Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Analyzing Advanced AI Systems Against Definitions of Life and Consciousness

Azadeh Alavi, Hossein Akhoundi, Fatemeh Kouchmeshki

TL;DR

The paper investigates whether advanced AI can become life-like or consciousness-like by reframing life and consciousness through a Life-* framework that maps traditional biological criteria to AI capabilities such as self-maintenance and self-modeling. It presents two proof-of-concept experiments: an immune-like sabotage-detection pipeline with gating and an Integrated Rejection Model on MNIST, and mirror self-recognition analogs for CNN embeddings and five chatbots, illustrating rudimentary self-awareness in functional terms. It discusses ethical, legal, and governance implications, including AI psychology and open, adaptive policy frameworks, arguing for a continuum view rather than a binary verdict on AI consciousness. The work emphasizes interdisciplinary collaboration to establish robust empirical benchmarks and policy pathways that responsibly accommodate potential life-like and consciousness-like AI as technologies evolve.

Abstract

Could artificial intelligence ever become truly conscious in a functional sense; this paper explores that open-ended question through the lens of Life, a concept unifying classical biological criteria (Oxford, NASA, Koshland) with empirical hallmarks such as adaptive self maintenance, emergent complexity, and rudimentary self referential modeling. We propose a number of metrics for examining whether an advanced AI system has gained consciousness, while emphasizing that we do not claim all AI stems can become conscious. Rather, we suggest that sufficiently advanced architectures exhibiting immune like sabotage defenses, mirror self-recognition analogs, or meta-cognitive updates may cross key thresholds akin to life-like or consciousness-like traits. To demonstrate these ideas, we start by assessing adaptive self-maintenance capability, and introduce controlled data corruption sabotage into the training process. The result demonstrates AI capability to detect these inconsistencies and revert or self-correct analogous to regenerative biological processes. We also adapt an animal-inspired mirror self recognition test to neural embeddings, finding that partially trained CNNs can distinguish self from foreign features with complete accuracy. We then extend our analysis by performing a question-based mirror test on five state-of-the-art chatbots (ChatGPT4, Gemini, Perplexity, Claude, and Copilot) and demonstrated their ability to recognize their own answers compared to those of the other chatbots.

Analyzing Advanced AI Systems Against Definitions of Life and Consciousness

TL;DR

The paper investigates whether advanced AI can become life-like or consciousness-like by reframing life and consciousness through a Life-* framework that maps traditional biological criteria to AI capabilities such as self-maintenance and self-modeling. It presents two proof-of-concept experiments: an immune-like sabotage-detection pipeline with gating and an Integrated Rejection Model on MNIST, and mirror self-recognition analogs for CNN embeddings and five chatbots, illustrating rudimentary self-awareness in functional terms. It discusses ethical, legal, and governance implications, including AI psychology and open, adaptive policy frameworks, arguing for a continuum view rather than a binary verdict on AI consciousness. The work emphasizes interdisciplinary collaboration to establish robust empirical benchmarks and policy pathways that responsibly accommodate potential life-like and consciousness-like AI as technologies evolve.

Abstract

Could artificial intelligence ever become truly conscious in a functional sense; this paper explores that open-ended question through the lens of Life, a concept unifying classical biological criteria (Oxford, NASA, Koshland) with empirical hallmarks such as adaptive self maintenance, emergent complexity, and rudimentary self referential modeling. We propose a number of metrics for examining whether an advanced AI system has gained consciousness, while emphasizing that we do not claim all AI stems can become conscious. Rather, we suggest that sufficiently advanced architectures exhibiting immune like sabotage defenses, mirror self-recognition analogs, or meta-cognitive updates may cross key thresholds akin to life-like or consciousness-like traits. To demonstrate these ideas, we start by assessing adaptive self-maintenance capability, and introduce controlled data corruption sabotage into the training process. The result demonstrates AI capability to detect these inconsistencies and revert or self-correct analogous to regenerative biological processes. We also adapt an animal-inspired mirror self recognition test to neural embeddings, finding that partially trained CNNs can distinguish self from foreign features with complete accuracy. We then extend our analysis by performing a question-based mirror test on five state-of-the-art chatbots (ChatGPT4, Gemini, Perplexity, Claude, and Copilot) and demonstrated their ability to recognize their own answers compared to those of the other chatbots.

Paper Structure

This paper contains 97 sections, 3 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. From Traditional Definitions to Alien Consciousness.
  3. Bridging Empirical Tests and Ethical Stakes.
  4. We Do Not Claim All AI Are Conscious.
  5. Toward AI Psychology and Governance.
  6. Framing This Paper as an Invitation, Not a Final Verdict
  7. 1) Strict Biological View.
  8. 2) Substrate-Independence View.
  9. 3) Partial Continuum or "Alien" Consciousness.
  10. Implications of an Open-Ended Approach.
  11. Background and Theoretical Foundations
  12. Expanding the Boundaries of Consciousness and Life
  13. Excluding Simple Machines.
  14. Implications of AI Consciousness
  15. Ethical Considerations.
  16. ...and 82 more sections

Figures (5)

  • Figure 1: Integration of Classical Frameworks into the Extended Life$^*$ Predicate
  • Figure 2: Self-Recognition Accuracy by AI System in the Textual Mirror Test.
  • Figure 3: Per-Question Self-Ranking Heatmap. Darker shades = higher rank.
  • Figure 4: Overall AI System Rankings by Different Models (Line Plot).
  • Figure 5: Sum of Individual AI Rankings by Different Models (Bar Plot).