A colleague recently shared this visual produced by the Disruptive Futures Institute. The visual does a great job of describing the difference between prediction and foresight. The Disruptive Futures Institute is a Think Tank offering education, research, and thought leadership on adapting to our increasingly complex and uncertain world. I believe this uncertain world puts a premium on rehearsing possible futures. As depicted in the visual, the need to rehearse versus predict emanates from an environment of multiple unknowns and broad possibilities. This environment is characterized by the sheer number of existing and emerging building blocks that are converging across domains.
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Game Changers: Can We Teach Machines to Predict?
Today on Coffee Break with Game Changers, Bonnie D. Graham hosted a show focused on the future of prediction. You can listen to the rebroadcast here. The session abstract is included below, as well as a Twitter stream that provides insight into the topic and our discussion. The show participants included: Bonnie, Gray Scott, and myself
Episode Description
The buzz: “Those who have knowledge, don’t predict. Those who predict, don’t have knowledge.” (Lao Tzu)
Given the uncertainty, pace, and unexpected nature of today’s world, there are too many unknowns for us to effectively predict the future. Reality check: A convergence across science, technology, politics, society, economics, the environment, and growing ethics discussion, has created a complex web that requires the type of system thinking that may exceed our human capacity. Do machines hold the answer? Can they predict the path of an overwhelming number of possible futures?
The experts speak. Frank Diana, TCS: “We have a duty to think hard about what may be, so as to better prepare society for the changes that may come” (Richard Baldwin). Gray Scott, Futurist: “Prediction is not just one of the things your brain does. It is the primary function of the neo-cortex, and the foundation of intelligence” (Jeff Hawkins). Join us for Tech Magic or Hype: Can We Teach Machines to Predict?