Highlights from IBM Research AI in 2018 in three key areas - advancing, scaling and trusting AI - and predictions about what's to come in 2019.
IBM scientists demonstrate the advantages of the Graph2Seq model for semantic parsing and natural language generation at EMNLP 2018.
Word Mover’s Embedding is an unsupervised framework for learning universal text embeddings that can be used for downstream tasks.
Scientists prove quantum advantage for certain mathematical problems: shallow quantum circuits are more powerful than their classical counterparts.
IBM researchers propose guidelines for novel analog memory devices to enable fast, energy-efficient and accurate AI hardware accelerators.
The Corpus Conversion Service is an AI-based cloud system that can ingest PDF documents at scale and extract their content using machine learning models.
Scientists publish a new approach to phase change memory using only a single chemical element—antimony—in Nature Materials.
A new technique to fabricate microelectronic devices that allows molecular integration on conventional silicon chips and by standard semiconductor methods.