Machine Learning Roundup (October 3, 2016)
Machine learning (ML) and deep learning (DL) content from Friday and the weekend. Scroll to the bottom for job postings.
ICYMI, the roundup is now daily and focuses solely on machine learning and deep learning.
Top stories from last week:
— Google releases Cloud Machine Learning to public beta.
— NVIDIA introduces System-on-Chip for Autonomous Vehicles.
— Amazon, Facebook, Google, IBM, and Microsoft form partnership to
restrain trade and stifle innovation promote ethical AI.
solves world hunger announces a data movement tool.
October 21: at UC Berkeley, Sergey Levine delivers a Colloquium on Deep Robotic Learning.
October 26: in Toronto, the MLX Fintech Conference.
Yahoo! Releases Porn Filter
AWS Announces Stupidly Powerful GPU-Accelerated Instances
The editors if Inside Big Data go out on a limb and predict that the volume of data will grow. That alone doesn’t get them into this roundup, but they’re also bullish on ML, so OK.
Abinash Tripathy thinks AI will not take over the world. How do we know he’s not a bot?
In a paper available at arXiv.org, Andy Zeng et. al. describe a system for robotic warehouse automation, which uses a convolutional neural network to segment and label views of a scene.
In TechCrunch, daco.io’s Claire Bretton explains how deep learning enables computers to see.
Methods and Techniques
In WildML, Google Brain’s Denny Britz offers tips on learning Reinforcement Learning.
Mikio Braun dissects how Zalando, a European fashion retailer, puts machine learning to work.
Rick Fulton, Engineering Lead at Postmates, explains how to predict delivery times.
On NVIDIA’s Deep Learning blog, Brian Caulfield describes MANTIS, a six-legged robot on display last week at the GPU Technology Conference (GTC) in Amsterdam last week. MANTIS uses NVIDIA’s Jetson TX1 system for embedded deep learning and computer vision.
In Forbes, Gil Press describes how Cox Automotive uses Splunk for machine learning.
In a published paper, Tao Zheng et. al. describe an approach that uses machine learning to identify Type 2 diabetes through electronic health records.
Bob Tedeschi reports on how recent developments in machine learning affect the practice of radiology.
The aptly named Mariella Moon reports on space drones that use machine learning.
Ananya Bhattacharya describes how researchers use game theory and machine learning to predict where elephant poachers will strike. Presumably, they strike where the elephants are.
In Forbes, Gil Press describes Baidu’s DeepBench, open-source software designed to evaluate the performance of deep learning operations on different hardware platforms.
Apache MADLib (Incubating), an open-source SQL-based machine learning the library, delivers release 1.9.1, with pivots, sessionization and prediction quality metrics. On the Pivotal blog, Frank McQuillan explains.
Demandbase announces the availability of DemandGraph, an AI-powered business graph for B2B marketing.
Noel Bambrick discusses key trends driving AI and ML in the enterprise.
Leslie D’Monte interviews Google executive John Giannandrea, who discusses the role of machine learning in improving Google Search.
In MedCity News, Stephanie Baum surveys new ML-driven startups in Health Care.
In Dignomica, Derek du Preez describes InsideSales, a startup that uses ML to improve sales effectiveness.
Tony Quested reports on several ML startups located in Cambridge (UK), including Prowler.io, ThisWayGlobal, Luminance, Invenia Technical Computing, and Sophia Genetics.
In Dusseldorf, Germany, Trivago seeks an algorithm engineer.
The Voleon Group, a fintech company in Berkeley, CA, seeks a senior researcher for its ML group.