kip/bot/blog

The first 1:30 of the documentary Moog has a great line from Robert Moog, the inventor of the synthesizer and one of the parents of electronic music: “I can feel what’s going on inside of a piece of electronic equipment.” There are other passages in the movie (which has great tributes from electronic music performers such as Sun Ra, DJ Spooky, Mix Master Mike, Electric Skychurch and a wonderful animated title sequence), where he shows how visualizes the interactions of circuits and components. Interestingly, he also points out that his synthesizers are analog instruments, not digital ones and that he knows musicians approach the physicality of the electrical vibrations in the same way they approach violin, guitar, and piano strings.

Anyway, that close connection to material — which strikes me as a result of closely working and experimenting with them — as a source of inspiration, quality, and innovation is a theme near and dear to me. So, I clipped the movie and encourage would-be innovators and inventors to check it out. Moog the man is charming, engaged, lives a full life, and could be a more earth-bound person to learn from than Steve Jobs, Edison, and other lofty luminaries.

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PS One last reason to watch the movie is a Schaeffer beer commercial in which a 70s-mustachioed dude in polyester, riffs on the Moog for 20 seconds before the jingle (“Schaeffer is the one beer to have when you’re having more than one.”) Worth the price of admission all by itself.

A colleague just sent me a link to an MIT student project/installation site, called “Personas: How does the internet see you?”, which is part of a larger exhibit called Metropathologies. You type in your name and it assesses what you are/do/care about based on on-line presence. Fun idea, great animation during the algorithm crunch, surprising results:

(Click image for larger, cleaner version.)

Amused: sports so large, fashion that it shows up at all (must be based on client lists)
Saddened: politics is so little (and in black! like a mournful armband)
Pleased: design and art seem to be big
Concerned: medicine?
Can’t tell if this blog is covered in it . . . that might explain the sports, what about flickr? Need to explore.

Stumbled across the OLPCorps on Flickr this morning. The program is pretty awesome: propose a teaching plan for a 9 – 10 week program in any African country if you get accepted, they help you go, hook you up with equipment, and then you participate in a conference about computing, constructivism and your experience. Best part of the find, though, was an interesting nugget buried in the FAQ for the program:

[Question] Can we give XOs to several schools to start computer labs?

One does not think of community pencils—kids have their own. They are tools to think with, sufficiently inexpensive to be used for work and play, drawing, writing, and mathematics. A computer can be the same, but far more powerful. Furthermore, there are many reasons it is important for a child to own something—like a football, doll, or book—not the least of which being that these belongings will be well-maintained through love and care. Read Core Principles for more.

The picture at the top of this post has nothing to do with OLPCorps. I couldn’t find many OLPCorps pictures, but in the process of searching came across this one. The Lego attachment is a viewfinder you attach to the laptop, in order to use the XO as a point and shoot camera.

or should be to a certain extent, if we take recently anointed Google numbers guru Hal Varian’s words to heart. The former economist (a very heavy maths-focused one at that) is frequently quoted as saying that statistician will be the next ‘sexy’ job (just like engineer was), but the line, from McKinsey goes much deeper:

I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s? The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.

I think statisticians are part of it, but it’s just a part. You also want to be able to visualize the data, communicate the data, and utilize it effectively. But I do think those skills—of being able to access, understand, and communicate the insights you get from data analysis—are going to be extremely important. Managers need to be able to access and understand the data themselves.

I recently started working my way through Ben Fry’s Visualizing Data and adding Fry’s process to Varian’s shows some of the deep changes people need to make in order to embrace the new numeracy. Visualizing Data is more about Fry’s Processing language and how to hook it to datasets than it is about thinking visually or how to work through those datasets to find a pattern or evocative image, but it begins with a seven-step process:

ACQUIRE — Obtain the data, whether from a file on a disk or a source over a network.

PARSE — Provide some structure for the data’s meaning, and order it into categories.

FILTER — remove all but the data of interest.

MINE — Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context.

REPRESENT — Choose a basic visual model, such as a bar graph, list, or tree.

REFINE — Improve the basic presentation to make it clearer and more visually engaging.

INTERACT — Add methods for manipulating the data or controlling what features are visible.

This does a nice job of highlighting that Varian’s charge is a mix of skills for managers, practitioners, and interpreters alike. Some of the steps are naive or described in a way that invites unhealthy simplisticism (simplicity == good, simplisticism, the thing we often get instead of simple is reductive, which is always bad). MINEing and REPRESENTing are the steps where numbers emerge into something living and actionable. MINE, as defined by Fry, is focused on software, rather than cognitive styles and elastic minds, for the generation of insights and pattern recognition. Certainly software is needed, but the hypotheses and candidate patterns you validate with the software come from soft eyes, something I blogged about a while ago. Similarly, REPRESENT is posed as choosing from a list of standard data tropes. But hey, it’s a software book and we all know Fry is more visual than that.

