Roundup 12/05/2016

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

News

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

— Max Kuhn, author of Applied Predictive Modeling and progenitor of the caret package for machine learning moves to RStudio.

— At AWS’ re:Invent Conference, Databricks announces HIPAA compliance for its Apache Spark managed service. Databricks has also achieved AWS Public Partner status.

— Amazon Web Services launches three new services:

— TensorFlow v0.12.0 RC0 is now available, and it runs on Microsoft Windows. Features available on Windows are a subset of the full feature set. For details, read the announcement.

— Health tech startup Health Catalyst launches Healthcare.ai, a suite of open source packages for healthcare machine learning with R and Python APIs. The R package works with any R distribution and RStudio; the Python package works with Anaconda.

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 and explains the supercomputing vision of NVIDIA CEO Jen-Hsun Huang.

Explainers

— In MIT Technology Review, Will Knight describes how a Google eye scanning algorithm can diagnose diabetic retinopathy better than human experts can. On the Google blog, Lily Peng explains. The JAMA paper is here.

— In a podcast, Ben Lorica interviews Mike Franklin, co-director of Berkeley’s recently wrapped-up AMPLab project, who talks about AMPLab’s legacy. That legacy includes Spark, Alluxio, BlinkDB, KeystoneML, and Succinct, among other projects.

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

— In the second installment of a planned series on deep learning research, Adit Deshpande explains reinforcement learning. The first installment covered generative adversarial nets.

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

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

— In MIT Technology ReviewNicholas Diakopoulos and Sorelle Friedler propose a framework to ensure accountability for algorithms. They stress five principles: responsibility, explainability, accuracy, auditability, and fairness.

— On GitHub, Simon Brugman builds a collection of deep learning papers.

— In Data Science Central, William Vorhies asks: has AI gone too far?  The context of his question is a paper that summarizes research into detecting criminality from facial images. In short, the researchers were able to successfully distinguish criminals from non-criminals in a sample of Chinese men aged 18 to 55 solely from facial measurements extracted from pictures. Vorhies notes that the research is rigorous, and, while the paper has evoked a chorus of criticism for its implications, critics have not yet identified a flaw in the methodology.

— Also in Forbes, Aaron Tilley chronicles NVIDIA’s transformation from a maker of gaming chips to a maker of AI chips.

Bottom Story of the Week

— In The Eponymous Pickle, Franz Dill reports on sex as an algorithm. I’m not kidding.

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