AI

IBM Research AI: Advancing AI for industry and society

Adversarial Robustness 360 Toolbox v1.0: A Milestone in AI Security

IBM researchers published the first major release of the Adversarial Robustness 360 Toolbox (ART). Initially released in April 2018, ART is an open-source library for adversarial machine learning that provides researchers and developers with state-of-the-art tools to defend and verify AI models against adversarial attacks. ART addresses growing concerns about people’s trust in AI, specifically the security of AI in mission-critical applications.

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Making Sense of Neural Architecture Search

It is no surprise that following the massive success of deep learning technology in solving complicated tasks, there is a growing demand for automated deep learning. Even though deep learning is a highly effective technology, there is a tremendous amount of human effort that goes into designing a deep learning algorithm.

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Making Neural Networks Robust with New Perspectives

IBM researchers have partnered with scientists from MIT, Northeastern University, Boston University and University of Minnesota to publish two papers on novel attacks and defenses for graph neural networks and on a new robust training algorithm called hierarchical random switching at IJCAI 2019.

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Improving the Scalability of AI Planning when Memory is Limited

We report new research results relevant to AI planning in our paper, "Depth-First Memory-Limited AND/OR Search and Unsolvability in Cyclic Search Spaces," presented at the International Joint Conference on Artificial Intelligence, IJCAI-19.

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Causal Knowledge Extraction: An Evaluation using Automated Binary Causal Question Answering

At IJCAI'19, IBM researchers present new results on causal knowledge extraction from large amounts of text for applications in enterprise risk management.

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IBM Research AI @ IJCAI 2019

At IJCAI-19, IBM Research AI will present technical papers exploring a variety of topics in AI including neurosymbolic reasoning, explainability, adversarial robustness, computational linguistics, graph analysis, optimization, and reinforcement learning.

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Introducing AI Explainability 360

IBM Research AI announced AI Explainability 360, a comprehensive open-source toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.

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Answering Complex Questions using Neural Program Induction

At ACL 2019, IBM researchers released a paper detailing the model they trained to answer complex questions using Neural Program Induction, which allows an AI model can be taught to procedurally decompose a complex task into a program.

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Adversarial Learning and Zeroth Order Optimization for Machine Learning and Data Mining

There is a growing number of adversarial attacks and nefarious behaviors aimed at AI systems. To combat this, IBM Research AI will present multiple papers that yield new scientific discoveries and recommendations related to adversarial learning at KDD 2019.

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IBM Research AI at KDD 2019

At KDD 2019, IBM Research AI will present technical papers describing the latest results in deep learning for graphs, adversarial learning, text understanding, and data science for healthcare, financial crimes, and scientific discovery.

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Help build the next generation of AI-driven dialog systems

IBM Research AI and the University of Michigan are organizing a public competition to inspire and evaluate novel approaches that will lead to the next generation of AI-driven dialog systems.

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