Roundup 11/17/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours.

Google Enhances Machine Learning Services

Google launches a dedicated team to drive Google Cloud Machine Learning, and announces a slew of enhancements to its services:

— Google Cloud Jobs API provides businesses with capabilities to find, match and recommend jobs to candidates. Currently available in a limited alpha.

— In 2017, GPU-accelerated instances will be available for the Google Compute Engine and Google Cloud Machine Learning. Details here.

Cloud Vision API now runs on Google’s custom Tensor Processing Units; prices reduced by 80%.

Cloud Translation API will be available in two editions, Standard and Premium.

Cloud Natural Language API graduates to general availability.

Infoworld’s Serdar Yegualp is impressed. Linkapalooza here.

Microsoft Partners for AI

— 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.

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

Fundamentals

— In Forbes, investor Jason Black offers a hype-free explanation of machine learning.

— Sam DeBrule provides a non-technical guide to machine learning and artificial intelligence.

— Camron Godbout delivers a simple description of deep learning.

Methods and Techniques

— 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.

— Meanwhile, Matthew Honnibal proposes a four-step approach that makes natural language processing with deep learning easy.

— On the BigML blog, someone who blogs under the username “mariajesusbigml” introduces you to Topic Models.

Software/Services

— Martin Heller reviews Spark ML 2.01. He likes it.

— Separately, Martin reviews Microsoft Cognitive Toolkit (CNTK). He likes that, too. Looking back at Martin’s reviews of TensorFlow, Google Cloud Machine Learning, IBM Watson, and Amazon Machine Learning, I gather that Martin likes everything, except for HPE Haven, of course, but that’s a given. Oddly, Martin thinks Microsoft Azure Machine Learning is harder to use than the rest of this lot, which is nuts.

— 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.

Applications

— 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.

Companies

— In The Motley Fool, Timothy Green argues that NVIDIA is crushing AMD.

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

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