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Preview: Anne Z.

Anne Z.



notes on numbers and other randomness



Last Build Date: Tue, 11 Apr 2017 11:46:36 +0000

 



Daily Links 04/11/2017Anne Z.

Tue, 11 Apr 2017 11:45:35 +0000

Demystifying data science The key to a successful analytical model is having a robust set of variables against which to test for their predictive capabilities. And the key to having a robust set of variables from which to test is … Continue reading (image)


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Daily Links 04/05/2017Anne Z.

Wed, 05 Apr 2017 15:32:51 +0000

New technology pushes machine smarts to the edge “The set of possible smart edge devices that can be used for industrial control is rapidly expanding as ever more compute and sensing capability moves to the edge,” says Greg Olsen, senior … Continue reading (image)


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Daily Links 04/04/2017Anne Z.

Tue, 04 Apr 2017 14:51:52 +0000

Emotion Detection and Recognition from Text Using Deep Learning The researchers used a data set of short English text messages labeled by Mechanical Turkers with five emotion classes anger, sadness, fear, happiness, and excitement. A multi-layered neural network was trained … Continue reading (image)


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Has the term AI become meaningless? How about MI insteadAnne Z.

Sat, 25 Mar 2017 19:30:02 +0000

Ian Bogost writing for The Atlantic says that in too many cases today “artificial intelligence” is just another name for a fancy computer program. I don’t see it that way. I know from experience that what most data scientists are … Continue reading (image)


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The dialectic of analyticsAnne Z.

Mon, 30 May 2016 18:53:38 +0000

From Gartner’s report The Life of a Chief Analytics Officer: Analytics leaders today often serve two masters: “Classic constituents,” with maintenance and development of traditional solutions for business performance measurement, reporting, BI, dashboard enhancements and basic analytics. “Emerging constituents,” with … Continue reading (image)


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Daily Links 01/27/2015Anne Z.

Tue, 27 Jan 2015 16:03:42 +0000

“What then should we teach about hypothesis testing?” – Statistical Modeling, Causal Inference, and Social Science Statistical Modeling, Causal Inference, and Social Science Traditionally we say: If we find statistical significance, we’ve learned something, but if a comparison is not … Continue reading (image)


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Paradigm shift: From BI to MIAnne Z.Screen Shot 2015-01-21 at 11.43.46 AM

Thu, 22 Jan 2015 16:00:12 +0000

I listened to a Gartner webinar Information 2020: Uncertainty Drives Opportunity given by Frank Buytendijk yesterday and it got me thinking about the evolution (/revolution?) from business intelligence (BI) to machine intelligence (MI). I see this happening but not as fast as I’d like, … Continue reading (image)


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Daily Links 01/19/2015Anne Z.

Mon, 19 Jan 2015 23:53:10 +0000

Jerry Seinfeld’s Productivity Secret He told me to get a big wall calendar that has a whole year on one page and hang it on a prominent wall. The next step was to get a big red magic marker. He … Continue reading (image)


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Daily Links 12/22/2014Anne Z.

Mon, 22 Dec 2014 23:42:36 +0000

What is science and why should we care? — Part III | Scientia Salon The bottom line is that science is not merely a bag of clever tricks that turn out to be useful in investigating some arcane questions about … Continue reading (image)


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Flexible, reconfigurable networks at workAnne Z.

Sun, 21 Dec 2014 16:38:40 +0000

Jon Husband, interviewed by Stowe Boyd, on his concept of “wirearchy”: I have been involved in a long and intense process of study of the sociology of work, organizations and institutions for 40 years now. Today I believe that a … Continue reading (image)


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