Dissociating Artificial Intelligence from Artificial Consciousness
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Abstract (arXiv)
Developments in machine learning and computing power suggest that artificial general intelligence is within reach. This raises the question of artificial consciousness: if a computer were to be functionally equivalent to a human, being able to do all we do, would it experience sights, sounds, and thoughts, as we do when we are conscious? Answering this question in a principled manner can only be done on the basis of a theory of consciousness that is grounded in phenomenology and that states the necessary and sufficient conditions for any system, evolved or engineered, to support subjective experience. Here we employ Integrated Information Theory (IIT), which provides principled tools to determine whether a system is conscious, to what degree, and the content of its experience. We consider pairs of systems constituted of simple Boolean units, one of which -- a basic stored-program computer -- simulates the other with full functional equivalence. By applying the principles of IIT, we demonstrate that (i) two systems can be functionally equivalent without being phenomenally equivalent, and (ii) that this conclusion is not dependent on the simulated system's function. We further demonstrate that, according to IIT, it is possible for a digital computer to simulate our behavior, possibly even by simulating the neurons in our brain, without replicating our experience. This contrasts sharply with computational functionalism, the thesis that performing computations of the right kind is necessary and sufficient for consciousness.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 18 of its 118 references that are in the map. First posted on arXiv 5 Dec 2024.
Builds on
18 of 118 references.
References · 18
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
- 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 99
- 2024Thousands of AI Authors on the Future of AIcited by 48
- 2024Taking AI Welfare Seriouslycited by 28
- 2021A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machinecited by 22
- 2022Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical termscited by 21
- 2022Annotated History of Modern AI and Deep Learningcited by 20
- 2023The feasibility of artificial consciousness through the lens of neurosciencecited by 18
- 2019Generative models as parsimonious descriptions of sensorimotor loopscited by 11
- 2019Awareness as inference in a higher-order state spacecited by 7
- 2020The Mathematical Structure of Integrated Information Theorycited by 5
6 more
- 2017PyPhi: A toolbox for integrated information theorycited by 4
- 2022System Integrated Informationcited by 2
- 2016Black-boxing and cause-effect powercited by 1
- 2023Measuring the integrated information of a quantum mechanismcited by 1
- 2023Upper bounds for integrated informationcited by 1
- 2024Why does time feel the way it does? Towards a principled account of temporal experiencecited by 1
Built on by
Citing papers per year: 2025: 5; 2026: 2.
7 papers.
- 2025Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Riskscited by 3
- 2025A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousnesscited by 2
- 2025Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysiscited by 1
- 2025Which Consciousness Can Be Artificialized? Local Percept-Perceiver Phenomenon for the Existence of Machine Consciousnesscited by 0
- 2025AI Consciousness and Existential Riskcited by 0
- 2026Toward IIT-Inspired Consciousness in LLMs: A Reward-Based Learning Frameworkcited by 0
- 2026Intrinsic Computational Functionalism and Simulated Consciousnesscited by 0
- 2026Intrinsic Computational Functionalism and Simulated Consciousness13 Jun 2026
- 2026Toward IIT-Inspired Consciousness in LLMs: A Reward-Based Learning Framework30 Jan 2026
- 2025A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness14 Dec 2025
- 2025AI Consciousness and Existential Risk24 Nov 2025
- 2025Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis26 Jun 2025
- 2025Which Consciousness Can Be Artificialized? Local Percept-Perceiver Phenomenon for the Existence of Machine Consciousness22 Jun 2025
- 2025Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks26 May 2025