AI Year in Review: Highlights of Papers and Predictions from...


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.

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December 13, 2018

IBM’s AI Experiments Hub Puts Cutting-Edge Technology in Your Hands


IBM Research launches new AI Experiments hub featuring prototypes of tools and resources that will unleash the power of AI.

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December 10, 2018

8-Bit Precision for Training Deep Learning Systems


IBM scientists show, for the first time, successful training of deep neural networks using 8-bit floating point numbers while fully maintaining accuracy.

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December 3, 2018

Interpretability and Performance: Can the Same Model Achieve Both?


Bridging the gap between interpretability and performance by transferring information from a high-performing model to a simpler, interpretable model.

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December 3, 2018

Dual 8-Bit Breakthroughs Bring AI to the Edge


IBM researchers showcase new 8-bit breakthroughs in hardware that will take AI further than it’s been before: right to the edge.

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December 3, 2018

Delta-Encoder: Synthesizing a Full Set of Samples From One Image


Delta-encoder enables AI to classify an image from a new category with only a limited number of examples from that category.

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November 27, 2018

IBM Research AI at 2018 Conference on Neural Information Processing...


IBM Research AI shares new ideas and research results at the Thirty-Second Conference on Neural Information Processing Systems in Montreal.

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November 20, 2018

Probabilistic Programming with Pyro in WML


How to write a probabilistic model using Pyro, a deep probabilistic programming language built on PyTorch, and run it via IBM Watson Machine Learning.

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November 7, 2018

Probabilistic Programming with Edward in WML


IBM scientists explain how to run Edward code, a deep probabilistic programming language, on the IBM Watson Machine Learning (WML) platform.

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November 2, 2018

Graph2Seq: A Generalized Seq2Seq Model for Graph Inputs


IBM scientists demonstrate the advantages of the Graph2Seq model for semantic parsing and natural language generation at EMNLP 2018.

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November 1, 2018

Word Mover’s Embedding: Universal Text Embedding from Word2Vec


Word Mover’s Embedding is an unsupervised framework for learning universal text embeddings that can be used for downstream tasks.

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November 1, 2018