HCqa: Hybrid and Complex Question Answering on Textual Corpus and Knowledge Graph

By Derrick Mwiti, Data Analyst

Text Analyzer Shiny’ (TAShiny)Interactive shiny application for working with textmining and text analytics. Various visualizations are provided.

Amazon SageMaker is a managed and infinitely scalable machine learning (ML) platform. With this platform, it is easy to build, train, and deploy machine learning models. Amazon SageMaker already has two popular built-in computer vision algorithms for image classification and object detection. The Amazon SageMaker image classification algorithm learns to categorize images into a set of pre-defined categories. The Amazon SageMaker object detection algorithm learns to draw bounding boxes and identify objects in the boxes. Today, we are excited to announce that we are enhancing our computer vision family of algorithms with the launch of the Amazon SageMaker semantic segmentation algorithm.

ggeffects (CRAN, website) is a package that computes marginal effects at the mean (MEMs) or representative values (MERs) for many different models, including mixed effects or Bayesian models. One of the advantages of the package is its easy-to-use interface: No matter if you fit a simple or complex model, with interactions or splines, the function call is always the same. This also holds true for the returned output, which is always a data frame with the same, consistent column names.

It has become commonplace to assert that autonomous agents will have to be built to follow human rules of behavior–social norms and laws. But human laws and norms are complex and culturally varied systems, in many cases agents will have to learn the rules. This requires autonomous agents to have models of how human rule systems work so that they can make reliable predictions about rules. In this paper we contribute to the building of such models by analyzing an overlooked distinction between important rules and what we call silly rules–rules with no discernible direct impact on welfare. We show that silly rules render a normative system both more robust and more adaptable in response to shocks to perceived stability. They make normativity more legible for humans, and can increase legibility for AI systems as well. For AI systems to integrate into human normative systems, we suggest, it may be important for them to have models that include representations of silly rules. Legible Normativity for AI Alignment: The Value of Silly Rules

Shervine Amidi, graduate student at Stanford, and Afshine Amidi, of MIT and Uber – creators of a recent set of machine leanring cheat sheets – have just published a new set of deep learning cheat sheets. These “VIP cheat sheets” are based on the materials from Stanford’s CS 230 (Github repo with PDFs available here), and include topics such as:

Azure SQL Database, the database-as-a-service based on Microsoft SQL Server, now offers R integration. (The service is currently in preview; details on how to sign up for the preview are provided in that link.) While you’ve been able to run R in SQL Server in the cloud since the release of SQL Server 2016 by running a virtual machine, Azure SQL Database is a fully-managed instance that doesn’t require you to set up and maintain the underlying infrastructure. You just choose the size and scale of the database you want to manage, and then connect to it like any other SQL Server instance. (If you want to learn how to set up an Azure SQL database, this Microsoft Learn module is a good place to start.)

The recent ABC News article Australia’s pollution mapped by postcode reveals nation’s dirty truth is interesting. It contains a searchable table, which is useful if you want to look up your own suburb. However, I was left wanting more: specifically, the raw data and some nice maps.

Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals