I read this story wanting to understand if the data mining they’re doing is really appropriate for making individualized statements in the way they are claiming when they suggest that hospitals will get risk assessments based on patient shopping data through credit cards, store cards, etc. Will receiving doctors get sufficient training in the ways in which these predictions are like and unlike the predictions that medical tests make about health risks?
Additionally, I read through the list of hypothesized triggers for heath risks and they seem to bank on the idea that everything I’m purchasing is for myself. Just in the first paragraph the article suggets issues if “ou’ve let your gym membership lapse, made a habit of picking up candy bars at the check-out counter or begin shopping at plus-sized stores” which could nicely match my patterns this year of regularly picking up candy for the lab and buying some clothes for an elderly relative who can’t get out to stores as easily anymore. The majority of the time I buy donuts, I do not actually eat any of the donuts – and I have colleagues whose donut-eating patterns may more closely match my shopping trends than theirs.
And then you get this statement: “While the hospital can share a patient’s risk assessment with their doctor, they aren’t allowed to disclose details of the data, such as specific transactions by an individual, under the hospital’s contract with its data provider.”
I’m not sure if that’s good — hooray for not sharing personal details! — or worse — so the computer says I’m at risk but we can’t sit down and talk about whether the patterns it’s identifying are real. And, going back to my opening question – what sorts of algorithms are being used and given that, what sorts of conclusions are even valid to draw.
With the semester over, I’m looking to what projects I’ll be taking on for the next couple of months, and I know many of my students are as well. Here are a few fun options people may want to consider, particularly focused on opportunities to get involved with data analysis:
I am always fascinated and creeped out by these stories about adapting system behavior to user emotion. The system described here is being tested out by analyzing facial expressions to detect engagement with educational materials which are then used to predict test performance. I’d love to see some extracted data of what engaged expressions look like. I’ve had too many conversations with colleagues where I’ve asked “You teach X a lot, is that angry look they get their thinking look?” to expect that engaged expressions must look like entertained or pleased expressions, and I know my students have that conversation about my own facial expressions as well. The applications of this also seem significantly more useful (and easier to consider managing the flow of personal data about one’s emotions) if such a system were embedded in one’s own computer and thus tuned to the vagarities of one’s own facial expressions.
I am sure the intended use for such a tool would be online educational materials, whether from a flipped classroom setting or a MOOC or what have you. But I can’t help but picture physical classrooms fixed with cameras at the front of the room, scanning all of the students and registering real-time engagement graphs on a lectern at the front. So file this away, along with Google Glass, as another piece of evidence we’ll be seeing camera-blocking devices, or straight-up masks as a fashion accessory, becoming more prominent in the coming decades.
IEEE’s prediction that 85% of the tasks in our daily life will include game elements by 2020 sounds to me like a prediction that requires thinking about game elements broadly enough, it might already be true. Considering this quote in particular, “by 2020, however many points you have at work will help determine the kind of raise you get or which office you sit in”, if you’ve ever had a performance review rating you on a number scale for different job functions, congratulations, your job is gamified! Does grocery shopping get you gas points? Your errands are gamified! Students, grades aren’t a drag, they’re a gamification of your learning!
I’m not trashing on gamification – I’m intrigued by it and always love when my games students experiment with it in their projects. But, I’m dubious of the 85% number cited in the article. Even if we all start getting Sparkleponies.
Weird Bug starts off for the first, say, 30 seconds looking like your standard puzzle-maze game, until you realize the first maze isn’t beatable, and that the real puzzle is how to go into the source code for the maze and fix it so the maze can be beat. The mazes are implemented in PuzzleScript, and the bulk of the game you’re in an IDE interface, changing the code, rebuilding, and playing your fixed level to get on to the next, broken level.
If you’ve ever coded before, you’ll be able to figure out PuzzleScript in just a minute or two of scanning the code, but there’s some tutorial information embedded in the game for those just getting started looking at code. Once you figure out the structure, you can really choose how you want to beat the mazes – I haven’t played it all the way through but I suspect you can always take the easy way out and place the goal right next to the player and move on. Which also makes the game a nice platform for thinking about level design.
I’m pretty blown away by Nothing To Hide, a currently free, browser-based puzzle game with a great premise and one of the most interesting introductory “scenes” I’ve come across. You play a character who must ensure that they are being surveilled at all times while moving around the world (for reasons the opening will make clear). The web-version is actually a demo being used to raise funds for a full version, but it’s as polished and fleshed out as any number of full online games I’ve played. Even in its handful of levels, you get a taste of the variety of elegant little puzzles you can create with the game’s premise and small set of game resources. Well worth a play, and an eye out for the extended version!
I hadn’t run into the unsolved Dorabella cipher before (that I remember). If you enjoy such things I highly recommend this account of it, with its many proposed decryptions that make clear why one of the conditions for a verified solution is that it “be self-evident”. It’s an excellent example of why decyphering without context is hard (maybe impossible?). And I enjoy the proposed solution that takes encryptions errors into account as a possibility, considering that it was done by hand, and by someone considered prone to such errors.
Nothing like avoiding end-of-the-year physical cleanup with end-of-the-year virtual cleanup! I finally got around to reading this detailed description of how Bitcoin works, recommended by Schneier on his weblog, and I need to hang on to this for next time I’m teaching security. From a teaching perspective, it does a nice job of showing how all of the various types of cryptography come together in an interesting way in this protocol. This is the part that always seems sort of wild to me:
The idea is to make it so everyone (collectively) is the bank. In particular, we’ll assume that everyone using Infocoin keeps a complete record of which infocoins belong to which person. You can think of this as a shared public ledger showing all Infocoin transactions. We’ll call this ledger the block chain, since that’s what the complete record will be called in Bitcoin, once we get to it.
The article does assume you have some cryptographic background, but I suspect that reading along as far as you can through the article would at least explain what some of the problems that Bitcoin has to solve are. A nice read with a cup of tea on a snowy day, especially if you’re a student getting your brain back into gear for school in another day or two!
First, and most important obviously, this is pretty neat research into training robot motion with online, and non-optimal, feedback. It’s a nice consideration of the type of feedback one is likely to get, or to get easily. And the illustrative video on their page is pretty great (I found the moment when they showed the robot how to point the knife towards itself, not someone else, adorable…)
But it’s also worth noting that the TechCrunch story on the research is pretty hysterical: “Cornell Researchers Help Robot Unlearn Stabby Motions With A Human Trainer”.
There’s something unavoidable about Wikipedia, even when you acknowledge its flaws, which makes it a constantly interested phenomenon to investigate and analyze. As the article notes, its rankings in search results and use in question answering systems like Siri only make it more interesting to understand what’s going on with it. Looking at the effect of the editorial structure and automated tools for handling edits is particularly interesting; I hadn’t really thought about the effect of bots on participation in this way:
In their paper on those findings, the researchers suggest updating Wikipedia’s motto, “The encyclopedia that anyone can edit.” Their version reads: “The encyclopedia that anyone who understands the norms, socializes him or herself, dodges the impersonal wall of semi-automated rejection and still wants to voluntarily contribute his or her time and energy can edit.”
The solution, the addition of a “thank” button instead of only negative feedback options, is pretty elegant.