Roundup 11/22/2016

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

Events

— Spark Summit East will meet in Boston February 7-9 2017.

Good Reads

— Adrian Colyer summarizes a report from Stanford University covering key topics in artificial intelligence, including large scale machine learning, deep learning, reinforcement learning, robotics, computer vision, natural language processing, algorithmic game theory, IoT, and neuromorphic computing.

— On the Moor Insights & Strategy blog, highlights from last week’s Supercomputing conference.

Fundamentals

— In MIT Technology Review, Will Knight describes some experiments recently released by Google that demonstrate how neural networks work.

— Zachary Chase Lipton writes a long thumb-sucker about algorithmic bias.

Methods and Techniques

— On the BigML blog, “talvarez” explains how to predict movie review sentiment with topic models. Part four of BigML’s Topic Modeling series is here.

— Don Hillborn explains oil and gas asset optimization with Amazon Kinesis, Amazon RDS, and Databricks.

— Engineers develop a new machine learning algorithm that learns from human instruction and not from data. That strikes me as an oxymoron.

Hardware

— In Fortune, Aaron Pressman argues that Intel’s strategy for machine learning and AI is smart, but lags NVIDIA. Nicole Hemsoth describes Intel’s approach as “war on GPUs.”

— Jelor Gallego reveals that NVIDIA just built the most energy-efficient supercomputer ever.

— Timothy Prickett Morgan dissects the “Summit” supercomputer on order from the U.S. Department of Energy for its Oak Ridge National Laboratory.

Applications

— Sophie Curtis asks: how can machine learning create a smarter energy grid?

— On the WeWork blog, Nicole Phelan describes how WeWork uses machine learning to design offices. A neural network significantly outperforms human designers at predicting utilization.

Companies

— A blogger on Seeking Alpha argues for a lot of upside in NVIDIA shares.

— Serkan Piantino, co-founder of Facebook’s AI research lab, quits to start a new venture, Top 1 Networks, which will provide GPU-accelerated computing as a service.

— Google announces a $4.5 million investment in the Montreal Institute for Learning Algorithms. PR avalanche ensues.

— In Investopedia, Richard Saintvilus chronicles the cloud machine learning wars.

Bottom Story of the Day

— Pinterest uses machine learning to determine what’s trending.

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2 comments

  • In general, keep up the good work. Love the newsletter

    Re: — ‘Don Hillborn explains oil and gas asset optimization with Amazon Kinesis, Amazon RDS, and Databricks.’ Did you read this? This is a classic case where a tiny little bit of business knowledge (the oil and gas industry has a lot of “stuff”) is used to justify a sales pitch viz:

    “Effectively managing these assets requires oil and gas industry to leverage advanced machine learning ”

    Requires? Really? The author really has to back that up. Is the solution proportional to the problem? Or is this just a very trendy ML sledgehammer to crack a dull old nut. Any cost justification, or shall we just dive into some code?

    And at the end of all that code: IS it more cost-effective than traditional techniques such as run-to-fail, or planned maintenance based on bathtub analysis? Where’s the testing?

    And lastly, the example given isn’t even from the oil and gas industry, its a power plant. Duh!

    PS there is a difference between raw materials and assets – a big one. Wouldn’t want to confuse anyone now, would we?

    • Stephen,

      Thanks for reading. The piece struck me as a demonstration of the tools rather than a working application — which is why it’s filed under “Methods and Techniques.” Appreciate the criticism.

      Regards,

      Thomas

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