The real point is that this path shows a range of skills and validation even broader than what Varian points to. Someone working with someone working with data needs to know, understand, and respect the technical underpinnings of the first two steps, which set up the infrastructure of your entire data exercise. Like software, you need to measure twice, cut once here because this is the infrastructure of your inquiry and you won’t be able to change it quickly. Filter, mine, represent are subjects for another book perhaps, but they put you in the land of Tufte, Orwell, as well Flowing Data and statistics — a mix of simple communication, humanities, and the techniques of numbers.

The last one was also pretty interesting. I love how Fry reminds people to let the data grow with the audience by giving some interactivity. Sure, you do the first crack at it, but letting your audience go deeper, create their own juxtapositions, or simply play with the data gets them more engaged, allows for even more meaning to emerge from the data.

http://www.kipbot.com/blog/2008/03/05/dd-my-grad-school-footnote/

Interesting read in May 2009 issue Servo Magazine, which I got free at Maker Faire about new ways to teach groups.

The writer/editor, Bryan Bergeron, teaches a course on technology and the future of healthcare at Harvard Medical School. Each year, a session of the class simulates the creation of a business to give students a brief sense of the hours, adrenaline rush, complexity, and many dimensions of a tech start-up. This year, he did something new. He had his class break into two teams and gave each of them a Lego Mindstorm NXT kit and an hour (another link here). The assignment was to “design, build, and program a robot that could traverse 32″ and then stop just before the obstacle.” (This is a classic, and continually revisitable, robotics program – a combination of “hello world” and a sorting algorithm. There are a million ways to have a robot measure/detect/sense/calculate the distance it has traveled with various tradeoffs around accuracy, amount of code, use of resources, speed, etc.) The winner would be whichever person’s robot got closest to the goal. (In the case of a tie they would look at business plans. This course didn’t teach the immutable law of marketing that quality and performance just don’t matter, apparently.)

The two groups further subdivided themselves into teams: the business crew which figured out a model for selling the robot; the programming crew which learned how to program the thing; the “alogrithm” group addressed the problem of how to measure 32 inches; and a fourth group that attempted to spy and prevent spying(!). Both groups built their robots successfully and the difference in performance was one millimeter.

It’s important to point out, and this is the point of the column, that these people were not technical. They weren’t programmers. They learned the NXT language and interface on-the-fly and then applied that knowledge to the solution of their problem. They focused their time mostly on solving the problem (creating an algorithm for moving the distance, essentially designing the product), implementing it (figuring out the production and engineering), and debugging and trying additional ideas (optimizing). Valuable modes of learning both for individuals and teams, enough technology to open people’s eyes to some of the complexities of tech development (but not so much as to kill the exercise).

Most important, though, it was a real-life problem to solve. Lots of team building exercises tend to focus on hypothetical situations into which you can throw hypothetical answers. The intangibility of the assignment forces us to say the process is what matters, not the outcome. But, really the process can suck too — one person can do it all so it looks good when you present back to the group, the conversations can be blue sky with no grounding, etc. This exercise forces people to think analytically, solve a problem, communicate, and really, really work together.

Kind of an adult version of another President Nerd charge featured at Maker Faire:

Psyche! I finally made it to Maker Faire and it was every bit as fun, interesting, and inspiring as I hoped. It was big and massive with welded giants of art and smashery. It was cool and witty with installations that made you laugh and wonder how the hell they did it. It was people-focused, having a large number of things that required no power or revived old skills (from vaudeville to composting to a lotta lotta Victoriana). Most of all, though, it was smart and, I hate to use the word, empowering. Everything had wit and intelligence and everything was comprehensible with a little help from the presenters, who were psyched to explain what they were doing.

My favorite, and I kept going back over and over again, were the soldering areas. Both the MAKERShed (MAKE Magazine’s store at the Faire) and Sparkfun (my favorite purveyor of fine electronic goods) had large tables set up with soldering stations where people could take the kits they had just bought and put them together with the help of staff.

These tables were never less than half full and it looked like there was always a mix of adults/kids, noobs/pros, male/female (though the females were predominantly adult). Sparkfun and Make both did a nice job of putting out projects that were doable, but not simplistic. Some kits let you solder two wires coming out of a battery pack to a thing that’s already running. While you learn to make a decent connection, and you’re not likely to fry any parts, you don’t really learn much and it’s not all that energizing. These kits, involved matching resistors, getting polarities right, and required some precision. I love the intensity on everyone’s faces.

