My cross-disciplinary adventure is starting in earnest. My first courses in CS are beginning and I hope to document the experience and the outcomes on this blog. In this post I’ll set the scene for what will follow over the next nine months.
One would think that since I have already received a PhD, I would know my way around universities in the UK. But Oxbridge is quite a different beast. Its managers have an interest in keeping it that way, since they want to form a class of their own and they have largely managed to do so. Consequently, I’ve been confronted with all kind of oddities. My choice of college protects from some of the more dubious aspects. Being of relatively recent origin, it doesn’t see the need to flaunt traditions. I don’t miss having a high table.
I’m enrolled in the Advanced Computer Science MPhil, a research degree with a considerable teaching component in the first two terms. Having no degree in CS, I decided to focus on one area of interest and hone in on it. Consequently, my courses focus on Natural Language Processing (NLP) and Machine Learning. From my interdisciplinary perspective NLP exerts a particular fascination, both allowing me to draw on skills and challenging assumptions I’ve developed over my academic career in philosophy. Here are three angles I want to keep in mind:
(Human) Cognition. While the best way to make our computers process natural language might not mirror the same way we understand it language, I’m expecting that NLP helps to reveal the structure of human cognition. Of course, the influence has also gone the other direction with cognitive science informing NLP. And how does machine learning change the cognitive perspective in NLP?
Reasoning. In my philosophical life I’ve investigated interactive reasoning. Drawing on social sciences and decision theory, I looked into how we share reasons with one another and how this changes our motivational landscape. I intend to continue this research and NLP throws a different perspective in it. Both from a pragmatic and a semantic direction, I hope to get a better grasp on reason giving.
The Unexpected. What elements of computational thinking surprise me most as someone breaking into the field? Which approaches to language or cognition puzzle me the most? Are there any topics which were not on my map at all? Motivational change has not lnly been one of my interests for a while, I am open for being lead into unknown territory.