# Edwin Wenink

## Philosophy | Artificial Intelligence | etcetera

Welcome to my personal space on the web ↯.

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## Ai ENGRAMS

Automation and the ethics of work

For a working group I give at the Radboud University, students were asked to read “The Coders Programming Themselves Out of a Job”. This article discusses the ethical considerations of people automating their own jobs, either partly or completely. What are these considerations, and how do they relate to common work ethic? On the one hand, these people fulfill their job description to perfection through the scripts they wrote. One could applaud this clever increase in efficiency and …

Ethics of Autonomous Vehicles: beyond the trolley dilemma

This is a short commentary I wrote in 2017, on Patrick Lin’s “Why Ethics Matters for Autonomous Cars”. The book Autonomous Driving formulates a set of use cases that serve as a reference point for discussing the technical, legal and social aspects of autonomous driving (Wachenfeld et al. 2016). In addition, Lin sketches scenarios that require ethical choices to be made by autonomous vehicles (AVs). An advantage of this approach is that these scenarios can take the form of …

Calculating pi with Monte Carlo simulation

I came across Monte Carlo sampling in a class on Bayesian statistics, where a Markov Chain Monte Carlo (MCMC) sampler was used to approximate probability distributions that were otherwise hard to calculate due to nasty integrals. This posts illustrates the basic idea of Monte Carlo sampling, by using it to approximate the number \(\pi\). The basic procedure is as follows: Take a circle with radius \(r\) The area of the circle is \(\pi r^2\) Draw a containing square which then has area …

Course overview

I drafted up a preliminary list of the courses I followed at university. It can be found here, and I also added a link to it in the “about” section of this blog.

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## Tag roulette

## Latest micros

#### Min-hashing (Mining Massive Datasets)

📖 https://www.youtube.com/watch?v=96WOGPUgMfw

↬ Superb explanation of min-hashing as a method for efficiently approximating Jaccard similarity on bit vectors.