Machine learning can introduce adaptation into systems and make problems more complex in several ways.
Machine learning algorithms can continuously learn and adapt to changes in the environment, making it possible to address problems in dynamic and evolving systems. However, this adaptation can also make problems more complex, as it can be difficult to understand how the model is making decisions and to predict the consequences of these decisions.
Machine learning algorithms can model non-linear relationships, which can make problems more complex to understand and solve. Non-linear relationships can be difficult to model using traditional methods, and machine learning can introduce new and unexpected behaviors into systems.
Machine learning algorithms can have unintended consequences that are difficult to predict. For example, a machine learning model designed to improve a system may end up making it more complex or introducing new problems. Thai causes humans to react to unpredictable or unreliable machine outputs, thereby increasing complexity.
When machine learning systems are added to engineered systems, the resulting system has a degree of adaptation.
Take an iPhone. Every phone that comes from the factory is exactly the same. Then users download apps and fix their own settings. It becomes impossible to predict all the possible interactions. Add machine learning and now every iPhone is as unique as its user. Engineered systems behave more like organic systems.
Whenever you see a machine learning system, look for complexity. It may be nascent but the more it touches human or social systems, the more likely it is to be part of a complex system.
