ICYMI: Top ML/DL Stories 10/3-10/7

Top stories of the week, compiled from daily roundups.

Top Reads

How to steal a predictive model.

Nicole Hemsoth describes 26 emerging applications for deep learning across business, scientific and engineering disciplines. Last week, she covered the next wave of deep learning architectures.

Sebastian Raschka publishes Part III of his series on model evaluation, model selection and algorithm selection in machine learning. Parts I and II are here and here.

Gale Morrison’s survey of neural computing in Semiconductor Engineering is an absolute must read. She summarizes intellectual and technical developments in the field, market drivers and briefly discusses what works. Companies mentioned: Amazon, Baidu, Google, Huawei, Intel, Nervana, NVIDIA, and Samsung.

AWS Announces GPU-Accelerated Instances

Amazon Web Services announces the availability of P2 instances designed for computationally intensive science and engineering applications. Details are here. Linkapalooza here.

Denso Invests in Vision Recognition

Global automotive supplier Denso announces an investment in THINCI, which produces low-power vision-recognition technology with embedded DL.

Mitsubishi: We Can Automate Deep Learning 

Mitsubishi Electric announces what it describes as the first Automated Design Deep Learning Algorithm, a system that designs deep learning structures to speed development of AI applications.


Ozzies Deploy Deep Learning Supercomputers

Australia’s Commonwealth Scientific and Industrial Research Organization (CSIRO) deploys two stupidly powerful NVIDIA DGX-1s, each of which has the throughput of 250 servers. First up on the list of projects: sifting through massive quantities of data to understand the impact of environment on disease. In ZDNet, Chris Duckett reports.

NVIDIA, FANUC to Build Smart Robots

NVIDIA and FANUC announce an alliance to embed DL-driven AI in the FANUC Intelligent Edge Link and Drive (FIELD) system for robotics. Adding AI enables robots to teach themselves to perform tasks more efficiently. FANUC will use NVIDIA GPUs and DL software. In MIT Technology Review, Will Knight explains why this is a big deal.

Elsewhere, NVIDIA Eats the News

  • Dave Neal recaps NVIDIA’s Global Technology Conference (GTC) in Amsterdam, featuring the Xavier SoC for autonomous vehicles.
  • On NVIDIA’s Deep Learning blog, Brian Caulfield describes spider-like robot MANTIS, which uses NVIDIA’s Jetson TX1 system for embedded deep learning and computer vision.
  • AI and robotics dominate NVIDIA’s GTC Japan and GTCx Australia.
  • UK-based computer retailer Scan proposes to offer time on NVIDIA’s stupidly powerful DGX-1, thereby demonstrating that GPU-based systems are the new mainframes.
  • Wall Street notices that NVIDIA is on a roll; in Seeking Alpha, Chris Lau explains.
  • Seeking Alpha’s got a fever, and the only prescription is more NVIDIA.
  • Meanwhile, HPC cloud provider Nimbix announces a partnership with NVIDIA and IBM; Nimbix will power its cloud with IBM Power Systems S822LC servers featuring NVIDIA Pascal GPUs.
  • According to reports, AMD’s RTG Radeon Technology will market a dual-GPU chip in 2017. Linkapalooza here.
  • NVIDIA demonstrates an autonomous vehicle driving on unmarked roads at night.

CIA Uses ML/DL to Predict Unrest

In the Fiscal Times, Frank Konkel summarizes how the CIA uses machine learning to predict such things as the flow of illicit cash to extremists. In Defense One, Frank Konkel reports that the CIA says it can predict social unrest 3-5 days in advance. I don’t think this is what folks have in mind when they call for Data Science for Social Good.

Khronos Group Proposes Open Standards for Neural Networks

The Khronos Group, a consortium of hardware and software companies, announces two initiatives to promote the development of neural network techniques. The Neural Network Exchange Format (NNEF) initiative will develop an open standard file format to exchange deep learning models between training and inference systems. The OpenVX Neural Network Extension project will be a high-level architecture specification to run Convolutional Neural Networks (CNN) as OpenVX graphs. Brandon Lewis reports.


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