More than ten years ago, simpleshow started to help their customers explain materials, ideas, and products by using three-minute animated explainer videos. These explainer videos use two hands and simple, black and white illustration to lead viewers through a story. Today, the company also provides mysimpleshow.com, a platform that allows anyone to produce high-quality explainer videos about virtually any topic. This platform is integrated with Amazon Polly, so anyone can use natural sounding voices for explainer videos, as long as transcripts are provided.

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By Xavier Amatriain, Cofounder/CTO at Curai.

A good six months after the previous release, another small feature release of our pinp package for snazzier one or two column Markdown-based pdf vignettes got onto CRAN minutes ago as another [CRAN-pretest-publish] release indicating a fully automated process (as can be done for packages free of NOTES, WARNING, ERRORS, and without ‘changes to worse’ in their reverse dependency checks).

Clued Recurrent Attention Model (CRAM) To overcome the poor scalability of convolutional neural network, recurrent attention model(RAM) selectively choose what and where to look on the image. By directing recurrent attention model how to look the image, RAM can be even more successful in that the given clue narrow down the scope of the possible focus zone. In this perspective, this work proposes clued recurrent attention model (CRAM) which add clue or constraint on the RAM better problem solving. CRAM follows encoder-decoder framework, encoder utilizes recurrent attention model with spatial transformer network and decoder which varies depending on the task. To ensure the performance, CRAM tackles two computer vision task. One is the image classification task, with clue given as the binary image saliency which indicates the approximate location of object. The other is the inpainting task, with clue given as binary mask which indicates the occluded part. In both tasks, CRAM shows better performance than existing methods showing the successful extension of RAM. …

In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a ‘Proof of Concept Machine Learning’ as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and among the divisions of Destatis. It was of particular interest to find out in which statistical areas and for which tasks machine learning is used and which methods are applied. This paper is intended to make the results of the surveys publicly accessible. Machine Learning in Official Statistics

As human beings we’re prone to bias in our thinking and decision making. AI can also be biased and demonstrate undesirable behavior - if we let it. In this live webinar, Colin Priest, Senior Director of Product Marketing at DataRobot will discuss how to identify and correct bias in AI.

R Tip: use seqi() for indexing.

Tools for Interactive Data Exploration (xplorerr)Tools for interactive data exploration built using ‘shiny’. Includes apps for descriptive statistics, visualizing probability distributions, inferentia …