Roundup 10/27/2016

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

I’m publishing a three-part series on the state of enterprise machine learning in The Next Platform. Part one is here.

The latest Kaggle newsletter is here, with lots of interesting content.

Good Reads

— Andrew Colyer summarizes a paper on the use of graph analytics to detect system intrusions at an early stage (before the hackers can do any harm.)

Issues

— Ben Dickson fears the “dark side” of machine learning.

— Stephen Gardner summarizes data privacy challenges posed by AI.

Marketing and Ad Tech

— Adgorithms, an ML-driven ad tech startup, uses AI to drive sales for Harley-Davidson.

— Qubit launches Qubit ML, an ML-driven digital experience management platform.

Applications

— Forbes asks: what product breakthroughs will recent advances in deep learning enable? Google Brain’s Eric Jang answers:

  • Customized data compression
  • Compressive sensing
  • Data-driven sensor calibration
  • Offline AI
  • Human-computer interaction
  • Gaming
  • Artistic assistants
  • Unstructured data mining
  • Voice synthesis

— Circadence, a startup that provides cyber security training, announces plans to use the NVIDIA DGX-1 system in Project Ares, a cyber security training platform.

— Reuters reports on the role of ML and AI in financial market surveillance.

— Adi Gaskell describes the work of two startups, Cogito and Canary Speech, who use speech recognition to assist medical professionals working with dementia patients.

— In CFO, Keith Button summarizes six examples of firms using ML for prediction. Also in CFO, Viral Chawda describes five ways to implement advanced analytics.

— GM plans to use IBM Watson to profile you and sell you stuff in your car.

Software

— Dave Ramel describes Microsoft’s Cognitive Toolkit and IBM Watson Data Platform.

Hardware

— Penguin announces the availability of two new Open Compute servers with NVIDIA GPU accelerators for deep learning.

Companies

— CaliberMind raises $1.1 million of seed capital. The company uses machine learning and language analysis to build psychographic profiles of buyers for B2B Sales and Marketing.

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