Roundup 11/29/2016

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

Events

— Spark Summit East will meet in Boston February 7-9 2017.

Good Reads

— In The Next Platform, Nicole Hemsoth explains why Microsoft invests in FPGAs for compute-intensive applications like machine learning. Separately, Nicole investigates Intel’s strategy to integrate the deep learning assets it acquired when it bought Nervana earlier this year.

Issues

— Benedict Evans speculates about the impact of cheap and pervasive cameras combined with image recognition technology.

— The Economist claims that economists love fads and machine learning is the latest fashion. The article is self-debunking. The chart below shows gradual assimilation over thirty years, not Pavlovian fashion-following.

20161126_fnc301

Fundamentals

— In Harvard Business Review, Anastassia Fedyk explains how to tell if machine learning can solve your business problem.

— Tyler Lacoma argues, cryptically, that machine learning is more advanced than ever before, but it’s not Judgement Day yet. That’s a relief.

— A Quora reader requests advice for novice machine learning users who feel overwhelmed. Sebastian Raschka responds. My advice: change careers. Successful data scientists tend to feel energized by all of the resources available today.

— Adrian Sampson describes three common statistical mistakes and how to avoid them.

Methods and Techniques

— Here is the complete series of posts on Topic Modeling from the BigML blog. If you don’t know what Topic Modeling is, read the series.

— Bioinformatics maven Shirin Glander asks: can we predict flu deaths with machine learning and R? She proceeds to answer the question by demonstrating multiple ways to do so in a tour de force post, with graphics and code snippets.

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Software/Services

— Serdar Yegualp explains why AWS standardized on MXNet for DL.

— Baidu releases “Long Utterance,” a set of Chinese language APIs for its speech recognition technologies.

— The Editorial Team at Inside Big Data gushes over IBM Watson Machine Learning because it is “built on Apache Spark.”  In fact, IBM Watson Machine Learning is a rebranded managed service for SPSS Modeler.

Hardware

— TechCrunch reports that Japan’s Ministry of Economy, Trade, and Industry plans to spend 20 billion yen to build a supercomputer capable of 130 petaflops. That’s a lot of yen and a lot of flops.

Applications

— NVIDIA Foundation awards $200K to the Translational Genomics Research Institute to perfect its software for the analysis of cancer cell genes.

— Nokia offers embedded machine learning to telcos for mobile customer experience analytics and customer care.

— In Business Insider, Lydia Ramsey describes five impactful innovations in radiology; machine learning drives two of the five.

Companies

— The MIT engineers and Wall Street analysts at Trefis see autonomous vehicles driving demand for NVIDIA’s DRIVE PX-2 deep learning platform. The same team explains NVIDIA’s steady margin growth.

Bottom Story of the Day

— In TechCrunch, Chris Nicholson argues that machine learning can fix America.

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