mlsanityData to 7 Oct 2026

On the independence between phenomenal consciousness and computational intelligence

arXiv 2208.02187 ↗First posted 3 Aug 2022cs.AIcited by 3

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Abstract (arXiv)

Consciousness and intelligence are properties commonly understood as dependent by folk psychology and society in general. The term artificial intelligence and the kind of problems that it managed to solve in the recent years has been shown as an argument to establish that machines experience some sort of consciousness. Following the analogy of Russell, if a machine is able to do what a conscious human being does, the likelihood that the machine is conscious increases. However, the social implications of this analogy are catastrophic. Concretely, if rights are given to entities that can solve the kind of problems that a neurotypical person can, does the machine have potentially more rights that a person that has a disability? For example, the autistic syndrome disorder spectrum can make a person unable to solve the kind of problems that a machine solves. We believe that the obvious answer is no, as problem solving does not imply consciousness. Consequently, we will argue in this paper how phenomenal consciousness and, at least, computational intelligence are independent and why machines do not possess phenomenal consciousness, although they can potentially develop a higher computational intelligence that human beings. In order to do so, we try to formulate an objective measure of computational intelligence and study how it presents in human beings, animals and machines. Analogously, we study phenomenal consciousness as a dichotomous variable and how it is distributed in humans, animals and machines. As phenomenal consciousness and computational intelligence are independent, this fact has critical implications for society that we also analyze in this work.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 71 references that are in the map. First posted on arXiv 3 Aug 2022.

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6 of 71 references.

References · 6

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021ZeRO-Offload: Democratizing Billion-Scale Model Trainingcited by 339
  3. 2021Acquisition of Chess Knowledge in AlphaZerocited by 120
  4. 2016Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfercited by 30
  5. 2020A Machine Consciousness architecture based on Deep Learning and Gaussian Processescited by 4
  6. 2022Do Artificial Intelligence Systems Understand?cited by 4

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Citing papers per year: 2022: 1; 2023: 2.

3 papers.