Roundup 10/25/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours. Plus, some AI stuff.

In TechCrunch, John Mannes asks: WTF is machine learning? 1,342 words later, he still doesn’t know.

Methods and Techniques

— Researchers from Google Brain, Penn State, and OpenAI describe a method for semisupervised knowledge transfer from a “teaching” model to a “student” model. The “student” model never sees the original data, and cannot leak it accidentally. Dave Gershgorn reports.

— On the MapR blog, Carol McDonald explains how to use Spark’s logistic regression learner to predict cancer malignancy. She uses the Wisconsin Diagnostic Breast Cancer data set to build her tutorial.

Issues

— In the New York Times, John Markoff describes the criminal potential of AI. So, if you’re a crook, read this article.

Health and Medicine

— Lisa Cornish reports on six technologies that will disrupt health care in developing markets. Guess what’s on top of the list.

— AliveCor and Mayo Clinic announce that they will collaborate on a mobile electrocardiogram program designed to detect hidden health signals. In Recode, Kara Swisher reports.

Trends

— In Forbes, Peter High explains Gartner’s picks for the top ten technology trends for 2017, of which AI and machine learning rank #1.

— Knowlton Thomas summarizes how Google, Apple, Microsoft, Amazon, Salesforce, IBM and Intel use AI. He misses a lot.

— Tod Newcombe reports on four developing technologies, including AI, that could help solve modern problems. That seems a wee bit exaggerated.

Applications

— Folks at the MIT Media Lab develop a Nightmare Machine that uses AI to scare us. Gizmodo, BoingBoing, and The Atlantic all report.

— In Digiday, Ross Benes explains why newsrooms are expanding their data teams. For starters, they need people to report on stories like MIT’s Nightmare Machine.

— Ian Lopez describes four technologies (including ML) that are disrupting legal tech.

— Matthew Finnegan explains why machine learning will play a key role in data center operations. For example, Google uses DeepMind to manage power consumption at its server farms.

— MIT Technology Review reports on Microsoft’s accomplishment matching humans in conversational speech recognition, and why that matters.

— Jack Vaughan describes how Reltio Cloud uses graph analytics for master data management.

— Researchers at University College London develop an algorithm that predicts the outcome of human rights trials with 79% accuracy.  While that may sound impressive, bear in mind that if 79% of all trials result in a guilty verdict, an algorithm that declares all defendants to be guilty will be correct 79% of the time. We judge predictive models by how much they improve accuracy over alternative methods and not their absolute accuracy.

Software

— Nozomi Networks announces the latest release of SCADAguardian, it’s ML-driven cyber security software.

Companies

— Paxata raises $33.5 million, plans to enhance the machine learning and semantic analysis capabilities of its data integration platform.

— NetSpeed Systems lands a $10 million “C” round to fund enhancements to its ML-driven System-on-a-Chip (SoC) design technology.

— In VentureBeat, Jordan Novet reports that NVIDIA sees government as its next goldmine. I don’t see why not; everyone else does.

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