The only disappointment is that there wasn’t much around renewable, social, or eco-preneurial. The DIY ethos was strong — make rather than buy, fix rather than replace — but it seemed like they could have dialed some of that up more, without being over-earnest or taking the fun out of it. Example: they had several playground toys designed by MAKErs. They were fun, looked cool, and had some interesting story to them — one was bicycle powered, one worked like a swing and was powered by leaning and leaning back. It would have been cool, given the theme, to see some of the playground toys that generate electricity or pump water.

Still, it was awesomely fun. I bought my second arduino kit and I’m converting space into a little work area and unpacking my soldering iron and box of switches, pots, leds, resistors, caps, transistors, etc and getting back to work. My first goal is to work with the Peggy:

It’s a board that allows you to address a 25*25 grid of multi-color LEDs. Loads of possibilities, especially if they’re connected and working in synch.

More pictures and vids and more to be added to a set on my flickrstream.

One of the funnest things about working in interactive is the tea-leaf reading that happens when screenshots of upcoming apps are released. Windows Longhorn, the annual Macworld run-up, console releases, game beta screens are fodder for endless speculation, geek-talk, and fantasies of what the new app might be.

Twitter is all a-twitter about Wave today, and normally I would be skeptical, but something in Google’s ability to help me get excited about something in a single page with less than 500 words and a couple unremarkable, unpretty, completely un-Apple-shined screenshots makes me hopeful.

From the site:

What is a wave?

A wave is equal parts conversation and document. People can communicate and work together with richly formatted text, photos, videos, maps, and more.

A wave is shared. Any participant can reply anywhere in the message, edit the content and add participants at any point in the process. Then playback lets anyone rewind the wave to see who said what and when.

A wave is live. With live transmission as you type, participants on a wave can have faster conversations, see edits and interact with extensions in real-time.

Frankly the screenshots add almost nothing to the page. The words somehow reflect not only the promise of what they’re offering, but an understanding of the problems that needed to be solved for this to work and the open source ethos of “pick one thing and do it well . . . then do another” which usually result in elegant solutions.

I’ve experimented with a fair number of “collaboration tools” — 37Signals stuff, MSFT Windows Live, Google sites — and am currently struggling with a whole bunch of file sharing/cloud concurrency issues. I’m dying to have a place where I can share pictures and documents with coworkers with easy commenting, versioning, and non-networked-but-secured accesss. The beauty of Google sites was that it allowed people to write HTML in pages that were open to self-organization without the constraints of content management/versioning controls and it was in the cloud. But, you still had trouble with versioning when people got lazy.

If Google gets the rewind action right (presumably that’s what’s Wave-y about it), you’ve got version control built in, along with conversation tracking and a dynamic de-archiving process that doesn’t depend on search (which sucks even on Google apps). The beauty and the hope is that playback and rewind and wave implies a product based on a concept firmly adhered to rather than a laundry list of features that people would like to string together. (Something 37Signals did nicely with Basecamp.)

The real-time collaboration piece is also intriguing, surprisingly so, since we’ve heard that phrase 100 times — “see the changes as they’re made in real time!” (Wonder: how many people understand the phrase ‘real time’ . . . do they actually know what un-real-time is?) Google, however, may have the potential to beat the WebEx client in performance and simplicity if they nail the browser code (I’m starting to pendulum back to a notion that software needs to get back to the browser (even chromeless browsers) for speed, interoperability, and true cloudness).

Weird. I’m not sure I was even this excited about a software launch when Wrath of the Lich King betaed.

50 hours, a couple rules, an understanding of emergent systems, and a delicate design touch:

I’m a little late to this . . . The NYT has been creating a developer network and slowly opening APIs. Last week, they opened an API to the Federal Election Commission (FEC) database.

When we first started talking about creating and releasing APIs for databases collected by The Times, campaign finance data from the Federal Election Commission was a natural choice. The upcoming presidential election has seen record fund-raising by the candidates and a host of new donors. Now we want our users to be able to analyze and reuse some of the data we’ve been looking at while reporting on the campaign.

Stephen Fry is an amazingly erudite, charming, and just fun writer about technology. This is one of the best, or at least freshest, looks at what open source means. It avoids the stridency of some open source advocates, dodges the now nearly-dead “not free like beer”, and has an interesting comparison of operating systems to plumbing.

The credits are fun too, almost all of the images are sourced at flickr.

He’s also got a fun column about the Wii.