From Data to Mind: A Critical Evaluation of AGI and Machine Consciousness
Nikesh Raj Gopal, Madhav Kumar, Rohan Bhalkar, Harsh Bhogayata, Dishika Kumari, Faruk Abdulla
International Journal of Artificial Intelligence and Machine Learning 2026 DOI: 10.51483/ijaiml.6.12s.2026.1141-1147 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Review Peer reviewed |
|---|---|
| Key findings | Argues that rapid gains in narrow AI capability do not establish a path to genuine general intelligence or machine consciousness, because conceptual and empirical gaps separate current architectures from both goals. The authors contend that the four reviewed theories of consciousness leave machine sentience unresolved and that this uncertainty carries ethical stakes requiring interdisciplinary study. |
Abstract
Artificial General Intelligence (AGI) — a hypothetical system capable of matching or exceeding human cognitive performance across arbitrary domains — remains one of the most consequential and contested goals in computer science. Parallel to this pursuit is the deeper philosophical question of whether such a system could possess genuine subjective experience, or machine consciousness. This paper critically evaluates the feasibility of AGI and machine consciousness by synthesizing evidence from artificial intelligence research, cognitive science, and philosophy of mind. We review dominant technical approaches to AGI — symbolic, connectionist, hybrid, and whole-brain-emulation paradigms — and assess their trajectories against benchmarks of generality, transfer learning, and reasoning. We then examine four influential theories of consciousness — Integrated Information Theory, Global Workspace Theory, Higher-Order Theories, and Predictive Processing — and analyze their implications for machine sentience. Our analysis suggests that while narrow capability gains are accelerating rapidly, the transition from sophisticated data-driven cognition to genuine general intelligence, and further to consciousness, involves conceptual and empirical gaps that current architectures do not resolve. We conclude by outlining the ethical stakes of this uncertainty and proposing directions for interdisciplinary research.