AI Year in Review: Highlights of Papers from IBM Research in 2019

January 17, 2020 Written by: John R. Smith IBM Research has a long history as a leader in the field of Artificial Intelligence (AI). IBM's pioneering work in AI dates back to the field's inception in the 1950s, when IBM developed one of the first instances of machine learning, which was applied to the game of checkers. Since then, IBM has been responsible for achieving major milestones in AI, ranging from Deep Blue – the first chess-playing computer to defeat a reigning world champion, to Watson – the first natural language question and answering system able to win at Jeopardy!, to last year's Project Debater – the first AI system that can build persuasive arguments on its own and effectively engage in debates on complex topics. IBM's leadership in AI continued in earnest in 2019, which was notable for a growing focus on critical topics such as making trustworthy AI work in practice, creating new AI engineering paradigms to scale AI for a broader use, and continuing to advance core AI capabilities in language, speech, vision, knowledge & reasoning, human-centered AI, and more. While recent years have seen incredible progress in "narrow AI," built on technologies like deep learning, IBM Research pushed its AI research in 2019 towards developing a new foundational underpinning of AI for enterprise applications by addressing important problems like learning more from less, enabling trusted AI by ensuring the fairness, explainability, adversarial robustness, and transparency of AI systems, and integrating learning and reasoning as a way to understand more in order to do more.