Intelligent Devices / Collective Intelligence
Everything that can be networked, will be networked (phones, computers, books, music players, automobiles and even our own health data thrown off from bathroom scales, pedometers and the like ). Each device, once connected, becomes capable of extraordinary things; They will be smarter, they will have memories, they will know who you are and they will have extraordinary powers of prediction. And the more data it connects to – not just from you – but from other devices, the more valuable it gets. In this inevitable future, a connected device is really just software in a very cool package and the more data is has to process, the smarter or more valuable it gets. “Dumb” gadgets become commodities. As the “always-on” world awakens to the need for a continuous stream of compelling content, the data exhaust from sensors embedded in our connected devices becomes a living narrative and a source of high value content. Examples: FitBit, NikePlus etc.
Intelligent devices become distribution channels for higher value content or data.
Think about how the iPad becomes a a distribution platform that allows us to monetize once-free content now that it is “packaged” within an app. The same goes for much of what is taking place on the Android, Apple and Ovi app stores. (see Jim Stogdill’s excellent Radar post on this subject: (http://radar.oreilly.com/2010/04/the-ipad-isnt-a-computer-its-a.html).
As devices get connected, the ecosystems they belong in (the user data and sensor data they collect and utilize, the user experience they connect to across paid, earned, owned media etc.) becomes so complex as to be entirely unmanageable by human beings. The opportunity is to abandon a “central planning” approach and begin programming for collective intelligence, that is coding ecosystems that evolve based on real analysis of how customers are using them (what Web 2.0 has been saying all along). Programming for Collective Intelligence allows us to move away from centralized planning towards a system that course-corrects based on real usage patterns of users and provides dashboard visualization as a sort of cockpit from which to tweak the overall direction (business goals, KPIs) against which the system is designed to optimize. While this approach has been fairly obvious in the management of complex supply chains and massive data processing tasks such as online search, we have seen very little true optimization of customer experience.