Roundup 12/2/2016

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

ZDNet has a special section on AI and machine learning. I’ve pulled some of the interesting pieces and linked them in the appropriate sections below.

Issues

— In Data Science Central, William Vorhies asks: has AI gone too far?  The context of his question is a paper that summarizes research into detecting criminality from facial images. In short, the researchers were able to successfully distinguish criminals from non-criminals in a sample of Chinese men aged 18 to 55 solely from facial measurements extracted from pictures. Vorhies notes that the research is rigorous, and, while the paper has evoked a chorus of criticism for its implications, critics have not yet identified a flaw in the methodology.

— In an article about fake news, Vincent Granville opines that it’s hard to detect fake news with machine learning because it’s hard to define fake news.

Fundamentals

— Alison DeNisco explains why AI and machine learning need to be part of your digital transformation strategy.

— Hope Reese lists five ways to get started implementing AI and ML.

Research

— MIT researchers develop a computational model of the human brain’s mechanism for face recognition.

— Cognitive scientist Joscha Bach ruminates on the elements of human intelligence we seem to be missing in AI.

Methods and Techniques

— In a podcast, Jon Bruner and Pete Skomoroch interview Richard Socher, chief scientist at Salesforce, and discuss how to make neural networks more accessible.

Software/Services

— Health tech startup Health Catalyst launches Healthcare.ai, a suite of open source packages for healthcare machine learning with R and Python APIs. The R package works with any R distribution and RStudio; the Python package works with Anaconda.

— Rescale’s Mark Whitney explains the ins and outs of running deep learning in the cloud. Rescale offers a managed service for deep learning in IBM Cloud.

— Reporting from AWS re:Invent, Doug Henschen argues that Amazon can make up for its late entry into machine learning services will be offset by its scale. File that under Things That Ain’t Necessarily So. AWS has a well-deserved reputation as the Stupid Cloud, and three services don’t begin to match what Microsoft, Google, and IBM offer.

— Nick Heath asks if Microsoft should be your AI and machine learning platform. He answers his own question by enumerating the many different services in the Cortana Intelligence Suite.

— Natalie Gagliordi asks the same question of Google.

— Hope Reese wonders if AWS should be your AI and machine learning platform. She quotes Gartner’s Alexander Linden, which is a bad sign.

— Conner Forrest asks if declining tech giant IBM should be your AI and machine learning platform. He doesn’t really answer the question.

Applications

— In Forbes, Suparna Goswami explains how an Indian startup uses machine learning for smarter hiring.

Companies

— Also in Forbes, Aaron Tilley chronicles NVIDIA’s transformation from a maker of gaming chips to a maker of AI chips.

— Jermy Hsu profiles Maluuba, a startup that uses deep learning to understand speech.

— Connor Forrest reports on five upstarts that are “leading the AI and machine learning revolution”: Uber, Tesla, Salesforce, NVIDIA, and Ayasdi. Wait, what? Ayasdi?  Also, for the record, Salesforce may be buying companies, but it’s not exactly leading the charge in machine learning.

Bottom Story of the Day

— GE CEO Jeff Immelt says he’s ready for Trump.

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2 comments

  • Thomas, Thanks for including my AWS re:Invent perspectives in your blog. Note, I didn’t say AWS would necessarily catch up with its rivals on ML. My reference to “scale” related to the fact that Amazon has many times the number of businesses and developers on its platform than all of its major cloud rivals combined. Having all these developers using AWS services with all that data absolutely will make a difference in how quickly AWS capabilities can mature. The best ML and AI options and the most heavily used ML and AI options may well be two separate things, as we’ve seen over and over again in the tech world.

    • Doug,

      Thanks for reading! I understand your point but am simply not convinced that AWS customers will adopt an inferior ML service just because they have other apps running on AWS. By that same reasoning, Oracle would have surpassed SAS in advanced analytics years ago. AWS customers did not choose AWS for its analytics — either they do not need analytics, or they chose AWS for its IaaS capabilities and built their own analytics on the AWS platform.

      Microsoft, Google, and IBM are all betting that superior analytics will pull customers to their platform, or at least make them competitive for greenfield use cases. We don’t know yet if that is a viable strategy, but I certainly wouldn’t advise a client looking for an analytics platform to choose AWS.

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