For fall 2014, I took Knowledge-Based AI (GT CS-7637). Frankly, before I enrolled for the class I didn’t really have much clue what separated “knowledge-based AI” from other types of artificial intelligence, but I was fascinated by the topics mentioned in the course description (i.e., Watson).
There were two different types of assignments for the semester – one involved writing papers on different KBAI topics, and the other involved writing code to solve various forms of Raven’s Progressive Matrices.
The latter were really interesting. I’ve posted the code I developed to solve these problems on github, which you can view here:
We started with very simple versions of the problems, solving them based on textual descriptions, and in the final project we had to actually analyze image data and make our best guess. This is where my experiences with OpenCV proved really beneficial.
I structured my code around identifying shapes and sizes using OpenCV, recreating the textual descriptions that we used in the earlier projects. This allowed me to reuse most of the code from project to project, with only slight tweaks to optimize for data that was difficult to get using OpenCV.
Other students approached the problem by looking at pure pixel data, but I greatly preferred this method. KBAI is largely about modeling knowledge structures/information in a way that is similar to how humans store and process it. Pulling out information about each shape, in a way a human might describe it to someone who can’t see it, seemed more along the lines of KBAI. It also had the benefit that it just made more sense to me!
I’ve been feeling an itch to do some programming lately, and stumbled across these Coffee Time Challenges while browsing reddit. It’s not really “coffee time” for me right now since I’m on summer break, but I think the idea still applies.
I decided to post my solutions to github. You can of course browse them (but you should really try them on your own first!).
I had the pleasure of taking GT CS-8802: Artificial Intelligence for Robotics this past term as part of my first semester working on my Online Master of Science in Computer Science degree (that’s a mouthful). The course was taught by Sebastian Thrun, who besides founding Udacity, also works for Google on their autonomous car team.
I worked with a team of two other engineers to investigate localization techniques using landmarks based solely on visual information. In other words, we wrote code to help a robot figure out where it was based on pictures. Rather than read about it in boring text, though, you should watch our presentation video!
You should take Computer Science at Hathaway Brown. But don’t take my word for it – watch this video explaining why!
I made the video mostly using Scratch, with a little bit of editing done later on to tighten up the timing and sync the audio. You can actually view/remix the Scratch project here:
The character sprite is based on the Jeff Winger sprite from the Community episode Journey to the Center of Hawkthorne. I used a similar animation last year, actually, but since then I’ve grown a beard and needed to update the sprites. 🙂
Hathaway Brown has a 6-day rotating schedule that makes it very hard to set up recurring events in most calendar programs.
We have used a solution called DynaCal, which supports all sorts of crazy school schedules, but DynaCal’s interface is pretty cumbersome for our faculty and staff to use. We are a Google Apps for Education school, and we want to encourage faculty to use Google Calendar rather. At the prompting of our director of academic technology, I decided to put together a web-based solution that would create iCal files based on our unusual schedule needs (which could be imported right into Google).
This was also a chance for me to make an interesting web app using Flask and deploy it using things like requirements.txt and pulling from a git repository.
You can see the code here: https://github.com/jamesmallen/calfiller
And you can see what the interface looks like here: http://calfiller.jamesmallen.net/hbus
My students in Intro CS this year have decided they want to build a tabletop arcade game for their final project. They picked this because they had heard about the Raspberry Pi, and wanted to do something cool with it. I swear that I had no influence on them choosing this project (though my personal collection of arcade games may have influenced my willingness to allow them to do something of this scale).
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