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Time Machine :: Human Camera :: Watson

Time Machine

I'm trying to restore my Apple Time Machine - after a hard disk upgrade my time machine backup has choked on the extra large data set.  It gets quite complicated but in the end, the machine can not reconcile that the new data is just a continuation of the old data and it should just piece it all together in one continuous flow of information.  At last the machine breaks down.  It can not see the forest for the trees.  It has no big picture overview of the scene.

This same day my father-in-law sends me this YouTube video of the "Human Camera"

Beautiful Minds: Stephen Wiltshire 

Watch the video and be awe struck at what a human mind can do. This is a power and skill we all have - but very few have ever nurtured this ability. Stephen can connect both the detail and the big picture and flow all that data into the information to recreate the image.  Many artist can do this same feat, they have practiced endless hours to master the skills.
Here's one that I went to school with:  Mark Stephenson Portraiture and Paintings.


Just recently IBM's Watson computer bested a human on Jeopardy (quiz show).  Take a look at Watson's technical specs - do you think you will have that kind of power in your pocket in 30 years?  In 1980 did you think you would have a cell phone and a search engine (Google) in your pocket (see Cell Phone time line) with video (FaceTime) calling?

The Meta Problem?

But why - oh why - do we still compare the human mind to a computer?  Compare what one of the best humans can do in their domain (Stephen Wiltshire) to what a computer can do in their domain (Watson).  Which is solving the harder problem?  Would Watson pass the Turing Test?  Can a human prove that it is not a machine - in other words does the subject have the power of patten matching at a high level - CAPTCHA?
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David's notes on "Drive"

- "The Surprising Truth about what Motivates Us" by Dan Pink.

Amazon book order
What I notice first and really like is the subtle implication in the shadow of the "i" in Drive is a person taking one step in a running motion.  This brings to mind the old saying - "there is no I in TEAM".  There is however a ME in TEAM, and there is an I in DRIVE.  And when one talks about motivating a team or an individual - it all starts with - what's in it for me.


Pink starts with an early experiment with monkeys on problem solving.  Seems the monkeys were much better problem solver's than the scientist thought they should be.  This 1949 experiment is explained as the early understanding of motivation.  At the time there were two main drivers of motivation:  biological & external influences.  Harry F. Harlow defines the third drive in a novel theory:  "The performance of the task provided intrinsic reward" (p 3).  This is Dan Pink's M…

What is your Engagement Model?

What must an Agile Transformation initiative have to be reasonably assured of success?

We "change agents" or Agilist, or Organizational Development peeps, or Trouble Makers, or Agile Coaches have been at this for nearly two decades now... one would think we have some idea of the prerequisites for one of these Transformations to actually occur.  Wonder if eight Agile Coaches in a group could come up with ONE list of necessary and sufficient conditions - an interesting experiment.  Will that list contain an "engagement model"?  I venture to assert that it will not.  When asked very few Agile Coaches, thought leaders, and change agents mention much about employee engagement in their plans, models, and "frameworks".  Stop and ask yourselves ... why?

Now good Organizational Development peeps know this is crucial, so I purposely omitted them from that list to query.

One, central very important aspect of your Agile Transformation will be your Engagement model.  

Exercise:: Definition of Ready & Done

Assuming you are on a Scrum/Agile software development team, then one of the first 'working agreements' you have created with your team is a 'Definition of Done' - right?

Oh - you don't have a definition of what aspects a user story that is done will exhibit. Well then, you need to create a list of attributes of a done story. One way to do this would be to Google 'definition of done' ... here let me do that for you: Then you could just use someone else's definition - there DONE!

But that would be cheating -- right? It is not the artifact - the list of done criteria, that is important for your team - it is the act of doing it for themselves, it is that shared understanding of having a debate over some of the gray areas that create a true working agreement. If some of the team believes that a story being done means that there can be no bugs found in the code - but some believe that there can be some minor issues - well, …

Refactoring - examples from the book

Martin Fowler's book Refactoring:  Improving the Design of Existing Code has a simple example of a movie rental domain model, which he refactors from a less than ideal object-oriented design to a more robust OO design. Included in this Zip file are the Java source code files of the Movie, Rental, and Customer classes. Along with a JUnit CustomerTest class. Using these example source files you too can follow along with the refactoring that Fowler presents in the first few chapters of his book.

Metrics for a Scrum Team (examples)

What metrics do you collect to analyze your scrum team?

We live in a world of data and information.  Some people have a mindset that numbers will diagnose all problems – “just show me the data.”  Therefore many directors and senior managers wish to see some list of metrics that should indicate the productivity and efficiency of the Scrum team.  I personally believe this is something that can be felt, that human intuition is much better in this decision realm than the data that can be collected.  However, one would have to actually spend time and carefully observe the team in action to get this powerful connection to the energy in a high-performing team space.  Few leaders are willing to take this time, they delegate this information synthesis task to managers via the typical report/dashboard request.  Therefore we are asked to collect data, to condense this data into information, all while ignoring the intangible obvious signals (read Honest Signals by Sandy Pentland of MIT).
What if …