In my PhD project, I do research in the area of “concept formation”. Before starting to talk about my PhD research in more detail, I would like to use this post to give a quick introduction into the area of concept formation.
Looking at my posts so far, it seems that a little “What is … ?” series is emerging (“What is AGI?”, “What are conceptual spaces?”). Today I’d like to add another post to this series – this time about the term “machine learning” and about three different types of machine learning algorithms one can distinguish.
As already discussed earlier, “good old fashioned AI” is based on manually writing rules and having some sort of inference system that applies these rules in a given situation. Machine learning is more about discovering rules from a (usually quite large) number of examples.
One can distinguish three types of machine learning: supervised, unsupervised and semi-supervised.
After having sketched what hides behind the term “Artificial General Intelligence” in my last post, today I would like to give a short introduction to conceptual spaces.
The term “conceptual spaces” describes a framework proposed by Peter Gärdenfors  that aims at a geometric representation of concepts. It is the starting point of my PhD research on concept formation.
But first things first: what is a concept?
My website says that I’m interested in Artificial General Intelligence (AGI). But what does this term actually refer to? There are a bunch of buzz words out there like “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “strong AI”, “weak AI”, and so on. “Artificial General Intelligence” is one of them (although probably not the most prominent one).
So let’s disentangle this a little bit: Continue reading “What is “Artificial General Intelligence”?”