AI

Characterizing the Evolution of Tasks Within Occupations

The team at the IBM-MIT Watson AI Lab presented a new study at the AAAI Conference on AI, Ethics, and Society on “Learning Occupational Task-Shares Dynamics for the Future of Work” that shows how to predict changes in the economy’s demand for different tasks.

AI

Deriving Complex Insights from Event-driven Continuous Time Bayesian Networks

Real-world decision making often involves situations and systems whose uncertain and inter-dependent variables interact in a complex and dynamic way. Additionally, many scenarios are influenced by external events that affect how system variables evolve. To address these complex scenarios for decision making, together with colleagues at the IBM T. J. Watson Research Center, we have developed a new dynamic, probabilistic graphical model called - Event-driven Continuous Time Bayesian Networks.

AI

IBM Showcases Key AI Research @ AAAI-20

IBM Research will present more than fifty technical papers at AAAI-20, as well a rich set of demos of our latest work, reflecting our focus on key areas of AI research including AutoAI, mastering language, planning, computational argumentation, the future of work and security.

Characterizing the Evolution of Tasks Within Occupations

The team at the IBM-MIT Watson AI Lab presented a new study at the AAAI Conference on AI, Ethics, and Society on “Learning Occupational Task-Shares Dynamics for the Future of Work” that shows how to predict changes in the economy’s demand for different tasks.

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Deriving Complex Insights from Event-driven Continuous Time Bayesian Networks

Real-world decision making often involves situations and systems whose uncertain and inter-dependent variables interact in a complex and dynamic way. Additionally, many scenarios are influenced by external events that affect how system variables evolve. To address these complex scenarios for decision making, together with colleagues at the IBM T. J. Watson Research Center, we have developed a new dynamic, probabilistic graphical model called - Event-driven Continuous Time Bayesian Networks.

Continue reading

IBM Showcases Key AI Research @ AAAI-20

IBM Research will present more than fifty technical papers at AAAI-20, as well a rich set of demos of our latest work, reflecting our focus on key areas of AI research including AutoAI, mastering language, planning, computational argumentation, the future of work and security.

Continue reading

AutoAI set to make it easy to create machine learning algorithms

AutoAI is a novel approach of designing, training and optimizing machine learning models automatically. With AutoAI, anyone could soon build machine learning pipelines from raw data directly, without writing complex code and performing tedious tuning and optimization, to then automate complicated, labor-intensive tasks. Several IBM papers selected for the AAAI-20 conference in New York demonstrate the value of AutoAI and different approaches to it in great detail.

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Quantum Computers Flip the Script on Spin Chemistry

Recent research by IBM and University of Notre Dame serves as a new use case for quantum computing, showing that qubit noise, typically an impediment to quantum computer use, can actually be an advantage over a classical computer for chemical simulations.

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$3.4M DARPA Grant Awarded to IBM to Defend AI Against Adversarial Attacks

Modern AI systems have reached human-level abilities on tasks spanning object recognition in photos, video annotations, speech-to-text conversion and language translation. Many of these breakthrough achievements are based on a technology called Deep Neural Networks (DNNs). DNNs are complex machine learning models with an uncanny similarity to the interconnected neurons in the human brain, giving […]

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IBM Research at SPIE 2020: New Architectures and Fabrications for AI Hardware

IBM Research had 21 papers accepted to SPIE, and throughout the four-day conference IBM researchers will present on topics ranging from EUV lithography, patterning materials, etch, selective deposition, and novel device integration.

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Bringing AI to the Command Line

For decades, developers and researchers have been using the command line interface (CLI) to build, execute, and deploy the software that runs the world around us. Users have come to love, hate and, eventually, embrace the unique, idiosyncratic, and sometimes antiquated challenges associated with using the terminal shell; and have adapted their behaviors and usage […]

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AI Can Predict your Age Based on Your Microbiome

The human microbiome consists of a community of trillions of micro-organisms, such as bacteria, fungi, viruses, and live all over the body including on the skin, in the mouth and along the digestive tract. A balanced microbiome is important for an individual’s health and wellbeing, including proper functionality the digestive and immune systems. The human […]

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IBM Research AI Hardware Center marks first birthday with explosive gains in AI computation

One year ago, we announced the creation of the IBM Research AI Hardware Center, a global research hub headquartered in Albany, New York. Building on work of the last few years, the launch of the Center initiated the next phase in a long-term effort to combine evolving, fundamental advances in AI with new computing accelerators, […]

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Progressing IBM Project Debater at AAAI-20 — and Beyond

At the thirty-fourth AAAI conference on Artificial Intelligence (AAAI-20), we will present two papers on recent advancements in Project Debater on two core tasks, both utilizing BERT.

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Mastering Language Is Key to More Natural Human–AI Interaction

IBM Research AI is leading the push to develop new tools that enable AI to process and understand natural language. Our goal: empower enterprises to deploy and scale sophisticated AI systems that leverage natural language processing (NLP) with greater accuracy and efficiency, while requiring less data and human supervision.

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IBM Automated Planning Research @ AAAI 2020

Research from IBM presented at AAAI-20 explores key aspects of adopting automated planners, including scaling to very large problems, interfacing with end users, and providing guarantees on the quality of solution.

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Dataset Lifecycle Framework: the swiss army knife for data source management in Kubernetes

Hybrid Cloud is rapidly becoming the go-to IT strategy for organizations seeking the perfect mix of scalability, performance and security. As a result, it is now common for an organization to rely on a mix of on-premise and cloud solutions, or “data-sources”, from different providers to store and manage their data. It doesn’t really sound […]

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