124 citations · 384 across the 16 of their papers we have counts for
12 papers · 1 filter
Language Model Crossover: Variation through Few-Shot Prompting
Elliot Meyerson, Mark J. Nelson, Herbie Bradley +4
This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models o…
Evolution through Large Models
Joel Lehman, Jonathan Gordon, Shawn Jain +3
This paper pursues the insight that large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic…
Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity
Adrien Ecoffet, Jeff Clune, Joel Lehman
Artificial life originated and has long studied the topic of open-ended evolution, which seeks the principles underlying artificial systems that innovate continually, inspired by b…
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang, Joel Lehman, Aditya Rawal +4
Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate pr…
Evolvability ES: Scalable and Direct Optimization of Evolvability
Alexander Gajewski, Jeff Clune, Kenneth O. Stanley +1
Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge; such evolvability is important because it accelerates evolution and e…
Evolutionary Computation and AI Safety: Research Problems Impeding Routine and Safe Real-world Application of Evolution
Joel Lehman
Recent developments in artificial intelligence and machine learning have spurred interest in the growing field of AI safety, which studies how to prevent human-harming accidents wh…