AI Must Embrace Specialization via Superhuman Adaptable Intelligence
Organizations: Columbia University, New York, NY, USA · Distyl, New York, NY, USA · New York University, New York, NY, USA
Abstract
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.
Figures & tables
| AGI Definition | Failure mode | Explanation |
|---|---|---|
| “Match or exceed the cognitive versatility and proficiency of a well-educated adult.” ( Hendrycks et al., 2025 ) | Not Consistent | Human cognition is not general in any meaningful way. This definition is also unnecessarily narrow |
| “Highly autonomous systems that outperform humans at most economically valuable work .” ( Morris et al., 2024 ) | Not Consistent | The focus here is explicitly on a subset of tasks that are of economic worth. Clearly not General |
| “A system that should be able to do pretty much any cognitive task that humans can do .” — Demis Hassabis (DeepMind CEO) ( Mitchell, 2024 ) | Not Consistent | This definition is not actually general both in its focus on humans, and also in its focus on ”cognitive tasks”, which seems to be to the exclusion of physical tasks like locomotion |
| “We need precise, quantitative definitions and measures of intelligence – in particular human-like general intelligence…. …The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty” ( Chollet, 2019 ) | Not Consistent | Chollet himself admits as much, calling human cognition ’only “general” in a limited sense’, a contradiction of terms |
| “We define AGI as a system that demonstrates broad generality (performing a wide range of tasks) and high performance (matching or exceeding human levels).” ( Morris et al., 2024 ) | Not Feasible | While they acknowledge generality requires exceeding human levels, with a focus on direct performance over adaptation, such a system is not realizable with finite resources. |
| “Intelligence measures an agent’s ability to achieve goals in a wide range of environments .” ( Legg and Hutter, 2007 ) | Not Feasible | Legg and Hutter define the domain of environments as all that are computable. They further emphasize ability over adaptability. Strong ability on such a vast set of tasks is not realizable with finite resources |
Artificial General Intelligence
Artificial General Intelligence studies AI systems that can learn, reason, and act across diverse tasks and environments.
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