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