Ecosystems often seem chaotic, or at least overwhelming for someone trying to understand them and make predictions for the future. Artificial intelligence and machine learning are able to detect patterns and predict outcomes in ways that often resemble human reasoning. They pave the way to increasingly powerful cooperation between humans and computers. Within AI, evolutionary computation methods replicate in some sense the processes of evolution of species in the natural world. A particular method called symbolic regression allows the evolution of human-interpretable formulas that explain natural laws.

Artificial intelligence and machine learning are able to detect patterns and predict outcomes in ways that often resemble human reasoning. They pave the way to increasingly powerful cooperation between humans and computers. Within AI, evolutionary computation methods replicate in some sense the processes of evolution of species in the natural world. A particular method called symbolic regression allows the evolution of human-interpretable formulas that explain natural laws. "We used symbolic regression to demonstrate that computers are able to derive formulas that represent the way ecosystems or species behave in space and time. These formulas are also easy to understand. They pave the way for general rules in ecology, something that most methods in AI cannot do," says Pedro Cardoso, curator at the Finnish Museum of Natural History, University of Helsinki.

Artificial intelligence and machine learning are able to detect patterns and predict outcomes in ways that often resemble human reasoning.