Step 12: Prepare for a Topics in AI Course (Part 5)

This is part of a series on the Topics in Artificial Intelligence course I will be teaching in the fall. The first part was posted on 2015-06-23.

Going by the original plan, the Topics in AI course will have four parts. The topic for the last part, however, is still to be determined. Looking back on how I planned the previous three topics, there may not be room for a fourth at all, if I want to get deeper into the other ones. Still, it’s worth thinking about the space of possible topics. One trend I noticed is that the previous topics (reinforcement learning, cognitive architectures, Bayesian networks) are all driven by algorithms, and not necessarily by problems; the techniques are generic and can be adapted to any particular task.

In contrast, for example, spending time on natural language processing would require students to not just think about computer science, but also bring in knowledge from linguistics and other fields. I’m not sure how much “real” NLP I can do – my NLP background has always been more on the data-mining side, the difference being I care not so much about really “understanding” language as the ability to do cool things with it. NLP has a long history though, and it may also be an interesting time to bring up Turing tests (if students have not already heard of it).

Another potential topic is some robotics topic. The “obvious” choice would be some kind of robotic control – for example, SLAM is how robotics figure out what the world is like and where in the world they are. My expertise here is even worse than with NLP, and it’s bad enough that it’s a consideration against this topic. At the same time, it would be cool for students to explore how hard it is to really deal with the real world, and how much of the “easy” things people do are actually quite hard (aka. Moravec’s paradox).

Finally, in keeping with the current trends in AI, I can see a module on neural networks and deep belief nets. I will probably be learning as much as my students on this topic, but I’m worried about the mathematical nature. It’s also unclear to me what students will learn from this – there are many devils in the details of DBNs, and it’s unclear to me what I want students to take away.

These are all possibilities, and another one to throw out is to allow students to pick some topic then do stuff with it (it is a more advanced course after all). In the end, I suspect I’ll have to talk to students to figure out what they are interested in. I will be sure to post an update when the semester gets to that point.

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Step 12: Prepare for a Topics in AI Course (Part 5)

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