AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence

Clune J
Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which roughly means AI that is as smart or smarter than humans. The dominant approach in the machine learning community is to attempt to discover each of the pieces that might be required for intelligence, with the implicit assumption that at some point in the future some group will complete the Herculean task of figuring out how to combine all of those pieces into an extremely complex machine. I call this the “manual AI approach.” This paper describes another exciting path that ultimately may be more successful at producing general AI. It is based on the clear trend from the history of machine learning that hand-designed solutions eventually are replaced by more effective, learned solutions. The idea is to create an AI-generating algorithm (AI-GA), which itself automatically learns how to produce general AI. Three Pillars are essential for the approach: (1) meta-learning architectures, (2) meta-learning the learning algorithms themselves, and (3) generating effective learning environments. While work has begun on the first two pillars, little has been done on the third. Here I argue that either the manual or AI-GA approach could be the first to lead to general AI, and that both are worthwhile scientific endeavors irrespective of which is the fastest path. Because both approaches are roughly equally promising, and because the machine learning community is mostly committed to the engineered AI approach currently, I argue that our community should shift a substantial amount of its research investment to the AI-GA approach. To encourage such research, I describe promising work in each of the Three Pillars. I also discuss the safety and ethical considerations unique to the AI-GA approach. Because it it may be the fastest path to general AI and because it is inherently scientifically interesting to understand the conditions in which a simple algorithm can produce general AI (as happened on Earth where Darwinian evolution produced human intelligence), I argue that the pursuit of AI-GAs should be considered a new grand challenge of computer science research.
Pub. Info: 
arXiv 1905.10985