We all have been in situations where we are faced with different ways of solving a problem that directly impact the time spent and the quality of the outcome.
For instance, imagine you are mining for gold. How do you determine at what depth to search?
Or maybe you want to bake the best brownie possible, then the amount of each ingredient, oven temperature and the baking time are important input features.
From these two examples, we can conceptualize tasks with a clear objective which depends on input features (mining depth or recipe for cake). These kinds of so-called ‘optimisation problems’ are numerous in science and engineering, and it is often of great interest to optimise objectives like reduced time consumption, increased profit or quality.
The ability of optimising problems in a closed loop using algorithms carries great potential in science. The algorithm ‘thinks’ differently than a human experimenter and the robots can work around the clock.
For some optimisations, the Bayesian optimisation approach has been shown to outcompete scientific experts.
But the interesting thing to note here is not the competition. It is how automation and algorithms are starting to augment scientists in well-defined tasks freeing their time for more complex tasks that humans excel at.
