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A Theory of Intelligences

arXiv 2308.12411 ↗First posted 23 Aug 2023cs.AIcited by 0

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Abstract (arXiv)

Intelligence is a human construct to represent the ability to achieve goals. Given this wide berth, intelligence has been defined countless times, studied in a variety of ways and represented using numerous measures. Understanding intelligence ultimately requires theory and quantification, both of which have proved elusive. I develop a framework -- the Theory of Intelligences (TIS) -- that applies across all systems from physics, to biology, humans and AI. TIS likens intelligence to a calculus, differentiating, correlating and integrating information. Intelligence operates at many levels and scales and TIS distils these into a parsimonious macroscopic framework centered on solving, planning and their optimization to accomplish goals. Notably, intelligence can be expressed in informational units or in units relative to goal difficulty, the latter defined as complexity relative to system (individual or benchmarked) ability. I present general equations for intelligence and its components, and a simple expression for the evolution of intelligence traits. The measures developed here could serve to gauge different facets of intelligence for any step-wise transformation of information. I argue that proxies such as environment, technology, society and collectives are essential to a general theory of intelligence and to possible evolutionary transitions in intelligence, particularly in humans. I conclude with testable predictions of TIS and offer several speculations.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 217 references that are in the map. First posted on arXiv 23 Aug 2023.

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References · 12

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  3. 2020The Next Decade in AI: Four Steps Towards Robust Artificial Intelligencecited by 194
  4. 2014Learning to Optimize via Information-Directed Samplingcited by 168
  5. 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
  6. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  7. 2022The free energy principle made simpler but not too simplecited by 27
  8. 2021Technological Approach to Mind Everywhere (TAME): an experimentally-grounded framework for understanding diverse bodies and mindscited by 25
  9. 2022Designing Ecosystems of Intelligence from First Principlescited by 23
  10. 2013The dynamics of correlated noveltiescited by 7
  11. 2021Thermodynamics of Evolution and the Origin of Lifecited by 7
  12. 2021An active inference model of collective intelligencecited by 3

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