Archive for November, 2008

For your eyes only

November 29, 2008

Another twitter find: a short article about how custom interfaces make computer clicking faster, easier. The introduction is very snowflake-oriented:

Insert your key in the ignition of a luxury car and the seat and steering wheel will automatically adjust to preprogrammed body proportions. Stroll through the rooms of Bill Gates’ mansion and each room will adjust its lighting, temperature and music to accommodate your personal preference. But open any computer program and you’re largely subject to a design team’s ideas about button sizes, fonts and layouts.

The whole issue of accessibility is of course rich with snowflake effect aspects. I am not sure that I am completely convinced by an approach like that of Supple that tests people for 20 to 90 minutes in order to adapt the user interface. But the general idea is absolutely the right one:

We argue that interfaces should be personalized to better suit the contexts of individual users. Many personalized interfaces are needed because of the myriad of distinct individuals, each with his or her own abilities, preferences, devices and needs. Therefore, traditional manual interface design and engineering will not scale to such a broad range of potential contexts and people. 

If you have additional examples of how and why user interfaces can be snowflaked, then we’d love to hear from you!

No more…

November 27, 2008

From Seth Godin’s Tribes (not really my cup of tea, but a decent audible “read”…)”

No more average products for average people

Seems like the essence of the Snowflake Effect to me…

Exponential Change to the Same End

November 27, 2008

As I write, speak and think more about the Snowflake Effect and the use of mashups as an overarching conceptual model, the more I’m struck by how this is all a new acceleration along a very long standing continuum of human expression, communication, collaboration and learning.

What we now commonly refer to as mashups, which I’ll simply describe as taking small existing bits and pieces and putting them together to create a whole new whole, is a model we’ve been using for almost all time.  Consider for example how this can be a description of creating music, where everyone uses the same existing relatively small set of existing musical notes, chooses some number of these and assembles them in some new way to create a new song.  And how this could similarly describe the act of writing prose or poems by selecting words from a relatively small and finite set of words in the dictionary and assembling these to create new stories, poems and lyrics. 

It is worth noting that in all these cases the “magic”, the creativity, the brilliance is all in a combination of the selection of the pre-existing bits and pieces and the way in which these are assembled to create something new and different.  Maybe it is just me, but I find the simplicity of this to be profound and beautiful.  Best of all perhaps there is still no end in sigh as this model would appear to be  infinitely expandable, sustainable and scalable.

As I’ve been writing and speaking about more and more, the true power of mashups will be realized as we come to understand it as an overarching conceptual model which can be applied to almost anything and not “just” a technology or data application. For example the mashup model can and is being applied to as diverse a set of areas as maps, software, manufactured goods, music, video, people and organizations. 

I’ll be posting and exploring more details on mashups and their role in enabling the Snowflake Effect in future postings here and on Off Course – On Target.  In the interim I’d encourage you to consider how our pursuit of this continuum of human expression is now accelerating with the transition from a text dominated age to an age of rich media that includes visualization, audio, graphics, simulations, models and video. 

To help stimulate some of your thinking and creative juices I can strongly recommend that you read some of Kevin Kelly’s recent perspectives on all this such as his Nov. 21st article in the New York Times “Becoming Screen Literate” and his summary thoughts in his “book in progress” site called The Technium on “Screen Fluency”.  Kevin continues to be an unending source of inspirational and thought provoking ideas and perspectives for me and I think you will find his writing to be VERY much worth your while.

The Snowflaking of Teaching

November 26, 2008

I’ve been pondering this for a long time and would like to finally take it more public to generate more discussion and change.  Hence I’d like to have more of you pondering the Snowflake Effect on teaching and see what we can come up with collectively.  Think about it this way perhaps:

Where will we find enough teachers when we need more teachers than there are learners & every living person is more than one learner?

Some of my thinking and reasoning (boldly assuming I’m capable of either) includes the following:

  • The Snowflaking of learning is about envisioning a time when every person on the planet has multiple “just right” learning experiences every day and more likely every hour.
  • Every living person, and perhaps more than that depending on your beliefs, is a learner.  So we start with all 6.6 billion of us on the planet, and growing exponentially, at least till 2050 or so but more on that later.
  • We have seen the inversion of the teacher:student ratio where we used to assume that every teacher would be surrounded by multiple students and now we understand that in fact every learner is surrounded by multiple teachers.
  • As the supply of things to learn about grows exponentially with new inventions, discoveries, content and people, the demand for learning is growing exponentially as the growth of grows as well.
  • If the vast majority of our learning is informal (typical estimates are about 90%), does the ratio of informal teaching need to match?
  • My use of the word “teacher” in this context is very broad and along the lines of anyone who assists someone else in learning something, gaining a new skill, acquiring new abilities, etc.
  • Teaching in this context does not need to be either in person, nor live and synchronous.  Though it would include these scenarios so too would any captured versions such as writings, audio, video, diagrams, sketches, and any other ways one person helps another to learn, understand, see, do, act.
  • If you “do the math” on this you start with 6.6 Billion and multiply this by these different factors, multiple times.  By whatever calculations I think we end up with a REALLY big number of how many “teachers” we need.  A number even larger than those being thrown around in all the discussion about the current economy where a trillion seems to be the new “one” as it is the starting point or base number we start with.

