AI Must Embrace Specialization via Superhuman Adaptable Intelligence
This addresses the conceptual confusion around AGI for AI researchers and policymakers, offering an alternative framework, but it is incremental as it builds on existing critiques of generality.
The paper critiques the concept of Artificial General Intelligence (AGI) as flawed and proposes Superhuman Adaptable Intelligence (SAI), which focuses on specialization and achieving superhuman performance in important human tasks, aiming to clarify discussions about AI's future.
Everyone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don't seem to agree on its exact definition. One common definition of AGI is an AI that can do everything a human can do, but are humans truly general? In this paper, we address what's wrong with our conception of AGI, and why, even in its most coherent formulation, it is a flawed concept to describe the future of AI. We explore whether the most widely accepted definitions are plausible, useful, and truly general. We argue that AI must embrace specialization, rather than strive for generality, and in its specialization strive for superhuman performance, and introduce Superhuman Adaptable Intelligence (SAI). SAI is defined as intelligence that can learn to exceed humans at anything important that we can do, and that can fill in the skill gaps where humans are incapable. We then lay out how SAI can help hone a discussion around AI that was blurred by an overloaded definition of AGI, and extrapolate the implications of using it as a guide for the future.