How It Works: Bitcoin Edition

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!

Stabby Robot Less Stabby

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”.

Building the right community for community editing

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.

Watch where you put that phone

This is awesome – I’ve long been in love with intrusions that rely on listening in on the sound of keystrokes, or the wiggle of a laptop screen, or what have you to learn and then reproduce what has been typed. This variation where a smartphone on your desk can pick up typing vibrations and from there learn to recognize what you are typing. It’s proof of concept, not found out in the wild at this point, but still very cool.

Are you ready for some learning?

I’m about a week out from another academic year starting (my tenth! how frightening!), and so it’s timely to share a few thoughts about learning and being a student…

Lots of attention is going to this article about a study showing that laptop use in class results in lower grades. Less press is going to the portion I remember most from when the article first came out – that someone next to you using a laptop also causes scores to drop. I’ll be mentioning this in my explanation for why I sometimes lock the classroom computers when we’re having discussions that don’t require the technology.

If you’re thinking about getting focused on your writing, consider this report on research showing pre-writing rituals can effect the quality of your writing. Or your appreciation for a carrot. And apparently the rituals don’t have to be as …. compelling …. as the examples cited in the opening paragraph of the article.

Finally, this blog post, from a generally humorous blog written by a just-graduated undergrad, on Great Books and the Discussion Method is the end point that I hope all students entering my school come to, if not by the end of the First-Year Seminar, by the end of their time with us.

Traveling Happy Truck Drivers

Starting out as an explanation of the traveling salesman problem, this article goes on to also be an excellent, and I think understandable, explanation of what an algorithm is and computational complexity. If you want to get a quick sense of what computer science is concerned about that goes beyond just “how to program”, and is more the “how to solve problems” side of things, this is a good read.

In 2006, for example, an optimal tour was produced by a team led by Cook for a 85,900-city tour. It did not, of course, given the computing constraints mentioned above, involve checking each route individually. “There is no hope to actually list all the road trips between New York and Los Angeles,” he says. Instead, almost all of the computation went into proving that there is no tour shorter than the one his team found. In essence, there is an answer, but there is not a solution. “By solution,” writes Cook, “we mean an algorithm, that is a step-by-step recipe for producing an optimal tour for any example we may now throw at it.”

The second half of the article also has some nice details about how the messy ways that people behave make the route planning problem for a company like UPS a lot more complicated than the pure traveling salesman problem:

People are also emotional, and it turns out an unhappy truck driver can be trouble. Modern routing models incorporate whether a truck driver is happy or not—something he may not know about himself. For example, one major trucking company that declined to be named does “predictive analysis” on when drivers are at greater risk of being involved in a crash. Not only does the company have information on how the truck is being driven—speeding, hard-braking events, rapid lane changes—but on the life of the driver.

This loops back to talking about (informally again) algorithms, solutions, optimization, and the idea of a heuristic approach. I’m wondering if this would also be a nice way to illustrate the idea of modeling – I’ve found that it’s a phrase we use a lot that it’s easy to nod and say “sure, a model”, but things can get confusing as you start to slip between our informal, day-to-day usage of the word model and a more technical or formal meaning. This might help bridge the gap, particularly as it starts to touch on the idea of good versus bad models.

Too many options

I’m finding a lot interesting to think about in this discussion of the Guided Pathways to Success conference and it’s investigation of the benefit to students of guidance/constraints in their educational paths: “Schwartz emphasized that even though it may seem counterintuitive and even paternalistic, students are actually much more empowered by choosing among fewer and more carefully constructed options.”

My first thoughts are about the curriculum we just instituted, which I have thought of as giving students more flexibility and choice about how they put together sets of courses to complete a major or minor. We try to make clear to students that they do not need to start with completing our list of “core” courses – that the electives are just as central to the major, many of them can also be at the introductory/no-prerequisite level, and may even be more interesting or compelling to them than the core, depending on their interests. Thinking about the core, though, as a delineated path that counteracts the excess of choice when looking at the entire catalog, makes this behavior both make sense, and makes me more comfortable with that choice of how to approach the major somehow. I think I’ve been able to mentally shift how I see our curriculum to being one that gives those students who want to have lots of freedom and choice that option, but does spell out some clear paths for students who prefer that as well.

Thinking more broadly, this also relates to some thoughts I’ve been having about MOOCs and initiatives to try to allow studdents to assemble degrees piecemeal out of courses from many institutions of many different types. The implicit question behind those initiatives is, with free or near-free education available on-line, what is gained by a more traditional school. One answer seems to be exactly this structure and advising, particularly highly personalized advising, which is essentially a collaborative narrowing of choices with the student.

