About a year ago I fervently advocated against using SQLite for storing research data, but was since proven absolutely wrong. In this article I explain where and why I was wrong, and share the real reasons why I think we shouldn't use SQLite for research: A lack of skills and time.
The category of AI is very broad. When someone talks about AI, they could be talking about anything from a very simple computer program to a large neural network. In this article I argue that talking about AI implies talking about expectations, not about actual, purposeful implementations. In order to be capable of talking about and regulating the dangers of AI we have to limit our use of the term "AI" to precisely what it denotes: expectations and possibilities that may or may not be realized some time in the future.
For two weeks I've had a serious problem of motivating myself. I had several ideas about what might've been the culprit, and I think I have finally pinpointed it: A feeling of existential dread caused by the climate catastrophe that people have already reported.
Besides actually going on vacation, I usually reserve one week of my vacation for work each year. In summer, I work for a music festival to get a break from my office and do physical work instead. This year, I had an experience that showed me the value of working a completely unrelated job for my actual work as a social scientist.
No, it's not. But yesterday, news broke that allegedly some Google chat bot system may have gained sentience. How credible are these claims? While certainly interesting, they do not prove anything – as always.
If you work with data, you can't escape them: tables. Everything from small-scale surveys to huge register datasets are represented using the mental model of a table. While this representation of our data has worked for the better part of recorded sociological history, we are increasingly experiencing roadblocks: our analyses run slow, sometimes our computers crash, and many exciting research opportunities are left unasked, because we fear our computers will be incapable of helping us answer them. In this article, I provide – again – a somewhat technical explanation for what makes data tables so ill-suited for so many tasks, and what we can do instead.
Deep learning is a fascinating piece of technology. It basically consists of chaining and stacking together millions of very small functions that, in effect, can predict incredibly complex things. This also means that deep learning can sometimes feel like witchcraft. For example, why do two classifiers almost always perform better than a single one? In this article I'll dig deeper into this curious finding.
The internet is full of scammers, and their most beloveth tool is emails. Therefore, whenever you receive an unsolicited email, you should be very careful whether even to reply. But even with many years of experience and after doing proper research, scammers can get you. Let me today share an episode where I almost got scammed myself.
France has voted. And it got another five years to ensure that the right-wing candidate Marine Le Pen has no chance of securing the presidency of one of the central states within the European Union. However, these elections were a warning sign for Emmanuel Macron. Here, I collect some loose thoughts on the election.
Zotero 6 is out — time for a review. I tested the app in production for two weeks now and want to share my thoughts.