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

TL;DR

This paper proposes AI-generating algorithms (AI-GAs) as a promising alternative to manual AI development for creating general artificial intelligence, emphasizing meta-learning and environment generation.

Contribution

It introduces the concept of AI-GAs, outlines three key pillars for their development, and advocates for increased research investment in this approach as a potential faster path to general AI.

Findings

AI-GAs could be more effective than manual approaches

Three Pillars are essential: meta-learning architectures, meta-learning algorithms, environment generation

AI-GAs may represent a new grand challenge in computer science

Abstract

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 required for intelligence, with the implicit assumption that some future group will complete the Herculean task of figuring out how to combine all of those pieces into a complex thinking 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 in 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 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. I argue that either approach could produce general AI first, and both are scientifically worthwhile irrespective of which is the fastest path. Because both are promising, yet the ML community is currently committed to the manual approach, I argue that our community should increase its research investment in the AI-GA approach. To encourage such research, I describe promising work in each of the Three Pillars. I also discuss AI-GA-specific safety and ethical considerations. 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.