Digressing a bit from the original point, I also worry a bit about what is missing from a student’s overall education when education is constructed in such a piecemeal fashion. For my own program, I think about how we teach ethics. It’s not unusual to tackle this by spreading ethics instruction out amongst several courses, teaching it alongside more technical content, as compared to having a single, designated ethics course – this is the approach we take. Obviously, I think it is a good choice – students see ethics from many perspectives and throughout their time in the program, and they see it integrated with their other activities in the field. If a student is assembling a degree, though, from a set of courses at many different institutions, I have a hard time seeing how content can be spread throughout a curriculum in this way. In theory, if every course labeled every piece of learning content with the number of hours allocated to it in the course, a system could be constructed to ensure that all boxes were checked to a sufficient degree. But this feels unwieldy, and I suspect the more tractable approach would be to fall back on mandating courses covering any required content areas (perhaps permitting for half/quarter courses to make up the slack).

Of course they put E.T. in New Mexico

The news that the landfill of Atari’s E.T. games is going to be excavated swept through the internet. This makes me doubly excited that I still have my copy and I’m considering using it an an anchor point for a “play bad games” day in my intro game design course in the fall. Particularly having also found this really cool review of the games flaws and fixes for them. It starts from the position that the game is actually fairly good, and even groundbreaking, except for a few flaws or misunderstandings about the game (such as, that it is an easy kids game to pick up, as compared to a highly challenging quest-based game) that need to be addressed. Plus you get a nice detailed explanation of why E.T. keeps falling in the darn wells all the time, and an inside look at the problem of modifying space-restricted code (there is lots of talk of finding 12 bytes here and 9 bytes there to sneak in the desired changes).

Using MOOCs to raise the bar

A recent article about how MOOCs might, in fact, increase and not decrease costs on college campuses has been getting a fair bit of attention for its argument that the large lecture classes that it replaces were already the cost-saving venues of higher education and many of the proposals for integrating MOOCs well involve replacing these cost-efficient large classes with free MOOCs and then expensive associated mentoring. Additionally, it observes that even if a college doesn’t choose to incorporate MOOCs, the fact that they exist may make students less tolerant of paying tuition for large lecture courses.

The quote that jumped out at me, though, came in the middle of this argument:

The large lecture class is efficient, with a low per-student cost as the expense of the instructor resource is spread across so many students. Every institution of higher learning would love to only have small classes, but the economics simply don’t work. Faculty are too expensive. The large lecture class subsidizes everything else.

Except, unless I’m interpreting the definition of “large” and “small” incorrectly here, I’m pretty sure that I went to a college with only small classes, and currently teach at a college with only small classes. The article’s assertion that MOOCs will lead to a shift where undergraduate education must be personal and interactive is at the same time a suggestion that there will be a strong and possibly growing market for the small, liberal arts college experience.

Of course, that requires a lot of education about what that experience really is, and how much it differs from what many people assume a college experience must be. And, as the article notes, it isn’t cheap – certainly not as cheap as MOOCs can be. But it seems that perhaps the national discussion about MOOCs can be a real opportunity to communicate those differences.

Data Vis Roundup

I’m supervising a capstone project right now where students are providing data analysis and visualization support for a local organization, and the following set of links have been queueing up in my feed as to-read items for me related to that project (and, hopefully, to-read items for them):

eagereyes has a nice summary of ISOTYPE (International System of Typographic Picture Education) which in the roughest strokes is those charts where the number of an item is represented not by a bar but as a collection of images or icons representing the thing being counted. But it’s a lot more complicated than that, including guidelines about the design of the icons.

The same site also has a nice, short illustration of how visualizing something makes it real in a way that just seeing/reading the source information or data does not. To me, this highlights the importance of being thoughtful about what you are presenting and the accuracy of the analysis behind your visualization.

In a similar vein, I enjoy Junk Charts dissections of poor data presentation; this critique of bubble charts via a self-sufficiency test analysis is a nice example and serves as a good model for a simple way to assess your own visualizations.

If you’re thinking about data visualization to monitor something (which my students are), you probably need to think about if a “dashboard” would be helpful. juice analytics has put together a collection of innovative dashboard designs, and also have a helpful link to a white paper they wrote on dashboard design at the top of the article.

We’re also probably working with some maps in our project, so this list of common problems in maps from cartonerd could be useful, as could the linked collection of UK maps which is recommended as a tool for looking for ideas of what not to do. The key focus here is on persuasiveness of the maps; the overall message is ultimately narrowed down to the question “is the map as it stands capable of properly supporting policy-making”?

On the tool front, the announcement that students can now download and use the full desktop version of Tableau for free got a fair bit of fanfare recently.

My colleagues and I teach a number of courses where we hope students will go out and find their own data sets to work with – Quandl looks like it will be a great source to share with them. It’s a searchable collection of free and open datasets, normalized into a standardized format which can then be output in a range of useful formats (right now, Excel, CSV, JSON, XML or R). I love that you can browse the data online and even see some basic graphs of it without downloading, though. This is a site that some will use to look at data and get answers directly, not just a repository to download from.