This study explores how the complexity of rule-based systems affects the capabilities of models trained to predict these rules, specifically focusing on elementary cellular automata (ECA). The research reveals that higher complexity in rules leads to greater intelligence in models as seen in their performance on reasoning and chess move prediction tasks. Uniform, periodic, and highly chaotic systems generally result in poorer performance, indicating a specific level of complexity beneficial for intelligence.
Article by Danny Hillis for Physics Today reprinted with permission, describing Richard Feynman's involvement with the Connection Machine project at Thinking Machines Corporation in the late 1980s. Feynman contributed significantly to the project, including designing an algorithm for computing logarithms and working on the router design.
Cellular Automata Music 1 is a unique and innovative project that combines the principles of cellular automata with music composition.
The project aims to create a new form of music that is both visually and audibly engaging.
The music is generated through a series of rules and patterns that are applied to a grid of cells, resulting in a dynamic and ever-changing composition.
The project is a collaboration between artists, scientists, and musicians, showcasing the potential of interdisciplinary collaboration.
The project aims to create a new form of music that is both visually and audibly engaging.
The music is generated through a series of rules and patterns that are applied to a grid of cells, resulting in a dynamic and ever-changing composition.
The project is a collaboration between artists, scientists, and musicians, showcasing the potential of interdisciplinary collaboration.
