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Perhaps the most ambitious scientific quest in human history is the creationof general artificial intelligence, which roughly means AI that is as smart orsmarter than humans. The dominant approach in the machine learning community isto attempt to discover each of the pieces required for intelligence, with theimplicit assumption that some future group will complete the Herculean task offiguring out how to combine all of those pieces into a complex thinkingmachine. I call this the "manual AI approach". This paper describes anotherexciting path that ultimately may be more successful at producing general AI.It is based on the clear trend in machine learning that hand-designed solutionseventually are replaced by more effective, learned solutions. The idea is tocreate an AI-generating algorithm (AI-GA), which automatically learns how toproduce general AI. Three Pillars are essential for the approach: (1)meta-learning architectures, (2) meta-learning the learning algorithmsthemselves, and (3) generating effective learning environments. I argue thateither approach could produce general AI first, and both are scientificallyworthwhile irrespective of which is the fastest path. Because both arepromising, yet the ML community is currently committed to the manual approach,I argue that our community should increase its research investment in the AI-GAapproach. To encourage such research, I describe promising work in each of theThree 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 isinherently scientifically interesting to understand the conditions in which asimple algorithm can produce general AI (as happened on Earth where Darwinianevolution produced human intelligence), I argue that the pursuit of AI-GAsshould be considered a new grand challenge of computer science research.

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