Thomas J Watson Research Center

Articles related to people and projects from the Thomas J Watson Research Centers in Yorktown Heights, NY and Cambridge, MA.

IBM Research and the Broad Institute Seek to Unravel the True Risks of Genetic Diseases

In 2019, IBM and the Broad Institute of MIT and Harvard started a multi-year collaborative research program to develop powerful predictive models that can potentially enable clinicians to identify patients at serious risk for cardiovascular disease (1, 2). At the start of our collaboration, we proposed an approach to develop AI-based models that combine and […]

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Impact of the SQL Relational Model 50 years later

Fifty years ago this month, IBM researcher and computing pioneer Edgar Frank Codd published the seminal paper “A Relational Model of Data for Large Shared Data Banks,” which became the foundation of Structured Query Language (SQL), a language originally built to manage structured data with relational properties. Today SQL is one of the world’s most […]

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The Story Behind IBM’s 2019 Patent Leadership

IBM inventors were awarded 9,262 U.S. patents – topping, once again, the list for the most U.S. patents received, for the 27th year running. That brings the total number of IBM’s U.S. patents to over 140,000.

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SysFlow: Scalable System Telemetry for Improved Security Analytics

No organization is safe against cybercrime. Recent studies have shown that these crimes will cost the world well over $5 trillion a year by 2024. Cyber attackers breach corporate networks using a myriad of techniques, with application vulnerabilities corresponding to 25% of all exploitable attack vectors. More disturbing is that these attacks can go unnoticed […]

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Take the Whisky test: How IBM tech can help root out fake Scotch

With the festive season just days away, it’s not just turkey and cranberry sauce flying off the supermarket shelves. It’s also champagne, wine, gin… and, of course, whisky. And it better be genuine. IBM researchers are working on technologies to help you make sure the whisky you buy is indeed what it says on the […]

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IBM Research advances in end-to-end speech recognition at INTERSPEECH 2019

IBM scientists presented three papers at INTERSPEECH 2019 that address the shortcomings of End-to-end automatic approaches for speech recognition - an emerging paradigm in the field of neural network-based speech recognition that offers multiple benefits.

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IBM Research Contributes to z15 Launch with Hybrid Cloud, Security Breakthroughs

Today, IBM is launching the new z15 mainframe, the culmination of four years of collaborative development company-wide, with a focus on meeting crucial customer data security and privacy needs across hybrid multicloud environments. To build this ground-breaking new system to meet these client demands, IBM Research partnered with IBM Systems to help develop a new […]

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Helping to Untangle Cancer Drug Resistance with Data

Why do targeted cancer therapies often fail? We have acquired so much more understanding about cancer in the last fifty years than in the last five thousand years. Approaches to patient treatments have dramatically changed, and statistics show significant improvement in patient response and outcomes to therapy in the last half a century [1]. Yet […]

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Overcoming Challenges in Building Enterprise AI Assistants

A team of researchers from IBM Research AI and AI Horizons Network-partner the University of Michigan published the papers “A Large-Scale Corpus for Conversation Disentanglement” and “Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use” at ACL 2019. This work address two main challenges in building enterprise AI assistants.

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Distributed Software-Defined Networking Control by Deep Reinforcement Learning for 5G and Beyond

IEEE ICC 2019 “Best Paper” details novel deep reinforcement learning approach to maximize overall performance of Software-Defined Networking that supports 5G.

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Text2Scene: Generating Compositional Scenes from Textual Descriptions

At CVPR 2019, IBM researchers introduce techniques to interpret visually descriptive language to generate compositional scene representations from textual descriptions.

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