1. Dirty Dish Dilemma (for now, linking to a radio interview I gave on this topic)
2. True Mirroring
3. Do-Nothing lucid dreaming and R.E.L.
4. Spontaneous yoga
4. Dynamically Distributed Democracy
5. The practice that liberates

It is a gift we give to one another
in these unsettled times
to gather together under an old tree in Summer
– as we have before
and as we will again,
in different times and temples than this one –
to author and celebrate the marriage of
Thomas Macario
and
Sabrina Rose.

These gatherings of ours
give occasion to the telling of truths:
where we say aloud what we inwardly know but speak of rarely.
Where we give thanks and say “I Love You” to those who made and raised us.
Where we smile at friends and say: “I have known you a long time.”
Where we say, in our own peculiar ways,
what the bee says to the flower,
the sea to the sea shore,
the tree’s leaves to the Sun’s shine.

And in saying these things,
it somehow reminds us of the passing of our lives.

In hearing what is spoken — here, now –
in the image of two people under an old tree,
we witness the wedding together of all the pairs of things:
hurt and its healing,
gift and its gratitude,
the ends and beginnings of a life.

You know, it sounds quite like two people saying ‘Thank You’ to each other.

All this is said, and all of us are gathered in, to remind you:
Love is.
There was never a time when it wasn’t.
All around you.

I used to be a computer scientist. I could respectably program in more than ten languages. I pondered the theoretical limits of computation and ways to overcome or exploit them. I tinkered solutions to arcane problems in artificial intelligence. For a short time, I even searched for the Holy Grail.

You see, there’s a kind of Holy Grail in computer science. No one has been able to write an algorithm that can compute this particularly hard set of problems (known as NP-complete) in a reasonable amount of time. A reasonable amount of time means less than a billion years.

One example is the so-called Traveling Salesman problem. What is the most efficient route a traveling salesman can take to visit all the cities in his sales area, (efficient meaning the least amount of driving time)? You can figure out the optimal route for a handful of cities, but as soon as you get above ten or so, the complexity of the problem grows astronomical. Analogous problems show up for routing traffic on the internet, routing airplanes between airports, coloring world maps, or whenever you go on a run of errands and want to be efficient with your driving time.

The Holy Grail algorithm would make this optimization problem tractable. No one knows if it exists. No one has been able to prove that it doesn’t, though they’ve tried. What we do know is that if a solution is found to any of the NP-complete problems, we’ll be able to solve all of them. That would mean instant fame, fortune and a shiny pedestal in computer history.

I once worked for an eccentric and brilliant Computer Science professor (let’s call him Ben) who was determined, in his own way, to find the Holy Grail algorithm. Ben had a hilarious sense of humor, was a great teacher, and could read Yoga sutras in the original Sanskrit. Working in the field of Artificial Intelligence, Ben had developed ingenious methods for getting computers to learn. He made money on the stock market with an automatic trading AI he’d written. He created a chess playing AI that started out with zero knowledge of the game but learned to play respectable chess after losing (and learning from) thousands and thousands of games. Most chess-playing AIs are pre-programmed with sophisticated models of chess strategy.

Yet despite all his practical successes in Artificial Intelligence solving heuristic problems that demanded merely excellent solutions as opposed to optimal ones, Ben had Holy Grail on the brain. He was obsessed with a particular NP-complete problem known as the 8-puzzle or N-puzzle, the old sliding tile game where you have to slide the scrambled tiles back into order. Ben felt that existing algorithms weren’t making optimal use of past experience – they were ignoring valuable lessons learned in the early stages of problem solving that could more quickly lead to a solution later on. He called it a waste of computation. And he was convinced he could discover a new path to solving the 8-puzzle and thereby find the Grail.

Of all my time spent working in Computer Science, it was probably the series of months I spent working with Ben, searching for the Holy Grail algorithm, that led me to realize it wasn’t the field for me. This is not to say it wasn’t time well-spent. I actually loved the work. It was fascinating and fed the part of me that sought beauty in the mathematical and abstract. I just eventually came to the conclusion that I was more interested in human beings than computers. And of course, I continue to be fascinated by the combination of the two. Still, my time with Ben was illuminating, and I think it’s a story worth sharing.

We would meet once a week or so. In order to gain some new foothold in solving the 8-puzzle – which begins in a random state and ends up in exactly one solution state – I must have dreamt up and coded a million ways to map out its strange, mountainous landscape; to find a way to see the end from wherever you happened to be starting from. It’s sort of like being trapped inside a garden maze with someone calling to you from the exit. You follow the sound of the voice and seem to be making headway, but over and over you come to a dead end that stops just short of the exit. So you turn around and try a different route. In other words, the voice calling to you is not enough. You need to be able to look down a path without walking down it and somehow know if it’s promising or not. If you can figure out a way to do that, you’ve solved NP-complete and found the Holy Grail.