Well you get the idea and I hope this helps cause some spontaneous cognitive combustion as your great mind ponders these questions.  What are your thoughts on this and where do you see us finding enough teachers to match the demand and need for learning?

I’ll be sharing more of what I’ve come up with in all my pondering and wandering on this topic, can’t way to hear more of yours.

Google Snowflaked

November 21, 2008

Google has personalized snowflaked search results for quite a while. Since yesterday, they also allow you to more be immediately involved in this activity: SearchWiki allows you to ‘make search your own’:

a way for you to customize search by re-ranking, deleting, adding, and commenting on search results

This blog as the number 1 search result for 'snowflake'

This blog as the number 1 search result for

As you can see in the screendump, this blog is now the number 1 result when I search for ‘snowflake’ on google 🙂 That would not be appropriate for everybody, but it works for me… Exactly what snowflaking is all about!

Snowflake Flip

November 21, 2008


Snowflaking the Flip camera

Snowflaking the Flip camera

A nice example of snowflaking a physical object: the Flip camera can be personalized – as the web site says: ‘the options are endless’…

(BTW, If you don’t know what present to give me: I wouldn’t mind getting one of these. You can personalize it for me 😉 Just kidding. I think…)

One gets MUCH Bigger!

November 21, 2008

I continue to find great fascination with the notion that “one is the biggest number” and with the thinking and writing of Kevin Kelly, and I believe that most of you share a similar interest in both as well.

Kevin has been writing and speaking for some time about his observations on the similarity between biology and technology and how as he puts it “technology is evolving to the point where it can be thought of as the 7th kingdom of life.”

When I was recently speaking with Kevin we discovered that we are both often use the “talk to think” model when giving presentations.  Thanks to TED Talks (Technology Entertainment Design) you can watch Kevin as he “thinks out loud” in this TED Talk from last year (Jan 2007) and see an excellent example of the power of inverted thinking, asking interesting questions and looking at things from different perspectives.  I particularly enjoyed how Kevin ponders the question “what does technology want?” and tried to look at it from technology’s view of the world.

These are very thoughtful ideas and Kevin is as prolific as every about them so I can heartily recommend that you spend some of your very valuable time watching this video and/or reading some of his writings on these and related topics such as this version on “The Seventh Kingdom” from his Technium writings.

You can also read Kevin’s views on how the combined networking of technology is creating a singular “computer” and covering the planet with its own “nervous system in his Technium article on “Evidence of a Global SuperOrganism” and his article from July 2008 Wired magazine “The Planetary Computer” where he comments:

“I suspect, but cannot prove, the seeds of progress lie not in increasing numbers of human minds, or artificial minds, or more powerful individual minds, but in the emergence of a more complex group mind, made of fewer humans, many more machines, and a new way of thinking.”

For me, this perspective on technology and our relationship with it are all part of the “perfect storm” that is emerging and enabling the Snowflake Effect to not only be possible but probable.  After you’ve spent some time considering these points of view please let me know your reactions and if you too see a future predominated by a snowstorm of mass personalization and design for uniqueness.

Snowflakemobile! LEGO Block Cars?

November 18, 2008

I’ve been following the story behind a new car being developed by Tata Motors in India called the Nano.  It is one of those stories that you follow with equal parts fascination and fear, and it is very much worth following whatever your reaction.  The short story is that this car is being developed as an alternative to the use of small motorcycles and mopeds for transporting multiple people.  If you’ve traveled to many other countries as I’ve been fortunate enough to, you may have witnessed the same scene that inspired the Nano when you’ve seen three to six people, often a whole family, riding on a single moped as they dart and weave their way through traffic on their way to work, school and home. 

I’ll leave you to read more about the car and the story behind it as a quick search will turn up plenty.  The July 2008 Wired magazine has an article for example called “The $3000, 33-Hoprsepower, Snap-Together ride to the Future” that will provide you with a good overview and insight.  Basic specs for the four door version include:

  • about 10 feet long and 5 feet wide.
  • 623cc two-cylinder 33 HP rear engine
  • capable of 65 miles an hour
  • projected cost new, 120,000 rupees, including road tax and delivery in India, = ~ $2500-3000

As interesting and scary as the whole concept of providing four wheels for the masses of the world is, what has caught my attention of late is the focus on cost  and other reductions which they are taking to a whole new level.  For example they are looking into reducing shipping volume and costs by shipping the cars in a snap together kit form which would be assembled at the destination.  Right now this is very UNsnowflake like in that these cars are in many ways the epitome of mass production and sameness.  However as they develop this LEGO block approach to car manufacturing and start to design for snap together modularity, it is easy to imagine how quickly this would morph into a mashup model that would enable each person to quite literally design their own car, have it shipped to them and and assemble their own snowflakemobile.

