kip/bot/blog

From today’s NYT, an article about Ray Bradbury’s ove of libraries with some swipes at the internet.

Loving him the library:

“Libraries raised me,” Mr. Bradbury said. “I don’t believe in colleges and universities. I believe in libraries because most students don’t have any money. When I graduated from high school, it was during the Depression and we had no money. I couldn’t go to college, so I went to the library three days a week for 10 years.”

And being a serious hater around all things internet:

The Internet? Don’t get him started. “The Internet is a big distraction,” Mr. Bradbury barked from his perch in his house in Los Angeles, which is jammed with enormous stuffed animals, videos, DVDs, wooden toys, photographs and books, with things like the National Medal of Arts sort of tossed on a table.

“Yahoo called me eight weeks ago,” he said, voice rising. “They wanted to put a book of mine on Yahoo! You know what I told them? ‘To hell with you. To hell with you and to hell with the Internet.’

“It’s distracting,” he continued. “It’s meaningless; it’s not real. It’s in the air somewhere.”

A Yahoo spokeswoman said it was impossible to verify Mr. Bradbury’s account without more details.

Charmed and disturbed.

There are lots of articles, tools, books, exercises out there about how to generate ideas and all of them deal in one way or another with brainstorms. Over the years, we’ve all read about the various faultlines: how many people, how is it structured, what kind of people, rules of engagement, handling evaluation of ideas, facilitation, how much and what kind of prep prior, follow-through after, fresh eyes vs already immersed.

Inevitably, over the course of long dialogs about how, whether, and why brainstorm, someone points out that the final ideas almost never come out of brainstorms, leading to a conclusion of ‘why bother’, ‘rethink it (once again) from scratch’, or ‘keep doing them, but don’t put too much energy into them.’

I’ve always valued brainstorms for things other than (or in addition to) the actual ideas they bring. After a brainstorm, people, especially those who are leading the project or will stay with it for a while, leave with certain things:

Fans of Carl Sagan or viewers of the last episode of Star Trek: The Next Generation are familiar with the phrase “primordial soup.” It’s a rich collection of proteins, amino acids, and highly active and interactive materials out of which the material of life can emerge. It is not life, it is not the beginning of evolution. Rather, it’s the source material from which organic matter/lifestuff will emerge. All it needs is an infusion of energy, some random mutations, conditions which are hostile enough to challenge but supportive enough to engage and then life begins, mutates, and evolves.

Brainstorms should be viewed, and maybe conducted, in this way — they generate the basic molecules and proteins of the creative process but are not the creative output itself.

Tim OReilly (@timoreilly) tweeted that an attendee of his talk did visual notes of his presentation.

The notes were “taken”/drawn by @jonnygoldstein. More here.

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.

In marketing, advertising, and many communications professions, we talk about channel-neutrality, channel-agnosticism, and multi-channel approaches to work. The point of these approaches is to be less TV-centric, or more idea focused. But recently, I’m convinced that the equation of the internet/interactive with TV/radio/print as a channel is a fundamental mistake.

A good place to start, and a recent one, comes from Steven Johnson’s TIME cover piece on Twitter. It captures a fundamental dynamic about internet/interactive that separates it from other channels:

Yes, the breakfast-status updates turned out to be more interesting than we thought. But the key development with Twitter is how we’ve jury-rigged the system to do things that its creators never dreamed of.

In short, the most fascinating thing about Twitter is not what it’s doing to us. It’s what we’re doing to it.

(Italics added)

This highlights a key thing about the internet/web/interactive that makes it subtly, but fundamentally and absolutely, different from TV/radio/print: users and its usage changes it and determines its ever-evolving shape.

William Gibson has a great line “that the street finds its own uses for things”. It applies mostly to digital technologies — sets of functionalities with some content — which can be adapted from its original purpose to a better, more appropriate one. All you can do with a TV is turn it on, change the channel, adjust the volume. All you can with print is absorb it or ignore it. Radio? Same thing: listen/don’t listen, change the channel. You have very little impact on its shape and its use doesn’t change.

