Siemens already uses these methods to augment procurement decisions for energy and copper. “Instead of just a single model of the future,” adds Zimmermann, “this method provides a range of different future scenarios to be played out and evaluated.”
How might the science of prediction evolve over the next few years? Clearly, if the past is any guide, we will see a steady progression toward increased accuracy. As Zimmermann points out, not only are SENN models learning more each day, but its creators are learning from the models it generates as they morph into closer and closer representations of reality.
Beyond forecasting energy and raw materials prices, beyond predicting the outputs of wind parks and turbines, SENN offers the potential for virtually limitless numbers of applications. It could help with some of the most challenging, complex and costly decisions of our time, namely those associated with urban and regional investment decisions in areas such as road, air traffic, water, and electrical infrastructures. Indeed, SENN’s potential as a decision support system is already being tested at Siemens to help determine, for instance, the relative long-term advantages of different sites before building a factory.
And beyond that? A different model for our relationship with the future is taking shape in the form of a demonstration SENN Forecast Server now running on Siemens’ intranet. The system is being used to introduce internal customers to SENN’s potential.
Fast forward ten years and we may be downloading SENN apps to monitor, learn from, diagnose, and optimize the functions of our homes, vehicles, businesses, and supply chains. SENN’s future versions may even be able to offer scenarios that support optimized, personalized nutritional, healthcare, educational, and financial paths. Every question, after all, has an answer that lies somewhere in the future.
“The science of prediction,” says Zimmermann, “is a race between the increasing complexity of the real world and our accelerating ability to mathematically represent it by means of information-technology-related capabilities, such as SENN models.”