The Data Thread. Issue #2.

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The Data Thread. Issue #2.

June 18, 2019

In The News

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Deepfakes have got Congress panicking. This is what it needs to do.

With the 2020 US election looming, the US Congress has grown increasingly concerned that the quick and easy ability to forge media could make election campaigns vulnerable to targeting by foreign operatives and compromise voter trust.

AI & Machine Learning

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Adobe’s new AI tool can spot when a face has been Photoshopped

…this collaboration between Adobe Research and UC Berkeley, is a step towards democratizing image forensics, the science of uncovering and analyzing changes to digital images. In the experiment, the tool reverted altered images to its calculation of their original state, with results that impressed even the researchers.

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Rise of the Low-Code ML toolboxes

…a new genre of ML capabilities has surfaced, pioneered by what we like to call ‘Low-Code ML Toolboxes.’ Projects like the Allen Institute’s AllenNLP, and Uber’s Ludwid seek to abstract much of the programming complexity inherent to building deep learning models, while preserving the above-mentioned power of libraries such as Pytorch and TensorFlow… As a result, they address codebase management challenges for many types of ML projects.

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Why Machines Need to Dream

Evolution has provided the criteria mammalian brains rely upon to determine what should be consolidated and what should be erased, but artificial networks need people to take on the task.

Business Intelligence & Data Viz

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A Wave of Acquisitions in Business Intelligence

Holy crap! Within the past week we’ve seen the acquisitions of the two biggest players in the modern BI landscape, Looker ( announcement) and Tableau ( announcement). And if you broaden your view to the entire analytics tech stack, it’s bigger: here are the major acquisitions I’ve tracked over the past year

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Who do we think we are?

How members of the data visualization society rate themselves among the three different skills axis

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The Dangerous Flaws in Boeing’s Automated System

Boeing’s new 737 Max has larger engines placed higher on the plane, creating aerodynamics that can push the nose up in some conditions. If a plane’s angle of attack becomes too steep, it could stall and crash.

Training & Resources

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TensorFlow Model Optimization Toolkit — Post-Training Integer Quantization

Our new post-training integer quantization enables users to take an already-trained floating-point model and fully quantize it to only use 8-bit signed integers (i.e. `int8`). By leveraging this quantization scheme, we can get reasonable quantized model accuracy across many models without resorting to retraining a model with quantization-aware training.

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Simulating genetic data with R: an example with deleterious variants (and a pun)

Genetic simulation is useful for all kinds of things. Sure, they’re only as good as the theory that underpins them, but the willingness to try things out in simulations is one of the things I always liked about breeding research.

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10 Simple hacks to speed up your Data Analysis in Python

A minor shortcut or add-on can sometimes prove to be a Godsend and can be a real productivity booster. So, here are some of my favourite tips and tricks that I have used and compiled together in the form of this article.

Interesting Data

Economic Mobility – The Fading American Dream

The defining feature of the American Dream is upward mobility – the aspiration that all children have a chance at economic success, no matter their background. However, our research shows that children’s chances of earning more than their parents have been declining. 90% of children born in 1940 grew up to earn more than their parents. Today, only half of all children earn more than their parents did.


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Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations

Avoid data blunders and create truly useful visualizations Avoiding Data Pitfalls is a reputation-saving handbook for those who work with data, designed to help you avoid the all-too-common blunders that occur in data analysis, visualization, and presentation.

About Me: Pedro A. Medina, E.E. lives at the intersection of advanced analytics, business intelligence, and social responsibility. He inspires a new breed of innovative Data Scientists at Haystack Data Solutions. Connect with him on LinkedIn:

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