Fig. 4

Conceptual overview of the steps to build a BioVR. A supervised machine learning algorithm is implemented to empirical environment-trait-species datasets in order to extract the patterns (or ‘rules’) of evolution across environments. Meanwhile, the initial settings for the BioVR world and the ancestral AI entity are also set. The settings include physical and environmental conditions, as well as patterns of lifespan, movement, and reproduction of the AI entity. Next, the ‘rules of evolution’ are incorporated into the BioVR and AI entity with original settings, and the BioVR is allowed to evolve. Note that the evolution patterns in the BioVR are then fed-back to the machine learning model, which is updated. This way, the only input from empirical data is at the initial states, and BioVR are allowed to evolve independently afterwards. As a result, we can measure and visualise species evolution as it happens, in an immersive experience of the BioVR