Summary of Automating the Search For Artificial Life with Foundation Models, by Akarsh Kumar and Chris Lu and Louis Kirsch and Yujin Tang and Kenneth O. Stanley and Phillip Isola and David Ha
Automating the Search for Artificial Life with Foundation Models
by Akarsh Kumar, Chris Lu, Louis Kirsch, Yujin Tang, Kenneth O. Stanley, Phillip Isola, David Ha
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach called Automated Search for Artificial Life (ASAL) that leverages foundation models (FMs) to explore large combinatorial spaces in the field of Artificial Life. Specifically, ASAL uses vision-language FMs to find simulations that produce target phenomena, discover temporally open-ended novelty, and illuminate diverse simulation configurations. The approach is demonstrated across various ALife substrates, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata, leading to the discovery of new lifeforms and quantification of previously qualitative phenomena. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called foundation models to help scientists discover new and interesting things in a field called Artificial Life. Right now, scientists have to do most of their work by hand, which can be slow and time-consuming. The computer program, called Automated Search for Artificial Life (ASAL), helps scientists find new and exciting things by using the computers to search through all the possible combinations of ideas and see what works best. |
