Roundup 12/1/2016

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

Events

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

AWS Launches AI Services

Amazon Web Services launches three new services:

A massive medialanche ensues.

Serdar Yegualp applauds. In TechCrunch, Frederic Lardinois touts the announcement as Amazon sharing its “machine learning smarts”. Meh. It seems to me that AWS is well behind Google, Microsoft, and IBM in machine learning.

People

— In a podcast, Ben Lorica interviews Mike Franklin, co-director of Berkeley’s recently wrapped-up AMPLab project, who talks about AMPLab’s legacy. That legacy includes Spark, Alluxio, BlinkDB, KeystoneML, and Succinct, among other projects.

Fundamentals

— Aatash Shah explains the differences between machine learning and statistics.

Research

— In the second installment of a planned series on deep learning research, Adit Deshpande explains reinforcement learning. The first installment covered generative adversarial nets.

— Professor Neil Lawrence of the University of Sheffield suggests that you avoid panic, assures you that deep learning will be mostly harmless, and offers some thoughts on new directions in kernels and Gaussian processes. He adapted his blog post from a recent talk at a workshop aptly named New Directions for Learning with Kernels and Gaussian Processes.

Methods and Techniques

— In MIT Technology ReviewNicholas Diakopoulos and Sorelle Friedler propose a framework to ensure accountability for algorithms. They stress five principles: responsibility, explainability, accuracy, auditability, and fairness.

— On GitHub, Simon Brugman builds a collection of deep learning papers.

— Aaquib Saeed explains how to implement a convolutional neural network in TensorFlow for human activity recognition.

Software/Services

— The BigML team releases bigml 4.7.0, an open source Python binding to the public BigML API.

Applications

— Ram Shankar Siva Kumar describes an approach to testing security procedures by using machine learning to simulate attacks.

— Google uses machine learning to write the snippets that accompany search results.

— Charlie Osborne breathlessly explains how machine learning can stop terrorists from money laundering. She seems to think that machine learning in AML is a new thing.

— In The Huffington Post, Adi Gaskell describes how machine learning supports healthcare.

Companies

— In Fortune, Aaron Pressman speculates that big tech companies will compete to acquire machine learning companies in 2017. It’s a safe bet. After all, if Apple is willing to shell out $200 million for GraphLab Dato Turi, they’re willing to invest in anything that sounds remotely like machine learning.

Bottom Story of the Day

— The Internet Archive builds a replica database in Canada due to concerns about Donald Trump’s election. The irony there is that by locating the database in Canada, The Internet Archive will be subject to the Canadian PIPETA law, which includes EU-style restrictions on data collection and governance, including the “right to be forgotten.” So the Canadian version of the Wayback Machine may have to delete all those references to Max Mosley’s Nazi S&M sex scandal.

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