Man, colloquia abstracts are a seemingly endless source of buried jokes. Check out the grad student dig in the following summary of a talk on using machine learning to study human and animal learning:
Machine learning studies the principles governing all learning systems. Human beings and animals are learning systems too, and can be explored using the same mathematical tools. This approach has been fruitful in the last few decades with standard tools such as reinforcement learning, artificial neural networks, and non-parametric Bayesian statistics. We bring the approach one step further with some latest tools in machine learning, and uncover new quantitative findings. In this talk, I will present three examples: (1) Human semi-supervised learning. Consider a child learning animal names. Dad occasionally points to an animal and says "Dog!" (labeled data). But mostly the child observes the world by herself without explicit feedback (unlabeled data). We show that humans learn from both labeled and unlabeled data, and that a simple Gaussian Mixture Model trained using the EM algorithm provides a nice fit to human behaviors. (2) Human active learning. The child may ask "What's that?", i.e. actively selecting items to query the target labels. We show that humans are able to perform good active learning, achieving fast exponential error convergence as predicted by machine learning theory. In contrast, when passively given i.i.d. training data humans learn much slower (polynomial convergence), also predicted by learning theory. (3) Monkey online learning. Rhesus monkeys can learn a "target concept", in the form of a certain shape or color. What if the target concept keeps changing? Adversarial online learning model provides a polynomial mistake bound. Although monkeys perform worse than theory, anecdotal evidence suggests that they follow the concepts better than some graduate students. Finally, I will speculate on a few lessons learned in order to create better machine learning algorithms. (Source, but ultimately via Eric Howell on the Hacker Within mailing list.)
No exactly stand-up material, but I love that the guy was playful enough to put it in the abstract. I guess I shouldn't be surprised, though, given what I found on this project's spring 2009 schedule page. We actually had that same xkcd hanging in our office for a while.