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