Roundup 10/24/2016

Top machine learning (ML) and deep learning (DL) stories from last week, plus new content from Friday and the weekend.

The theme for featured images this week is art produced by deep learning.

ICYMI: Top Stories of Last Week

— AMD, Dell EMC, Google, HPE, IBM, Mellanox, Micron, NVIDIA, and Xilinx launch the OpenCAPI Consortium and industry group to promote specs for the next generation of data center hardware.

opencapi-architecture

— Apple hires Carnegie Mellon University professor Ruslan Salakhutdinov as Director of AI research. Linkapalooza here.

— Andrew Oliver proposes dropping seven technologies from the Big Data ecosystem: MapReduce, Storm, Pig, Java, Tez, Oozie, and Flume. He forgets to mention Mahout, which is forgivable since nobody uses it.

— Daniel Gutierrez interviews Jim McHgh of NVIDIA’s Deep Learning Group, who says he wants to collaborate with Databricks to integrate the BIDMach machine learning library with Spark.

— Meanwhile, Gartner announces the top ten strategic technology trends for 2017, and machine learning is right up there at #1 on the list.

— Serdar Yegualp describes Microsoft’s big bet on FPGAs, explains the potential of FPGAs for machine learning, notes that existing machine learning software generally does not support FPGA acceleration.

— Meanwhile, however, Baidu announces that it will accelerate its machine learning applications with Xilinx FPGAs.

— Xilinx is on a roll. TeraDeep announces a fast deep learning solution that leverages Xilinx FPGAs.

— Using CNTK, MSFT researchers achieve parity with humans in speech recognition; medialanche ensues.

— In HBR, Tom Davenport explains how to introduce AI into your organization. The next generation of AI will introduce itself.

— Tesla announces that its new cars will include all of the hardware needed for level 5 autonomy. The software isn’t available yet but will be added through over-the-air updates.

Good Reads from Last Week

— Christine Barton et. al. explain why companies can’t turn customer insights into growth.

— François Maillet of MLDB.ai explains how to use MLDB for machine learning. MLDB looks like an exciting project.

— Emmanuelle Rieuf reviews Cathy O’Neil’s Weapons of Math Destruction. So does Jo Craven McGinty in the Wall Street Journal.

Microsoft’s Big Bet on FPGAs

— Top analysts chew over Microsoft’s announcement that it uses Field Programmable Gate Arrays (FPGAs) to accelerate servers in its data centers. Karl Freund of Moor Insights and Strategy dissects Microsoft’s approach. In The Next Platform, Stacey Higginbotham delivers a tick-tock covering how MSFT decided to place its bet.

Methods and Techniques

— Alex Handy lists a collection of resources in ML, DL, and AI.

— A community of contributors offers an excellent open guide to Amazon Web Services, including Amazon Machine Learning.

Health and Medical Applications

— Jennifer Bresnick explains the potential impact of Blockchain, IoT and ML on healthcare.

— HealthNextGen, a startup that specializes in ML for health care, announces a partnership with Charité – Universitätsmedizin in Berlin, Europe’s largest university hospital.

— The National Institutes of Health awards a grant of $1.2 million to Xi Luo of Brown University and colleagues at Johns Hopkins and Yale. The grant funds ML-driven research into brain scans and brain function.

Software and Services

— Serdar Yegulalp reports on progress towards a version of TensorFlow that runs on Windows. I wonder if it will force you to upgrade to Windows 10.

— Bernd Bischl et. al. describe mlr, a machine learning framework for R with more than 160 learners and support for parallel high-performance computing.

Companies

— Nielsen adds a machine learning capability to the Nielsen Marketing Cloud.

— Wall Street mulls Tesla’s partnership with NVIDIA.

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