IBM Research Staff

We’ve moved! The IBM Research blog has a new home

In an effort better integrate the IBM Research blog with the IBM Research web experience, we have migrated to a new landing page: https://research.ibm.com/blog

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Pushing the boundaries of human-AI interaction at IUI 2021

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.

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The remarkable work of women scientists and researchers at IBM Research

During the month of March, IBM Research put the spotlight on a number of women scientists and engineers, and asked them about their professional and personal motivations, journeys and experiences as women — and particularly, as women in STEM. They represent the breadth of career experiences at IBM Research, across disciplines, geographies, ethnicities, tenures and backgrounds, who share a passion for science and tech, as well as a commitment to help all women rise to meet their aspirations.

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Celebrating Black talent at IBM Quantum

In recognition of Black History Month, we spoke with a few of our growing cohort of Black IBM Quantum team members working to build the future of quantum computing. The four featured here perform indispensable roles at each level of the quantum stack, from researching quantum algorithms and their potential applications to building a global quantum ecosystem. We hope you'll follow along and celebrate their accomplishments with us today and into the future.

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IBM’s AI goes multilingual — with single language training

At AAAI, our team presented two new multilingual research techniques that enable AI to understand different languages while only trained on one.

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Quantum-Inspired Logical Embedding for Knowledge Representation

In our new paper, to be presented at NeurIPS 2019, we develop a new knowledge representation, which we call “quantum embedding”, that represents conceptual knowledge using a vector space representation that preserves its logical structure and allows reasoning tasks to be solved accurately and efficiently.

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More Is Less: Learning Efficient Video Representations

IBM researchers developed a novel low memory footprint and efficient architecture for spatio-temporal analysis of video. The results show strong performance on several benchmarks – and allow training of deeper models using larger sequences of input frames, which will lead to higher accuracy on video action recognition tasks.

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