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 Research launches new AI Experiments hub featuring prototypes of tools and resources that will unleash the power of AI.
IBM scientists show, for the first time, successful training of deep neural networks using 8-bit floating point numbers while fully maintaining accuracy.
Bridging the gap between interpretability and performance by transferring information from a high-performing model to a simpler, interpretable model.
Delta-encoder enables AI to classify an image from a new category with only a limited number of examples from that category.
IBM Research AI shares new ideas and research results at the Thirty-Second Conference on Neural Information Processing Systems in Montreal.
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.