Machine Learning Roundup (September 30, 2016)

Machine learning (ML) and deep learning (DL) content from the past 24 hours.

Public Beta for Google Cloud Machine Learning

Google releases Cloud Machine Learning services from private beta to public beta, announces additional customer support plans and a certification program. In VentureBeat, Jordan Novet reports on Google’s brand launch for Google Cloud. Stephanie Condon reports that Google seeks to “democratize” machine learning. Sergar Yegualp writes the story for InfoWorld. Linkapalooza here.

Note to readers: “Linkapalooza” means there are so many *&^% stories on this topic I can’t link them individually. Follow the link and browse them all.

NVIDIA Introduces System-on-Chip for Autonomous Cars

Michael Feldman reports from NVIDIA’s first European GPU Technology Conference in Amsterdam. Highlights: new users of NVIDIA’s DGX-1 “supercomputer in a box”, plus a look at the company’s next-generation Volta GPUs designed to power autonomous cars. Linkapalooza here.

PAIBPS (“Partnership on Artificial Intelligence to Benefit People and Society”)

Amazon, Google, Facebook, IBM and Microsoft form an alliance, just as some feared. The Register’s Kat Hall doesn’t think it sounds at all sinister, notes the absence of Apple and Elon Musk’s OpenAI. Linkapalooza here.

IBM Launches DataWorks, Applauds Self

With its customary hype and hoopla, IBM launches DataWorks, a loose collection of managed services in IBM Cloud with a pretty front end. Since some of those services are Watson APIs, IBM claims some kind of ML breakthrough. Before you get too excited, remember this: IBM acquires and markets software for advanced analytics; never in its history has it ever developed successful software for advanced analytics. If you disagree with that statement, name one example in the comments.


Pedro Domingos describes ten myths about machine learning.


Mark Melin thinks there is an AI bubble, and it’s about to burst.  Of course there’s a bubble. The history of finance is the history of bubbles.


Randy Olson, a senior data scientist at the University of Pennsylvania’s Institute for Biomedical Informatics, uses an algorithm called Tree-based Pipeline Optimization Tool (TPOT) to understand variations in genome-wide association studies. In his spare time, he uses ML to plan road trips.


In PCWorld, Agam Shah reports on progress in video recognition technology,

The Johns Hopkins News-Letter interviews Georgetown professor of Neuroscience Maximilian Riesenhuber, who discusses deep learning.


On the Microsoft Health blog, Tom Lawry explains how Microsoft partner KenSci helped Fullerton Health in Singapore identify fraudulent medical claims with ML.

Jonah Comstock describes how weight-loss app Lose It! lets users log foods with their phone’s camera and image recognition technology.

VentureBeat promotes a webcast featuring WildFang, an online retailer that uses ML and AI to improve shopper conversions.

Phillip Tracy describes how a health care provider uses the ThingWorx ML platform to reduce hospital readmissions for patients with ischemic heart disease.

Janet Wagner of ProgrammableWeb describes how Relink, a recruitment technology startup, developed a recommendation engine that matches people to jobs.

DigitalGenius announces a pilot to use AI to support customer service Q&A.

AI beats humans at Doom.


In Data Science Central, Vincent Granville posts 15 deep learning tutorials.

Brian Lee Yung Rowe proposes a simple workflow for deep learning.

Software and Services

Apptus introduces Max, an intelligent agent to manage Quote-to-Cash.


NetSpeed Systems announces the availability of its Gemini 3.0 cache-coherent network-on-chip IP, which is designed to maximize the performance of heterogeneous multicore system-on-chip designs for automotive, mobile and IoT applications. Whew, that’s a mouthful.

In Semiconductor Engineering, Ed Sperling explains progress towards building chips that can learn.


Microsoft launches AI and Research Group. Pedro Hernandez reports.

Techstars has selected the first class of its IOT accelerator, according to a report in TechCrunch.

In Alphr, Ian Betteridge describes NVIDIA’s push to position itself as “the AI computing company.”

NLP Logix launches WiseEye AI, a company that plans to deliver computer vision solutions to the healthcare community. lands $2 million to fund AI for autonomous systems.


There’s a job opening for an actuary in Paris. The posting is in French, so I guess that’s a requirement.

One comment

  • Siegel (TwoSigma) is very worried yes, but about the competition. Algorithms or humans, they can only profit if strategies are not overcrowded. And for a long time pressures have been mounting over the quants in wall street. And I’ll eat my hat if Siegel can show me where in Wall Street can we find the “common sense” that he touts as a sign of intelligence.

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