AGI as Second Being: The Structural-Generative Ontology of Intelligence
- URL: http://arxiv.org/abs/2509.02089v1
- Date: Tue, 02 Sep 2025 08:38:52 GMT
- Title: AGI as Second Being: The Structural-Generative Ontology of Intelligence
- Abstract summary: true intelligence exists only when a system can generate new structures, coordinate them into reasons, and sustain its identity over time.<n>Current AI systems, however broad in function, remain surface simulations because they lack this depth.
- Score: 1.3330466303929975
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial intelligence is often measured by the range of tasks it can perform. Yet wide ability without depth remains only an imitation. This paper proposes a Structural-Generative Ontology of Intelligence: true intelligence exists only when a system can generate new structures, coordinate them into reasons, and sustain its identity over time. These three conditions -- generativity, coordination, and sustaining -- define the depth that underlies real intelligence. Current AI systems, however broad in function, remain surface simulations because they lack this depth. Breadth is not the source of intelligence but the growth that follows from depth. If future systems were to meet these conditions, they would no longer be mere tools, but could be seen as a possible Second Being, standing alongside yet distinct from human existence.
Related papers
- A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws [69.11818285240689]
This study attempts to develop a mathematical approach to formalize emergent intelligence from the perspective of limit theory.<n>We recast emergent intelligence as existence of the limit $lim_N,P,K to infty mathcalE(N,P,K)$, with emergent abilities corresponding to the limiting behavior.
arXiv Detail & Related papers (2026-04-27T04:43:42Z) - Evaluating Intelligence via Trial and Error [59.80426744891971]
We introduce Survival Game as a framework to evaluate intelligence based on the number of failed attempts in a trial-and-error process.<n>When the expectation and variance of failure counts are both finite, it signals the ability to consistently find solutions to new challenges.<n>Our results show that while AI systems achieve the Autonomous Level in simple tasks, they are still far from it in more complex tasks.
arXiv Detail & Related papers (2025-02-26T05:59:45Z) - Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence [0.0]
The pursuit of creating artificial intelligence mirrors our longstanding fascination with understanding our own intelligence.
Recent advances in AI hold promise, but singular approaches often fall short in capturing the essence of intelligence.
This paper explores how fundamental principles from biological computation can guide the design of truly intelligent systems.
arXiv Detail & Related papers (2024-11-22T02:55:39Z) - On a Functional Definition of Intelligence [0.0]
Without an agreed-upon definition of intelligence, asking "is this system intelligent?"" is an untestable question.
Most work on precisely capturing what we mean by "intelligence" has come from the fields of philosophy, psychology, and cognitive science.
We present an argument for a purely functional, black-box definition of intelligence, distinct from how that intelligence is actually achieved.
arXiv Detail & Related papers (2023-12-15T05:46:49Z) - The Generative AI Paradox: "What It Can Create, It May Not Understand" [81.89252713236746]
Recent wave of generative AI has sparked excitement and concern over potentially superhuman levels of artificial intelligence.
At the same time, models still show basic errors in understanding that would not be expected even in non-expert humans.
This presents us with an apparent paradox: how do we reconcile seemingly superhuman capabilities with the persistence of errors that few humans would make?
arXiv Detail & Related papers (2023-10-31T18:07:07Z) - A Survey on Brain-Inspired Deep Learning via Predictive Coding [85.93245078403875]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.<n>PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - The Nature of Intelligence [0.0]
The essence of intelligence commonly represented by both humans and AI is unknown.
We show that the nature of intelligence is a series of mathematically functional processes that minimize system entropy.
This essay should be a starting point for a deeper understanding of the universe and us as human beings.
arXiv Detail & Related papers (2023-07-20T23:11:59Z) - Suffering Toasters -- A New Self-Awareness Test for AI [0.0]
We argue that all current intelligence tests are insufficient to point to the existence or lack of intelligence.
We propose a new approach to test for artificial self-awareness and outline a possible implementation.
arXiv Detail & Related papers (2023-06-29T18:58:01Z) - Defining and Explorting the Intelligence Space [0.0]
This article lays out a cascade of definitions that induces both a nested hierarchy of three levels of intelligence and a wider-ranging space that is built around them and approximations to them.
Within this intelligence space, regions are identified that correspond to both natural -- most particularly, human -- intelligence and artificial intelligence (AI)
These definitions are then exploited in early explorations of four more advanced, and likely more controversial, topics: the singularity, generative AI, ethics, and intellectual property.
arXiv Detail & Related papers (2023-06-10T18:05:16Z) - Understanding Natural Language Understanding Systems. A Critical
Analysis [91.81211519327161]
The development of machines that guillemotlefttalk like usguillemotright, also known as Natural Language Understanding (NLU) systems, is the Holy Grail of Artificial Intelligence (AI)
But never has the trust that we can build guillemotlefttalking machinesguillemotright been stronger than the one engendered by the last generation of NLU systems.
Are we at the dawn of a new era, in which the Grail is finally closer to us?
arXiv Detail & Related papers (2023-03-01T08:32:55Z) - An argument for the impossibility of machine intelligence [0.0]
We define what it is to be an agent (device) that could be the bearer of AI.
We show that the mainstream definitions of intelligence' are too weak even to capture what is involved when we ascribe intelligence to an insect.
We identify the properties that an AI agent would need to possess in order to be the bearer of intelligence by this definition.
arXiv Detail & Related papers (2021-10-20T08:54:48Z) - Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles.
This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing.
The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
arXiv Detail & Related papers (2020-11-30T18:29:25Z) - Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of
AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains.
Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
arXiv Detail & Related papers (2020-08-07T21:00:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.