AI

The path to the “perfect” analog material and system: IBM at IEDM and NeurIPS

Researchers from the IBM AI Hardware Center will showcase at IEDM and NeurIPS new analog devices, algorithmic and architectural solutions, a novel model training technique, and a full custom design.

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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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Sobolev Independence Criterion

At NeurIPS 2019, IBM Research AI will showcase the “Sobolev Independence Criterion," a novel interpretable dependency measure that provides feature importance scores that can be used for controlling False Discovery Rate.

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IBM Research AI at NeurIPS 2019

IBM researchers from our labs around the world will present more than 100 papers across regular sessions and workshops at NeurIPS. They are all focused on different core technologies and use cases of AI. And a number of them will be on display in booth #111 with demos scientists will be presenting throughout the week.

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Augmenting Humans: IBM’s Project Debater AI gives human debating teams a hand at Cambridge

Two teams, sparring on a controversial topic — whether artificial intelligence would bring more harm than good — the Thursday night debate in front of 300-strong audience seemed rather typical for Cambridge Union, the world’s oldest debating society. Except it wasn’t.

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IBM Joins Stanford Human-Centered AI Institute’s Partner Program

IBM Research is the first founding corporate partner of the Stanford Institute for Human-Centered Artificial Intelligence.

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IBM Research AI focuses on Human-Centered Data Science at CSCW

IBM Research AI's contributions at CSCW 2019 reflect its participation in defining the emerging academic sub-discipline of Human-Centered Data Science (HCDS).

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New Research from the MIT-IBM Watson AI Lab Reveals How Work is Transforming

New empirical work from the MIT-IBM Watson AI Lab uncovers how jobs will transform as AI and new technologies continue to scale across business and industries. We created a novel dataset using machine learning techniques on 170 million U.S. job postings. The dataset and research, The Future of Work: How New Technologies Are Transforming Tasks, allow us to extract key insights into how AI is shaping the future of work.

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IBM Brings AI Retrosynthetic Analysis to the Cloud

IBM researchers are extending IBM RXN for Chemistry, a cloud-based app that takes the idea of relating organic chemistry to a language, by training the model to determine the chemicals needed to create a target molecule.

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High quality, lightweight and adaptable Text-to-Speech (TTS) using LPCNet

Recent advances in deep learning are dramatically improving the development of Text-to-Speech systems through more effective and efficient learning of voice and speaking styles of speakers and more natural generation of high-quality output speech.

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Dark Matter Matters: AI Makes DNA Dark Matter Useful

What is the minimal description that captures a space? Asking a mathematician’s basic question of a  biological dataset reveals interesting answers about biology itself. This summarizes our underlying approach to subtyping hematological cancer. Disease subtyping is a central tenet of precision medicine, and is the challenging task of identifying and classifying patients with similar presentations […]

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Pushing the boundaries of convex optimization

Convex optimization problems, which involve the minimization of a convex function over a convex set, can be approximated in theory to any fixed precision in polynomial time. However, practical algorithms are known only for special cases. An important question is whether it is possible to develop algorithms for a broader subset of convex optimization problems that are efficient in both theory and practice.

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