM Research is undertaking a new research initiative funded by the National Institute of Health.
One year ago, IBM Research published the first major release of the Adversarial Robustness Toolbox (ART) v1.0, an open-source Python library for machine learning (ML) security. ART v1.0 marked a milestone in AI Security by extending unified support of adversarial ML beyond deep learning towards conventional ML models and towards a large variety of data types […]
Scientists at Mitsubishi Chemical, a member of the IBM Q Hub at Keio University in Japan, reached out to our team about experimenting with new approaches to error mitigation and novel quantum algorithms to address these very challenges. In the new arXiv preprint, “Applications of Quantum Computing for Investigations of Electronic Transitions in Phenylsulfonyl-carbazole TADF Emitters,” we – along with collaborators at Keio University and JSR - describe quantum computations of the “excited states,” or high energy states, of industrial chemical compounds that could potentially be used in the fabrication of efficient organic light emitting diode (OLED) devices.
A recent panel discussion on “The Promise of Quantum for Industry” at the annual IBM Quantum Summit homed in on several business challenges that quantum computers are well-suited to tackle.