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.

Mining Web API Specifications

Online services like Facebook, Twitter, PayPal, LinkedIn, The Weather Company (an IBM business) and IBM Watson – despite their obvious differences – all share one characteristic: application programming interfaces (API). APIs make data and capabilities available to third-party applications. For application developers, web APIs offer tremendous opportunities to integrate vast amounts of data, like social […]

Continue reading

We’ve Reached the Summit

Introducing the world’s smartest, most powerful supercomputer In 2014, the US Department of Energy (DoE) kicked off a multi-year collaboration between Oak Ridge National Laboratory (ORNL), Argonne National Laboratory (ANL) and Lawrence Livermore National Laboratory (LLNL) called CORAL, the next major phase in the DoE’s scientific computing roadmap and path to exascale computing. They selected […]

Continue reading

Capturing the value of place and time with geospatial-temporal insights

IBM Research is introducing an experimental offering named IBM PAIRS Geoscope (Physical Analytics Integrated Data Repository & Services), a unique cloud-centric geospatial information and analytics service that can accelerate the discovery of new insights. Terms like big data, analytics, data science, and the Internet of Things (IoT) have arisen in recent years to help explain […]

Continue reading

Industry-University Cooperative Research Center Increases Focus on AI

The Center for Advanced Knowledge Enablement (CAKE), started about a decade ago at Florida International University (FIU) and Florida Atlantic University (FAU) under the auspices of the National Science Foundation. Since then, CAKE has added 71 Industry members and completed more than 100 applied industry projects. I serve as the Chairperson of the Industry Advisory […]

Continue reading

End-to-End Open-Domain QA via Multi-Passage Reading Comprehension

Recently, impressive progress has been made in neural network question answering (QA) systems which can analyze a passage to answer a question. These systems work by matching a representation of the question to the text to find the relevant answer phrase. But what if the text is potentially all of Wikipedia?  And what if the […]

Continue reading

IBM Fellow Awarded Levchin Prize for Contributions to Cryptography

We can all thank an elementary school teacher for keeping our electronic data safe and secure. Growing up in Argentina, and studying in Israel, not far from where his career started at IBM’s Haifa Lab, IBM Fellow and cryptographer, Hugo M. Krawczyk cites his third-grade teacher as one of his first influences. “She piqued my […]

Continue reading

Automated knowledge base construction solution wins at ISWC 2017

Automated knowledge base construction is a long-standing challenge in AI. The goal is to abstract concise representations from various sources of knowledge, such as unstructured documents, web data and knowledge bases. The outcome is a knowledge graph that can be used to enhance downstream applications like search engines and business analytics. Highly accurate and extensive […]

Continue reading

Brain-Inspired AI: How Neuroscience Helps to Advance Machine Learning

While building artificial systems does not necessarily require copying nature — after all, airplanes fly without flapping their wings like birds — the history of AI and machine learning convincingly demonstrates that drawing inspirations from neuroscience and psychology can lead to significant breakthroughs, with deep neural networks and reinforcement learning being perhaps the two most […]

Continue reading

Removing the hunch in data science with AI-based automated feature engineering

For data scientists, predictive modeling is the practice of predicting future outcomes using statistical models. Its increasing adoption in the field of AI includes diagnosing cancer, predicting hurricanes and optimizing supply chains, amongst other areas. However, the value of predictive modeling comes at the cost of practicing it. The cornerstone of successful predictive modeling, known […]

Continue reading

Learning to answer non-trivial questions: reasoning over knowledge bases with deep learning

While most of today’s question answering (QA) systems have proven adept at responding to simple questions about specific domains or topics, there’s a growing demand for systems that are able to answer questions across multiple, inexact domains and entities. New research from IBM Research’s AI Foundations team proposes how to overcome this challenge by creating […]

Continue reading

Using distributed learning to boost Watson’s Visual IQ

Quantity matters when training computers to accurately recognize what’s in an image. The more they see, the more they learn. But, training new visual recognition models from a large number of images using deep learning can quickly become a bottleneck, especially for cloud environments that use commodity hardware and GPUs. Commodity machines with an average […]

Continue reading