Join me in San Diego on November 6 to hear about new trends in how enterprises are leveraging big data for business intelligence.  

With Ebola, time is of the essence, which means that IT is having a big impact.

My summary of this article: eHealth Africa (www.ehealthafrica.com) is using about $14 million in private foundation funds to support emergency operations with information technology services.  Fast communication and instant tracking, enabled by cell phone GPS are used by "contact trackers": Ebola detectives tracing the spread of the disease, using an android app that reduced field-to-database time by 75%.  eHealth also supports lab techs as they enter data into a database so that this information can be gathered rapidly.   eHealth Africa also provides information services, using twitter, facebook, Google Maps, and Google Earth. 

So, telecom, analytics, TMF, big data, AI, and machine learning friends: how can we help?  Is there a way to pool our resources and do some database / mapping work remotely? 

News to me: cities are great microcosms in which to study and implement complex adaptive systems principles.  And Jane Jacobs has analyzed them from that point of view.  Highlights:

- "The phenomenal creativity and prosperity of cities like New York is now understood as a dynamic interaction between web-like networks of individuals who exchange knowledge and information about creative ideas and opportunities."

This explains what I feel in New York (and the Bay Area, as well).  It's a palpable difference from how it feels in Denver, for example.  To do this for yourself, copy my "Starbucks experiment".  Get a cup, sit down, and listen to the conversation around you.  Are they productive ("how can we build this?") or consumptive ("what did you buy?").  Positive or negative?  Energetic or defeated?

And of course the key question is how to generate this productive energy?  My belief: model cities, using the great data sources we have available.  But don't just *measure*, understand the *dynamic* cause-and-effect systems.  Big data alone doesn't do this.  It needs decision intelligence.

Which is where machine learning comes in: it helps us to induce these cause-and-effect links from data.

Another highlight: "Cities perform best economically and environmentally when they feature pervasive human-scale connectivity. "

A great read.

Out of curiosity, I ran this Google Trends report today.  If you're not familiar with Google Trends, it tracks phrases used in searches over the years.  It's an interesting - if imperfect - barometer of global interest in certain topics.

Some interesting findings from this graph:

• Machine Learning is on its way back
• Neural Networks have been dropping in popularity, and have crossed below machine learning in 2012
• Although interest in Data Science and Data Scientists is growing, it is still much smaller than interest in Machine Learning.
• These topics are most popular in India, followed by South Korea, Singapore, Iran, Hong Kong, China, and Taiwan.  The US is not on the top 7 list of countries.
• If we sort by city, the most interest is in Pittsburgh (CMU friends: care to comment?), followed by Cambridge, Bangalore, San Jose, Beijing, Chennai, and Hyderabad.

An interesting aspirational article including an interview with John Smart.  I believe, as the article says (in part) that we'll use digital assistants for decision making long before we hand over decisions in their entirety.  This is the logical next layer on top of social networks, as we move from text (like this) to interactive complex systems Decision Intelligence models that allow us to build, visualize, track, and keep our models up-to-date. 

The study described here is to me one of the most compelling reasons that we need decision intelligence. Think about it: if we didn't have ways to overcome our biases / tendency to overlook certain facts when designing bridges or airplanes, they'd fall down.  It's no surprise that we are also biased and blind in certain ways when it comes to making decisions in a complex environment, especially when they're emotionally and tribally charged as political decisions often are. So of course we need mechanisms (visualization, collaboration, QA, and more) to help here as well.

I had no idea that there was so much literature around systems thinking, especially for children.  This article has a good overview of the genre, plus a list of resources.  Time to connect the dots, I think, from complex systems analysis to decision analysis to Forrester to "Bucky" to the analytics work here in Silicon Valley.  Emergence is...emerging :-)