Automating Artificial Life Discovery: The Power of Foundation Models

The recent Nobel Prize for groundbreaking advancements in protein discovery underscores the transformative potential of foundation models (FMs) in exploring vast combinatorial spaces. These models are poised to revolutionize numerous scientific disciplines, yet the field of Artificial Life (ALife) has been slow to adopt them. This gap presents a unique opportunity to overcome the traditional reliance on manual design and trial-and-error methods for uncovering lifelike simulation configurations.

In a new paper Automating the Search for Artificial Life with Foundation Models, a research team from MIT, Sakana AI, OpenAI, The Swiss AI Lab IDSIA and Independent introduces Automated Search for Artificial Life (ASAL). This novel framework leverages vision-language FMs to automate and enhance the discovery process in ALife research.

ASAL demonstrates its potential across various ALife substrates, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata…