Month: January 2020

Does Australia need More Fires (but the Right Kind)? A Multi-Agent Simulation


We have all watched with great horror the catastrophic fires in Australia. Over many years scientists have been studying simulations to understand the underlying dynamics better. They tell us, that “what Australia needs is more fires, but of the right kind”. What do they mean by that?

One such simulation of fire is based on Multi-Agent Systems (MAS), also called Agent-Based Modelling (ABM). An excellent piece of free software (and in fact the de facto standard) is NetLogo. Even better is that NetLogo can be fully controlled by R… and we will use this feature to learn some crucial lessons!

If you want to understand more about the dynamics of fire in particular and about some fascinating properties of dynamical systems in general via controlling NetLogo with R, read on!
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Explainable AI (XAI)… Explained! or How to whiten any Black Box with LIME


We already covered the so-called Accuracy-Interpretability Trade-Off which states that oftentimes the more accurate the results of an AI are the harder it is to interpret how it arrived at its conclusions (see also: Learning Data Science: Predicting Income Brackets).

This is especially true for Neural Networks: while often delivering outstanding results, they are basically black boxes and notoriously hard to interpret (see also: Understanding the Magic of Neural Networks).

There is a new hot area of research to make black-box models interpretable, called Explainable Artificial Intelligence (XAI), if you want to gain some intuition on one such approach (called LIME), read on!
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Business Case Analysis with R (Guest Post)


Learning Machines proudly presents a fascinating guest post by decision and risk analyst Robert D. Brown III with a great application of R in the business and especially startup-arena! I encourage you to visit his blog too: Thales’ Press. Have fun!
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Psst, don’t tell anybody: The World is getting more rational!


Happy New Year to all of you! 2020 is here and it seems that we are being overwhelmed by more and more irrationality, especially fake news and conspiracy theories.

In this post, I will give you some indication that this might actually not be the case (shock horror: good news alert!). We will be using Google Trends for that: If you want to know what Google Trends is, learn how to query it from within R and process the retrieved data, read on!
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