Each meeting with Ben went much the same. I would share what I’d tried that week and point out the flaw I’d discovered in our earlier reasoning. Over the course of an hour, we’d inevitably have a leap of insight into a promising new technique to try out. We were back on the brink of Eureka! As the meeting wound down, the conversation would drift… to chess, metaphysics, Sanskrit, stock market prediction, yoga, or the philosophy of J. Krishnamurti.

One afternoon in his office, we sat talking while chainsaws buzzed gratingly outside Ben’s window. Every ten minutes or so, an enormous redwood tree would come crashing down. They were clearcutting to make room for the brand new Engineering building. As it fell, each tree made the most horrific moaning sound, something like a blue whale dying, not a redwood. I made a remark about the beautiful and sad old trees. Ben turned and looked out the window.

“I know, it’s terrible. What a waste of computation.”

Later that week, I’d be up at night coding our latest wizardry, some little algorithmic sleight-of-hand we’d dreamed up. As always, without fail, another flaw in our reasoning would stare back from the terminal screen in the strange half-light of dawn. Another devil in the details. Another promising path that dead-ended just short of the exit.

The weeks slid by. During the drifting, philosophical epilogues to our meetings, I began to wonder if Ben secretly knew our Holy Grail search was futile. Was he playing a sophisticated joke on me? Was the 8-puzzle merely a kind of algorithmic Zen koan intended to reveal the limits of my own mind, in order for me to let go of it? Was our collaboration an allegory for the futility of striving after rational answers to absurd questions? Was he trying to tell me that life was purposeless?

After precisely one too many of those Holy Grail nights, I found myself at a crossroads. I took a good long look down the path I was about to take – without walking down it. And you know what? It didn’t look promising. A waste of computation, really.

With the advent of the Industrial Revolution, in times of great political struggle, Marx wrote of the need for social change in the form of political revolution. Later on, disciples of the global industrial age preached modernization as the key change process.

In those days, design meant a surface-treatment to functional objects; what we would call stylizing or decorating. Today, design encompasses an entire orientation to the world, what David Kelley calls design-thinking: creativity and the confidence to act on it. Latour echoes this idea.

it would be absurd to distinguish what has been designed from what has been planned, calculated, arrayed, arranged, packed, packaged, defined, projected, tinkered, written down in code, disposed of and so on. From now on, “to design” could mean equally any or all of those verbs.

In my view, design becomes social change when design-thinking becomes cultural, when communities of people learn to see their worlds not as finished products, but as prototypical works-in-progress that invite feedback, tinkering, and continuous improvement.

We need to connect citizens with each other to engage them more fully and directly in solving the problems that face us. We must use all available technologies and methods to open up the federal government, creating a new level of transparency to change the way business is conducted in Washington and giving Americans the chance to participate in government deliberations and decision-making in ways that were not possible only a few years ago.

The deliberation and decision-making processes in the United States are archaic. They were designed in an era when the fastest message traveled at the speed of a horse. Consider that the precedents for presidential and congressional term limits were also set during that era. Four years worth of decision-making in 1798 was probably orders of magnitude less power than four years worth in 2008. The globalized, digitally-connected world moves far swifter than the one our Founding Fathers lived in.

I hope that the idealistic position quoted from Obama’s site above can be reduced to concrete practice in the coming years. Instead of relying on lobbyists to travel to Washington DC and “represent” the public interest with sweet talk and bribery, let us use the Web to create a channel for direct public comment and polling on legislative and executive issues. It would be really quite simple. That’s what Change.gov should really be.

The internet gave us all a voice. President-elect Obama, please open up an ear to listen.

As an experiment, I recently sponsored my own design challenge on Kluster (you’ll need a Kluster account to see it). I offered $50 of my own money and challenged the Kluster community to design a killer location-aware application for the iPhone. The challenge ran for about a week and a half, during which time 47 proposals were made and 68,523 watts (Kluster currency) were invested to determine the best among them.

The main purpose of my experiment was to try and re-create the human-centered design process within Kluster, which, according to my previous post, is difficult if not impossible. Not to be a mere critic, I gave my own best shot and making it happen.

