With the intentions of ending the week well, I decided to go through Mozer’s paper. Our seniors and professor considered this to be a model paper given the extensive use of reinforcement learning by the researchers.
Yesterday we’d asked Professor Littman for a text on machine learning. The result of our query was a hefty tome – Artificial Intelligence, a Modern Approach by Peter Norvig (one of my role-models) and Stuart Russell. The concepts were straightforward enough to understand but the text was littered with forehead-wrinkle-inducing jargon (eg: classification, regression, realizable hypothesis spaces). I managed to get through a section or two, stopping at ‘learning decision trees’; it was slow going especially since I was making a personal (metaphor filled) summary on my research wiki.
Today I waded through a paper outlining the shortcomings of the Nest smart thermostat and its limited success at learning users’ preferences. I also read a long rant from a tech blogger who had tried and failed at basic home automation. The combined (albeit limited) picture I got from the two articles conjured a somewhat bleak status quo for machine learning and user-programming in smart homes. Which of course, is just what an eager young researcher wants to hear (I’ll also try reading some more upbeat literature tomorrow).