Want to try your hand at designing your own Nano?  Head over to this “design your own Nano” site to get an idea how this might work when the choices were much more in number and detail so you could truly create your own Snowflakemobile!


November 17, 2008

As announced on the TWIST site:

With excuses for the delay in posting this: TWIST session number 3, in which Wayne and I discuss All Things Snowflake.


$1 Million Snowflake Prize

November 15, 2008

When explaining showing example of how The Snowflake Effect is already at work I often use Netflix as an example.  This DVD movie subscription service has been a huge hit since it first began by eliminating the need to make the trip to the video store and by eliminating any chance of late fees.  They did this through an ingenious combination of old and new by using the postal service to mail DVD’s to your home and by having a simple per month subscription fee that entitled you to keep the DVD’s for as long as you liked before mailing them back. 

To get started you went online and created an ordered list or queue of movies you wanted to watch.  Depending on which subscription level you chose you could have 1-3 DVD’s at a time and so to start they mailed you the first 1-3 DVD’s on your list.  You could keep the DVD’s as long as you like and whenever you were finished you sent them back in pre-paid mailers and they would send the next one in your list to you.

Handy to be sure and the service was a huge success from the very beginning.  However the real value turned out to be a little noticed feature at the time which was the feedback loop that they built into the system.  Each time they received one of the DVD’s you sent back they would send you an Email to confirm that they’d received it and tell you they had sent out the next one on your list.  Then they added the real value item, a simple 5 star rating system asking you to indicate how well you liked the movie you had sent back.  Netflix then took this preference data and used it to create an additional  list of Netflix recommended movies. 

Based on talking to many people who used Netflix, it was typical to pay very little attention to this additional list at first but after some time of using the service they would start to have some  difficulty choosing good movies to add to their list and so they would try some of the ones from the Netflix recommended list.  This would continue for a while and then because you were asked to rate each movie after watching it, people would begin to notice that more and more of the movies they really liked were the ones Netflix had recommended.  Netflix had developed was a movie recommender technology they called CinematchSM and most people found that Cinemax was better than they were at choosing movies they’d love!

To their credit, Netflix soon began to realize that their true and lasting value proposition was NOT delivering DVD’s via the mail or even avoiding late fees.  The real values was in helping people resolve the “paradox of choice”, Netflix lists over 100,000 movie titles, and growing, by helping them consistently choose movies that THEY really loved to watch. .  In fact Netflix as recently struck deals with cable TV and other companies to deliver their movies directly and almost instantly to your home via the internet and so the mailing service will likely soon be a thing of the past.  

In looking for ways to improve on their ability to deliver on this value proposition Netflix began to pay more and more attention to Cinemax and then they got REALLY smart and decided to “crowdsource” the next big improvement in Cinemax by creating a contest they called the “Netflix Prize” which offered one million dollars to the first person or team who could improve Netflix recommendations by 10%.  And therein lies the story I’ve been fascinated to follow since it started back in October 2006.  In February this year (208) Wired magazine had an article “This Psychologist Might Outsmart the Math Brains Competing for the Netflix Prize” wrote up a good account of how the competition had taken off with thousands of entries submitted by everyone from large corporations to research departments to single individuals who were from countries all over the world.  One individual, and the feature of the Wired article identified himself simply and quite accurately as it turns out, as “Just a guy in a garage”.

To help the competitors, Netflix did something which has turned out to be a “prize” in itself to the data mining world at large when they posted what is apparently the largest dataset to ever be published, consisting of 100 million of the preference ratings from Netflix customers.   This enables contestants to write their recommender algorithms that are more and more accurate at recommending movies that users will like.  When competitors submit their latest algorithm, Netflix tests it against a different set of ratings data which they keep secret and the post the results of this testing to the Netflix Prize site.  The competition is still running and you can keep up with the progress of the top contenders on the Netflix Prize Leader board.  As of this writing (Nov.14, 2008) they leading entry is at 9.44% and so while the last 1% of improvement is estimated to be more difficult than the first 9%,, the steady progress would seem to indicate that the prize will soon be awarded.

An additional item of note is that the participants have taken a surprisingly open approach to the competition by openly posting details of their methods and many are analyzing these and building upon them for their own models so there is quite a cyclical improvement happening.  Netflix also took what I thought was a very smart and novel approach in that the winning team retains ownership of the solution they come up with and must license it (non-exclusively) to Netflix. And according to the Wired article;

“The company is already incorporating some of BellKor’s ideas into its own system and in the future may buy code from other contestants, as well.”

For me this is great fun to watch not only for this specific contest but also for an intriguing and replicable way to promote innovation and creativity.  This is proof positive of the extremely tangible value there is in amplifying The Snowflake Effect and moving us further along the continuum towards the end goal of “just right”.