Roundup 11/21/2016

ICYMI: Top machine learning (ML) and deep learning (DL) stories from last week.


— On the AWS Compute Blog, Naveen Swamy and Joseph Spisak explain distributed deep learning with the AWS Deep Learning AMI. The AMI supports Caffe, CNTK/Microsoft Cognitive, MXNet, TensorFlow, Theano, and Torch. Product reviewers are underwhelmed, noting the absence of NVIDIA, CUDA, Anaconda or Python3 support.

— Cray unveils deep learning toolkits for the Cray XC and Cray CS-Storm systems. Kits include Caffe, Microsoft Cognitive Toolkit (CNTK), MXNet, NVIDIA DIGITS (Deep Learning GPU Traning System), TensorFlow, and Torch.

— Google launches a dedicated team to drive Google Cloud Machine Learning and announces a slew of enhancements to its services. Infoworld’s Serdar Yegualp is impressed. Linkapalooza here.

— IBM launches the PowerAI software kit to run on OpenPOWER LC servers for DL/AI workloads.

— Inspur announces the D1000 deep learning appliance based on NVIDIA Tesla GPUs, with support for the Caffe-MPI framework.

— In the Wall Street Journal, Don Clark reports that next year Intel will start shipping deep learning chips based on technology it acquired through its purchase of Nervana Systems. Stephanie Condon elaborates.

— Not-for-profit AI research organization OpenAI announces a partnership with Microsoft to advance AI research. Microsoft Azure will serve as OpenAI’s primary cloud platform. Linkapalooza here.

— NVIDIA partners with Microsoft to optimize its GPU development tools for Microsoft Cognitive Toolkit. Press release here.

— Separately, Microsoft and NVIDIA announce a partnership to accelerate AI. Microsoft Azure will serve as the hub. Linkapalooza here.

— Karl Freund reports on NVIDIA’s announcements at the SC16 show.

— BigML announces its fall release and offers a webinar on November 29 to review the new features.

— DataRobot, a machine learning company, partners with data blending leader Alteryx.

— A team at Ohio State University announces the High Performance Deep Learning project, which aims to accelerate DL with HPC.


— In The Wall Street Journal, Burton Malkiel reviews Virtual Competition, a book about the darker side of the algorithmic economy. The book describes the use of algorithms to support price discrimination and collusion; it closes with a discussion of potential approaches to regulation.


— Matthew Mayo explains ensemble learners.

— In O’Reilly Radar, Reza Zadeh explains why deep learning is hard. In a word: optimization.

— Separately, S. Zayd Enam explains why machine learning is hard.

— On Slideshare, Makoto Yui describes Apache Hivemall, a machine learning library for Hive, Pig, and Spark.

— Microsoft’s David Smith explains how to call Microsoft Cognitive Services (CNTK) with R.

— Timothy Prickett Morgan examines NVIDIA’s recent growth.

— Also on The Next Platform, Nicole Hemsoth dissects Cray’s new XC50 supercomputer, built on the NVIDIA Pascal GPU-accelerated chip.

— Moor Insights & Strategy releases two white papers pertinent to machine learning. The first details AMD’s Radeon Open Compute Platform (ROCm), which supports GPUs in HPC and DL. The second explains Xilinx’ reconfigurable acceleration stack, an FPGA-based approach for machine learning workloads.

— In Forbes, Moor’s Karl Freund says AMD’s ROCm could be a game-changer.

— NVIDIA touts the energy efficiency of its DGX SATURNV supercomputer.

— Paul Alcorn offers additional detail on Intel’s new Xeon CPU and Deep Learning Inference Accelerator.

— In Fast Company, Christina Farr describes a joint project of General Electric and the University of California – San Francisco to develop a library of machine learning algorithms for diagnostics in primary care, pathology, and radiology.

— Nicole Hemsoth describes the Cancer Distributed Learning Environment (CANDLE) framework, powered by NVIDIA. It’s part of the “Cancer Moonshot” initiative. More stories on this here.

— Scientists at UCLA and the University of Illinois use machine learning to discover and design alpha-helical membrane-active peptides, which may lead to improved cancer therapies.

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