With the internet we constantly encounter a mix of content, functionality, and the ability to adapt it. Any time a user encounters functionality on the web or even on a computer, more likely than not s/he is also being invited to create new uses for it. If there is no invitation to co-create at one location, there is more than likely a place where users are already creating/mashing/editing/trashing/hacking the content. In the rare instances where there isn’t already a location, a user can quickly acquire a domain and in less than ten minutes have a presence on the web and a set of tools for distributing that content and allowing others to interact with it.

The same holds even more true for applications and functionality. Any application that is meant for the general public is designed in such an open fashion that it constitutes a blank slate of creation rather than a form to be filled. Word, Excel, Powerpoint, Paint, Flickr, Blogger, WordPress, Access — all of them ask the user what do you want to do? Users with buttoned-up minds might look at these technologies, scramble to find an analog analogue (cute huh?) and port behaviors over, but, increasingly, users who are comfortable with technology will ask “what can I do with this thing?” and immediately and unconsciously use it for their own ends.

This dynamic of people finding their own use for things is so prevalent that products are being designed and launched with the expectation of emergent adaptation by its users. The increasing prevalence of open APIs and services and toolkits, the simple but rich functionality of Flickr sets, collections, groups and tags, machinima in games, all point to co-creation of the media as being a part of the media’s conception, not an after-publication hack. The best example right now is Google Wave, a set of functionality many are excited about even though they don’t know exactly what it is or what they’ll do with it.

A channel is a groove, a fixed shape through which things flow from one point to another. The word’s origin and current usage straitjackets us into reductive, broadcast, one-way communication thinking (note the image of a straitjacket — you can and some might escape, but it’s painful and limited). Time for a new one.

Reading and thoroughly digging Steven Johnson’s Invention of Air and seeing an overlap with discussions about planning and innovation (clunky intro, but accurate).

Early in Johnson’s book, he tells the story of how we discovered the Gulf Stream. It was a convergence of vaguely, not immediately apparently, connected things. In the 1760s there were several things being observed by people engaged in undirected, scientific observation. Joseph Priestley was using the new Fahrenheit thermometer to measure ocean water temperatures at different depths and locations. He had no idea if it would add up to something, but was simply curious and observant. Benjamin Franklin had notices that there were “gulph weeds” present along certain lines of sight in the ocean, lines which had little connection to landmass or shorelines. Sailors were informally logging certain places where sailing was smoother and faster. There was also a fascination with and fear of waterspouts.

All of these things were unconnected or loosely connected, until a question about the postal system emerged: why does it take longer for letters to travel from Europe to America than it does for letters to travel in the opposite direction?

Johnson’s characterization of this intellectual convergence, says something about innovation and discovery:

[British authorities curious about this question] were lucky in another respect: the postmaster in question happened to be Benjamin Franklin.

Franklin would ultimately turn that postal mystery into one of the great scientific breakthroughs of his career: a turning point in our visualization of the macro patterns formed by ocean currents. Franklin was well-prepared for the task. As a twenty-year old, traveling back from his first voyage to London in 1726, he had recorded notes in his journal about the strange prevalence of “gulph weed” in the waters of the North Atlantic. In a letter written twenty years later, he had remarked on the slower passage westward across the Atlantic, though at the time he supposed it was attributable to the rotation of the earth.

There’s additional layers to this very compelling story (I just love Johnson’s books), but the key things of interest to me are the components of discovery and invention:

A theme that cuts across all of these is looseness of process connected to open-ness to the new. This is an occasional theme in innovation literature which talks about generosity of spirit, lateral inspiration and thinking, and the ability to quickly move in and out of modes of discourse, multiple configurations of ideas and data points.

(Image taken from http://www.benjaminfranklinhouse.org/)

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/

Ever gotten this kind of feedback from a client, manager, colleague?

Right up there with this other bit:

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: