Description: Chairperson Address to the Predictive Analytics and Futurism Section

Description: The article is about how current advancements in AI can both help and potentially replace actuaries. We focus on three types of AI as defined by Numenta.org: Classic AI, Machine Learning, and Machine Intelligence. We present how Machine Learning and Machine Intelligence can affect actuarial analysts and actuaries alike. We then show how Machine Intelligence can be used to help regulators possibly construct a system or model that dynamically learns the behavior of insurance companies to predict failing insurance companies.

Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.

Description: The newer predictive modeling tools are powerful but they work best when supported by a lot of computing power. Parallel processing is very powerful but often very difficult to implement. Dihui and Richard take us through the maze of options with H2O, SparkNet/MLib and Mahout. Which do you use for a generalized linear model (GLM), and which is better for high-volume data manipulation?

Description: A discussion on artificial intelligence.

Description: An introduction to creating Shiny web applications, with sample code available to reproduce the interactive application displayed in screenshots in the article.

Description: This article is about how insurance companies are deploying insurance technologies.

Description: Introduction to PMML and how to use of it in the R programming language. PMML is an interchange format for predictive models.

Description: Anders reviews the results of a recent survey of PAF section members and discusses initiatives that are planned in response.

Description: It cam oftentimes be difficult to determine the easiest way for an SOA section member to volunteer. With our section's new focus, we will need a lot of help. This article talks through some of the more accessible places that section members can get involved.