I’d say the results were so-so. While some really good ideas were generated (some even innovative), on the whole, the design process I provided wasn’t strongly adhered to. I created three phases: 1) Needfinding: identifying unfilled user needs based on actual personal experiences, 2) Wild brainstorm: going for a quantity of unconstrained ideas, 3) Final solution: the final brainstorm.
Also, in every phase I stressed the importance of being succinct. I’ve noticed that most Kluster projects are plagued by long-winded proposals that could really be said in a few sentences. This makes for really tedious evaluation of the idea’s quality. I went so far as to require a two sentence limit for phases 1 and 2 — which people promptly ignored.

Lessons learned:

1. You can’t expect wild brainstorms when you title your design challenge with a specific solution in mind (i.e. “location-aware iPhone app”). People basically treated phase 2 and 3 the same.
2. People have a hard time discovering needs based on real personal experience. It’s so much easier to imagine “what if…” or “wouldn’t it be cool if…”, instead of developing insights about the current state of the art.
3. Kluster’s algorithmic selection process, based on an investment market metaphor, doesn’t seem to work very well for the small-scale collaborations happening on the site. (The final winning idea was mediocre, at best.) The only projects that draw a large enough crowd are the ones that offer hundreds of dollars or more as reward. Hardly a platform for democratized design… But potentially a good recipe for well-funded crowdsourcing.

After it was all over I received two interesting messages from people who had participated in my challenge. One asked if I would send him a check for $4.70 (his return on investment for the winning idea). Kluster has yet to tell me how I’m actually supposed to pay out my reward. Symptom of a brand-new startup I suppose.

The other person wanted to know how the final idea had been chosen since its stats seemed to indicate it was lower-rated and had less investment than other proposals. My inquiry to my Kluster contact revealed that the timeline of investment plays a big part in preventing people from gaming the system by investing big right before the challenge ends. In other words, earlier investments carry more weight than last-minute ones. While that makes sense, early investments are also much-less informed: there are fewer ideas to compare it to.

I suggested to Kluster that they put this information in a FAQ so people aren’t investing in the dark. I actually lost several thousand Watts investing last minute like that. That was after I’d won over $100 with some good investments in the earliest Kluster projects. I figured I’d use that money to run two experiments. Now you know about the results of the first. Stay tuned for the second.

This one looks at the material redefinition of information in the digital participatory age.

It goes without saying that I’m excited to see how Kluster fares in this space. Others have come before it — Innocentive, Cambrian House, CrowdSpirit, IdeaBlog — but none of these have impressed me as much. Kluster reads like a potent combination of community technologies for online collaboration — prediction markets, community currency, user-generated content, social filtering — and applies it to an area very close to my heart: design. It’s great to see someone create what looks like a solid platform that targets and incentivizes a co-creative community.

However, I have my doubts.

As a student and practitioner of “West coast-style” human-centered design thinking, (as pioneered in the Stanford design community and practiced by pros at IDEO and countless other firms), I’m going to be watching Kluster to see if the wisdom of the crowds can adequately substitute for deep, insightful human-centered design.

The problem is, the Web is still talk-centered, despite rich multi-media support. And Kluster is no exception, where the engines of creation are more or less Digg-style social filtering of idea proposals and comments. What rises to the top is still what sounds good or looks good, not what is grounded in real, meaningful user need.

Kluster’s participatory design is essentially algorithmic brainstorming. That’s only half the battle, and, in the end, can only lead to half-assed products. Good design starts with ethnographic research methods — needfinding, as designers say — which take time and effort (away from computer screen) talking to real human beings in order to understand their worldviews, their culturally-specific meanings, their unmet needs.

Good design also depends on prototyping — putting physical (or software) mock-ups and models in front of actual users get their feedback, then incorporate that feedback into new design iterations.

In other words, good design doesn’t happen in one fell swoop.

Social filtering model is one-dimensional. Stories, comments, pictures, videos, links, etc. bubble up from ground zero to mass visibility. There’s no room for the chaotic ebb and flow of real design process, where ideas that rise up because they seem good at first — sure-fire winners, even — come crashing down after a reality-check…to rise again, crash, and rise again, re-centered on user need. I believe that real, nuts n’ bolts, collaborative design will not be successfully crowdsourced until these elements are included fundamentally in the prescribed process.

Thomas Maiorana, an accomplished human-centered designer and crowdsourcing maven blogs about how to combine the best of both worlds in his Six Steps to Effective Crowdsourcing. Highly recommended.

Let me be clear: I am a huge believer in the potential for democratized design, and I see Kluster as a wonderful first prototype. But there’s a real danger that lies dormant in this field, especially when combined with the democratization of material manufacturing. When products are designed and manufactured merely on the basis of sounding cool — we’ll end up with even more mountains of landfill than we have already, because no one actually needed the stuff in the first place. Human-centered design — which requires needfinding, prototyping, iteration — is more than a smart design process, it embodies an ethics of sustainable design.