AI Year in Review: Highlights of Papers and Predictions from IBM Research AI [IBM]


For more than seventy years, IBM Research has been inventing, exploring, and imagining the future. We have been pioneering the field of artificial intelligence (AI) since its inception. We were there when the field was launched at the famous 1956 Dartmouth workshop. Just three years later, an IBMer and early computer pioneer, Arthur Samuel, coined the term machine learning. And ever since, our gaze has always been fixed on what’s next for the field, and how we’ll get there.

Today we released a 2018 retrospective that provides a sneak-peek into the future of AI. We have curated a collection of one hundred IBM Research AI papers we have published this year, authored by talented researchers and scientists from our twelve global Labs. These scientific advancements are core to our mission to invent the next set of fundamental AI technologies that will take us from today’s “narrow” AI to a new era of “broad” AI, where the potential of the technology can be unlocked across AI developers, enterprise adopters and end-users. Broad AI will be characterized by the ability to learn and reason more broadly across tasks, to integrate information from multiple modalities and domains, all while being more explainable, secure, fair, auditable and scalable.

Here, we highlight some of this year’s work in three key areas – advancing, scaling and trusting AI – and, as we focus on the future, a few predictions around what’s to come.

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