Norbert Wiener defined cybernetics in 1948 as “the scientific study of control and communication in the animal and the machine”. The objective of cybernetics is to steer a system towards a reference point. To achieve this, the output of the system is continuously monitored and compared against a reference point. The difference, called the error signal is then applied as feedback to the controller which in turn generates a system input that can direct the system towards the reference point. At times the quantity of interest that is required to compare against the reference can not be measured directly and hence has to be inferred from other quantities that are easier and cheaper to measure. With an increase in computational power and availability of big data, there are two major improvement possible within the cybernetics framework.
- Firstly, the controllers can be improved by increasing its complexity to incorporate more nonlinear physics.
- Secondly, the big data can be utilized for a better estimation of the quantity of interest.
In order to address these two issues a new field of Big Data Cybernetics is proposed. It is an adaptation of the concept first conceived in at the Norwegian Univeristy of Science and Technology. In the figure, the first step is partial interpretation of big data using well understood physics-based models. The uninterpretable observation at this stage is termed interpretable residual and in a second step is modeled using an explainable data-driven approach. After the second step, again an uninterpretable residual remains which is modeled using more complex and black-box models preferably with some inbuilt sanity check mechanism. The remaining residual is generally noise which can be discarded. The three steps result in a better understanding of the data and hence improved models, provided, new approach can be developed to combine physics based modeling and data-driven modeling with big data. The steps are continuously looped with the availability of new streams of data