Roundup 10/20/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours. At ML/DL, it’s nothing but machine learning and deep learning 24/7.

Microsoft AI is on a Roll

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

— Last week, MSFT won first place in the COCO Image Segmentation Challenge.


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

— InsideBigData reports from the O’Reilly AI Conference, noting serious discussion about the drawbacks of deep learning and some attractive alternatives.

Methods and Techniques

— In Dataconomy, Kavitha Mariappan describes the ideal platform for ML.

— Adit Deshpande provides a two-part introduction to convolutional neural networks. Part One. Part Two.

ActiveClean, a collaborative between Columbia University’s WuLab and Berkeley’s AMPLab, offers an open source tool that cleans data for machine learning applications.

— Lior Shkiller describes how to capture semantic meanings in text with DL.

— Grant Ingersoll interviews the authors of Deep Learning: A Practitioner’s Approach, who explain the topic.

— In a podcast, Ben Lorica interviews Natalino Busa, who describes developments in feature engineering and predictive analytics.

Interesting Novelties

— Folks at Northwestern develop an algorithm that analyzes your Twitter feed and predicts whether you will vote for Trump or Clinton. (Gary Johnson and Jill Stein could not be reached for comment.) Of course, you already know how you’re going to vote.

— NYU researchers use ML to identify Yik Yak users.


— Nick Heath explains Microsoft’s bet on FPGAs in its Azure data centers.

— Cirrascale announces the future availability of new GPU-accelerated servers.

Health and Medicine

— Researchers at Colorado State use machine learning to identify invasive osteosarcoma cells.


— Stripe uses machine learning to detect and prevent fraud in its new Radar service.

— Glassdoor launches a new tool that uses machine learning to estimate a user’s fair salary. I suspect that complaints about errors will be asymmetric.

— LinkedIn enhances the endorsement feature with ML to make it more relevant.

— Chris Baker, Managing Director of SAP Concur, explains why AI will soon target expense reports. Consider yourself warned.

— John Dix interviews Suresh Acharya, head of JDA Labs, who discusses self-learning supply chains.

— RTB House, a marketing tech company that specializes in retargeting, announces a new conversion model built with deep learning.

— Dean Tang argues that AI will unlock IoT. Hopefully, something will.

— Martech startup CallRail releases Conversation Intelligence, an ML-driven service that automatically qualifies call leads.


— Citi Ventures invests in Feedzai, a startup that uses ML to fight fraud.

— Ravelin, another startup that fights fraud, lands a tiny “A” round.

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