Nature has kept evolving for at least 4 billion years on Earth, transforming random interactions into progressively more complex “random but probabilistically directed” interactions that all together create emerging behaviours that in turn increase probability and in a way decrease randomness.
The basic steering force in this evolution can be seen as Darwin said “the survival of the fittest” or as other biologists put it “the success of the most adaptable”. A physicist would probably say “the drive towards lower and more efficient power consumption against the second law of thermodynamics”.
Indeed, if we look at biological systems we see an amazing success in minimising the power requirements, from the flight control system of an insect that selectively activates just what’s needed in a specific moment to the automatic temperature control in termite nests.
Engineers are trying to reduce as much as possible, nowadays, the power budget in their systems and the overall power budget in system aggregation. This latter is much more challenging since the aggregation results from a multitude of systems, each one optimised but those optimisations when aggregated do not necessarily result in an overall optimisation. We need to move from local optimisation to an overall optimisation and that requires that each individual system can evolve and adapt over time. A very big challenge indeed. So why not learn from Nature that had billion of years to perfect strategies and went through billions of missteps eventually coming to good solutions?
This is what many scientists are actually doing. More specifically, this post is originated by having read a news from the National Science Foundation reporting on the work of a team at the Real Power and Intelligence Systems Laboratory at Clemson University.
This is a team of neuroscientists that have decided to approach the problem of controlling the complexity of electrical grids using live neurones grown in a culture dish. By leveraging the ability of neurones to process complex data (and understanding patterns) the neuroscientists hope to create a “smart grid”.
According to Venayagamoorthy, the team lead researcher:
“What we need is a system that can monitor, forecast, plan, learn, make decisions. Ultimately, what we need is a control system that is very brain-like. The brain is one of the most robust computational platforms that exists. As power-systems control becomes more and more complex, it makes sense to look to the brain as a model for how to deal with all of the complexity and the uncertainty that exists”.