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AI Must Embrace Specialization via Superhuman Adaptable Intelligence

Judah Goldfeder, Philippe Wyder, Yann LeCun, Ravid Shwartz Ziv

TL;DR

It is argued that AI must embrace specialization, rather than strive for generality, and in its specialization strive for superhuman performance, and introduces Superhuman Adaptable Intelligence (SAI), defined as intelligence that can learn to exceed humans at anything important that the authors can do.

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.

AI Must Embrace Specialization via Superhuman Adaptable Intelligence

TL;DR

It is argued that AI must embrace specialization, rather than strive for generality, and in its specialization strive for superhuman performance, and introduces Superhuman Adaptable Intelligence (SAI), defined as intelligence that can learn to exceed humans at anything important that the authors can do.

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.
Paper Structure (11 sections, 3 figures, 1 table)

This paper contains 11 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Human Intelligence is Specialized
  3. Implications for AI North Star Terminology
  4. A Survey of Definitions
  5. Why Existing Definitions are Insufficient
  6. Why Specialization Wins
  7. Towards Superhuman Adaptable Intelligence
  8. Why Self Supervised Learning
  9. The substrate for fast adaptation
  10. On the importance of diversity
  11. Discussion

Figures (3)

  • Figure 1: A two-dimensional semantic map organizing prominent definitions for AGI and other North Star measures of artificial intelligence, along two axes. The vertical axis represents the source of intelligence, ranging from performance-based capabilities (DO, bottom) to learning and adaptability (LEARN, top). The horizontal axis represents the scope of tasks, from universal/open-ended domains (left) to human-centric and economically-focused domains (right). Definitions cluster into three categories: Adaptive Generalists (teal) emphasize learning efficiency and generalization in open environments; Cognitive Mirrors (violet) focus on replicating human-level cognitive capabilities across broad task domains; Economic Engines (orange) prioritize practical utility and economic value in human-relevant tasks. Superhuman Adaptable Intelligence (SAI) falls into the realm of adaptable AI that can do anything that is important both inside and outside the human realm.
  • Figure 2: Illustration of the task space overlap between the human domain and the AI domain within the universal task space.
  • Figure 3: Illustration of autoregressive model divergence