What Most People Get Wrong About Evolution | Akarsh Kumar

Artificial life research aims to understand intelligence by exploring the space of all possible life forms and physical systems, rather than focusing solely on biological life on Earth. By parameterizing simulation spaces—such as cellular automata or Core War programming games—and utilizing foundation models as automated critics, researchers efficiently navigate complex, non-linear search spaces to identify emergent behaviors. This approach treats evolution as a non-random, path-dependent optimization process that builds regularities sequentially, mirroring how natural systems develop robustness. Integrating large language models into evolutionary loops allows for the discovery of sophisticated, self-organizing patterns that are computationally prohibitive to find through manual design or random search. This methodology shifts the focus from statistical intelligence to regularity-based intelligence, providing a framework to study open-ended creation and the fundamental principles governing complex, self-replicating systems.