Roundup 12/7/2016

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

On Thursday, December 8, Databricks’ Joseph Bradley and Jules Damji will deliver a webinar on migrating Spark ML workloads to DataFrames.

Good Reads

— On the Algorithmia blog, Matt Kiser explains why deep learning matters.

— In The Wall Street Journal’s CIO Journal, Sara Castellanos reports on Capital One’s pursuit of explainable machine learning models.

— Separately, WSJ’s Christoper Mims explores the challenges businesses face in delivering value from AI and machine learning. He cites three constraints: insufficient data; a shortage of business problems where AI/ML can make a difference; and the talent shortage. I would note that the data shortage problem isn’t a matter of volume but quality. Many of those petabytes that data warehousing people brag about are worthless.

Fundamentals

— Carlos Perez explains why deep learning is fundamentally different from machine learning. Carlos, co-founder of Intuition Machine, is writing a book called Deep Learning Design Patterns; he blogs regularly here.

— In ZDNet, George Anadiotis discovers that machine learning and predictive analytics go together as if this is news, thereby reinforcing the impression that the folks who publish ZDNet are clueless about both topics.

— Bernard Marr explains the difference between artificial intelligence and machine learning.

— NVIDIA offers a deep learning teaching kit for educators, complete with lecture slides, videos, hands-on labs, coding projects, source code solutions, e-books and GPU resources.

Software/Services

— The folks at Google Cloud Big Data and Machine Learning Blog — “GCBDMLB” for short — list their top ten favorite Google BigQuery user experiences of 2016.

— BigML publishes a webinar covering its Fall 2016 release.

— Google DeepMind releases its training environment to open source on GitHub. Jeremy Kahn reports in Bloomberg.

Hardware

— Kunal Jain explains how to build a machine learning/deep learning workstation for under $5,000.

— Ben Cotton discusses the ins and outs of GPUs, ASICs and FPGAs for machine learning, and profiles Graphcore, a startup with a distinctive approach.

Applications

— In Wired, Davey Alba profiles Amazon Go, a retail concept that uses RFID, sensors, and artificial intelligence to enable checkout-free shopping. You just grab what you want; the order posts to your Amazon account later. Currently, there is one Amazon Go store operating in Seattle, in beta for Amazon employees. Linkapalooza here.

— Aspire Health, a Medicare Advantage provider, develops an algorithm that can predict which patients are likely to die in the next year (based on medical records.) The company uses the algorithm to offer palliative care to patients in lieu of heroic treatment and save itself a bundle of money. They should rename the company Soylent Green and take the program to the next step if you know what I mean, wink, wink.

— Aspectiva’s Rafi Mendelsohn explains how his company uses machine learning to identify fake online reviews.

— In Recode, Eric Johnson interviews AliveCor CEO Vic Gundotra, who opines about the emerging role of machine learning in medicine.

Bottom Story of the Day

— The Facebook audience grows older and crankier, and this may harm the social media giant’s revenue.

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