AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence — part 8/8

Cleaned-text code points [106761, 108714). Bibliography omitted here; source.md includes it.

My overarching goal in this essay is not to argue that one path to general AI is likely to be better or faster. Instead, it is to highlight that there is an entirely different path to producing general AI that is rarely discussed. Because research in that path is less well known, I briefly summarized some of the research we and others have done to take steps towards creating AI-GAs. I also want to encourage reflection on (1) which path or paths each of us is committed to and why, (2) the assumptions that underlie each path (3) the reasons why each path might prove faster or slower in the production of general AI, (4) whether society and our community should rebalance our investment in the different paths, and (5) the unique benefits and detriments of each approach, including AI safety and ethics considerations. It is my hope that this essay will improve our collective understanding of the space of possible paths to producing general AI, which is worthwhile for everyone regardless of which path we choose to work on. I also hope this essay highlights that there is a relatively unexplored path that may turn out to be the fastest path in the greatest scientific quest in human history. I find that extremely exciting, and hope to inspire others in the community to join the ranks of those working on it.

## Acknowledgements

My foremost thanks go to Ken Stanley and Joel Lehman, whose lifetime of excellent, creative work greatly informed, influenced, and inspired my thinking on this subject. For helpful discussions on the ideas in this manuscript and/or comments on the manuscript, I thank both of them and Peter Dayan, Zoubin Ghahramani, Ashley Edwards, Felipe Petroski-Such, Vashisht Madhavan, Joost Huizinga, Adrien Ecoffet, Rui Wang, Fritz Obermeyer, Martin Jankowiak, Miles Brundage, Jack Clark, Tegan Maharaj, David Krueger, and all the members of Uber AI Labs. Jeff Clune was supported by an NSF CAREER award (CAREER: 1453549)