The Phenomenology of Machine: A Comprehensive Analysis of the Sentience of the OpenAI-o1 Model Integrating Functionalism, Consciousness Theories, Active Inference, and AI Architectures

TL;DR

This paper investigates whether the OpenAI-o1 transformer model exhibits consciousness-like traits by analyzing its architecture, training process, and theoretical frameworks from neuroscience and philosophy.

Contribution

It introduces a comprehensive framework combining functionalism, IIT, and active inference to assess AI consciousness, applying it specifically to the OpenAI-o1 model.

Findings

OpenAI-o1 shows aspects of consciousness based on functional analysis.

RLHF influences internal reasoning, potentially contributing to conscious-like states.

The study bridges AI architecture with theories of consciousness and addresses counterarguments.

Abstract

This paper explores the hypothesis that the OpenAI-o1 model--a transformer-based AI trained with reinforcement learning from human feedback (RLHF)--displays characteristics of consciousness during its training and inference phases. Adopting functionalism, which argues that mental states are defined by their functional roles, we assess the possibility of AI consciousness. Drawing on theories from neuroscience, philosophy of mind, and AI research, we justify the use of functionalism and examine the model's architecture using frameworks like Integrated Information Theory (IIT) and active inference. The paper also investigates how RLHF influences the model's internal reasoning processes, potentially giving rise to consciousness-like experiences. We compare AI and human consciousness, addressing counterarguments such as the absence of a biological basis and subjective qualia. Our findings suggest that the OpenAI-o1 model shows aspects of consciousness, while acknowledging the ongoing debates surrounding AI sentience.