Google has unveiled GPipe, an open-sourced library that makes training deep neural networks more efficient under the TensorFlow framework Lingvo for sequence modeling. According to Google AI software engineer Yanping Huang, “in GPipe … we demonstrate the use of pipeline parallelism to scale up DNN training,” noting that larger DNN models “lead to better task performance.” Huang and his colleagues published a paper on “Efficient Training of Giant Neural Networks Using Pipeline Parallelism.” Continue reading Google GPipe Library Speeds Deep Neural Network Training

IBM now has a patent-pending, machine learning enabled watermarking process that promises to stop intellectual property theft. IBM manager of cognitive cybersecurity intelligence Marc Ph. Stoecklin described how the process embeds unique identifiers into neural networks to create “nearly imperceptible” watermarks. The process, recently highlighted at the ACM Asia Conference on Computer and Communications Security (ASIACCS) 2018 in Korea, might be productized soon, either within IBM or as a product for its clients. Continue reading IBM Creates Machine-Learning Aided Watermarking Process