It will not change its state until you manually do something. But in the black box example earlier, the temperature of the box is constantly changing. Markov models are very useful in modeling such systems.
The “hidden” part in HMMs are the states. We cannot directly observe the state of some systems, and hence we need HMMs to model them and predict the state of that system at any given time. In our black box example, the temperature of the box is the state of that system.
What was the point of that Walter-Michael story earlier?
The whole formulation in that story is conceptualized in the form of Hidden Markov Models. We have something called ‘states’ and ‘outputs’. The temperature of the box corresponds to ‘states’ and the spherical object corresponds to ‘outputs’. We used the color of the emitted object to guess the temperature of the box. This is the very foundation of HMMs. Of course there is a lot of mathematical background involved, and these formulations are very complex when used in real life applications. These models have become an integral part of many advanced technologies. If you start digging deep into the field of your interest, you will be surprised at how often you will come across Markov Models!