AAAI

IBM’s AI goes multilingual — with single language training

At AAAI, our team presented two new multilingual research techniques that enable AI to understand different languages while only trained on one.

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IBM researchers check AI bias with counterfactual text

Our team has developed an AI that verifies other AIs’ ‘fairness’ by generating a set of counterfactual text samples and testing machine learning systems without supervision.

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IBM’s AI learns to navigate around a virtual home using common sense

In a recent paper introduced at the 2021 AAAI Conference on Artificial Intelligence (AAAI), we describe an AI that trades off ‘exploration’ of the world with ‘exploitation’ of its action strategy to maximize rewards. In Reinforcement Learning, an AI gets a reward – such as a bag of gold behind a locked door in a video game – every time it reaches specific desirable states. We have greatly improved this exploration vs exploitation tradeoff using additional commonsense knowledge – in the form of crowdsourced text. Our work could lead to better mapping and navigation applications, and to a new generation of interactive assistive agents able to reason like humans.

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IBM AI helps to break down massive code to ease cloud migration

We use AI to automatically break down the overall application by representing application code as graphs. Our AI relies on Graph Representation Learning – a popular method in deep learning. Graphs are a natural representation for software and applications. We translated the application to a graph where the programs become nodes. Their relationships with other programs become edges and determine the boundary to separate the nodes of common business functionality.

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IBM Research AI at AAAI 2019

IBM Research AI will present dozens of technical papers and demos at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI) in Honolulu, Hawaii.

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On-Line Learning of Linear Dynamical Systems with Kalman Filters

A forecasting method that is applicable to arbitrary sequences and comes with a regret bound competing against a class of methods, which includes Kalman filters.

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Deep Learning Training Times Get Significant Reduction

IBM researchers developed a novel compression algorithm that could significantly improve training times for deep learning models in large-scale AI systems.

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Automating Code Generation for Deep Learning Models from Research Papers

In an upcoming presentation at the 2018 AAAI Conference, our team of deep learning experts at IBM Research India propose a new and exploratory technique that automatically ingests and infers deep learning algorithms in published research papers and recreates them in source code for inclusion in libraries for multiple deep learning frameworks (Tensorflow, Keras, Caffe). With […]

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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 […]

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IBM Research AI at the AAAI Conference on Artificial Intelligence

At the 32nd AAAI conference on artificial intelligence, IBM will share significant progress from its AI research team, including technical papers as well as results from the company’s ongoing collaboration with academic institutions through the MIT IBM Watson AI Lab and the AI Horizons Network. Among the featured IBM AI research projects that will be […]

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