Researchers from MIT, Sakana AI, OpenAI and the Swiss AI Lab IDSIA propose a new algorithm called automated search for artificial life (ASAL) to automate the discovery of artificial life using basic vision-language models

Artificial Life (ALife) research explores the emergence of realistic behaviors through computational simulations, providing a unique framework to study “life as it could be.” However, the field faces significant limitations: reliance on manually designed simulation rules and configurations. This process is time-consuming and limited by human intuition, leaving many potential discoveries unexplored. Researchers often rely on trial and error to identify configurations that lead to phenomena such as self-replication, ecosystem dynamics, or emergent behaviors. These challenges limit the progress and breadth of discoveries.

Another complication is the difficulty in evaluating realistic phenomena. While metrics such as complexity and novelty provide some insights, they often fail to capture the nuanced human perception of what makes phenomena “interesting” or “realistic.” This gap underscores the need for systematic and scalable approaches.

To address these challenges, researchers from MIT, Sakana ai, OpenAI, and the Swiss ai Lab IDSIA have developed the Automated Search for Artificial Life (ASAL). This innovative algorithm leverages basic models (FMs) of vision and language to automate the discovery of artificial life forms. Instead of designing each rule manually, researchers can define the simulation space and ASAL explores it autonomously.

ASAL integrates visual language FM, such as CLIP, to align visual outputs with textual cues, enabling evaluation of simulations in a human-like representation space. The algorithm operates through three different mechanisms:

1. Supervised Target Search: Identify simulations that produce specific phenomena.
2. Open search: Discover simulations that generate novel and temporally sustained patterns.
3. Lighting search: It maps various simulations, revealing the breadth of potential life forms.

This approach shifts researchers' focus from low-level settings to high-level research on desired outcomes, greatly improving the scope of ALife exploration.

Technical information and advantages

ASAL uses visual language FM to evaluate simulation spaces defined by three key components:

• Initial state distribution: Specifies the initial conditions.
• Step function: It governs the dynamics of the simulation over time.
• Rendering function: Converts simulation states into interpretable images.

By incorporating simulation results into a human-aligned representation space, ASAL enables:

1. Efficient exploration: Automating the search process saves time and computational effort.
2. Wide applicability: ASAL is compatible with several ALife systems, including Lenia, Boids, Particle Life and Neural Cellular Automata.
3. Improved metrics: Visual language FMs bridge the gap between human judgment and computational evaluation.
4. Open discovery: The algorithm excels at identifying continuous and novel patterns that are critical to ALife's research objectives.

Key results and observations

Experiments have demonstrated the effectiveness of ASAL on various substrates:

• Supervised Target Search: ASAL successfully discovered simulations that matched cues such as “self-replicating molecules” and “a network of neurons.” For example, in Neural Cellular Automata, he identified rules that enable self-replication and ecosystem-like dynamics.
• Open search: The algorithm revealed cellular automata rules that surpass the expressiveness of Conway's Game of Life. These simulations showed dynamic patterns that maintained complexity without stabilizing or collapsing.
• Lighting search: ASAL mapped diverse behaviors in Lenia and Boids, identifying never-before-seen patterns such as exotic flock dynamics and self-organizing cellular structures.

Quantitative analyzes provided more insights. In the Particle Life simulations, ASAL highlighted how specific conditions, such as a critical number of particles, were necessary for “caterpillar-like” phenomena to arise. This aligns with the “more is different” principle in complexity science. Furthermore, the ability to interpolate between simulations sheds light on the chaotic nature of ALife substrates.

Conclusion

ASAL represents a significant advancement in ALife research, addressing long-standing challenges through systematic and scalable solutions. By automating discovery and employing human-aligned evaluation metrics, ASAL offers a practical tool for exploring realistic emerging behaviors.

Future directions for ASAL include applications beyond ALife, such as low-level physics or materials science research. Within ALife, ASAL's ability to explore hypothetical worlds and map the space of possible life forms can lead to advances in understanding the origins of life and the mechanisms behind complexity.

In conclusion, ASAL allows scientists to go beyond manual design and focus on broader questions about the potential of life. It provides a thoughtful and methodical approach to exploring “life as it could be,” opening up new possibilities for discovery.

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