Authors:
(1) Lenore Blum (lblum@cs.cmu.edu);
(2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) Before we indicate CtmR’s alignment with a number of major theories of consciousness, we remark on CtmR’s potential to serve as a framework for constructing an Artificial General Intelligence (AGI). This is a result of CtmR’s global architecture (kindred to arguments made for global latent workspace by (VanRullen & Kanai, 2021)) and, at the same time, the result of an essential difference between CtmR and Baars’ global workspace. CtmR has no Central Executive. This is a feature, not a bug. It enables CtmR to become an AGI (Blum & Blum, 2023): The competition to get information on stage considers the |weight|ed information submitted by a huge collection of (N) processors. And it does this quickly (log2 N steps). This enables CtmR to engage processors to solve problems, even though CtmR does not know which of its processors might have the interest, expertise, or time to do so. [KM15] Specifically, if CtmR has a problem to solve, meaning that one (or several) of its LTM processors has a problem, the processor can submit the problem into the competition as a highly |weight|ed chunk. If no other chunks are highly |weight|ed, the chunk wins with high probability and becomes globally broadcast to all processors. Processors with the interest, expertise and time to work on the problem will respond with appropriately |weight|ed chunks. In this way, unexpected ideas (from unexpected sources) may turn out to have relevance to solving the problem, and useful collaborations can emerge. [KM15] A Central Executive would have to know which processors had the inclination, expertise, and resources to solve problems as they arise, and figure this out quickly. Baars’ does not say how a Central Executive could do this. [KM15] More generally, we predict that a Central Executive is not needed for consciousness or for general intelligence; indeed, it might prove to be an impediment. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Authors: Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 5 Summary and Conclusions 6 Acknowledgements 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References References 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) Before we indicate CtmR’s alignment with a number of major theories of consciousness, we remark on CtmR’s potential to serve as a framework for constructing an Artificial General Intelligence (AGI). This is a result of CtmR’s global architecture (kindred to arguments made for global latent workspace by (VanRullen & Kanai, 2021)) and, at the same time, the result of an essential difference between CtmR and Baars’ global workspace. CtmR has no Central Executive. This is a feature, not a bug. It enables CtmR to become an AGI (Blum & Blum, 2023): The competition to get information on stage considers the |weight|ed information submitted by a huge collection of (N) processors. And it does this quickly (log2 N steps). This enables CtmR to engage processors to solve problems, even though CtmR does not know which of its processors might have the interest, expertise, or time to do so. [KM15] Specifically, if CtmR has a problem to solve, meaning that one (or several) of its LTM processors has a problem, the processor can submit the problem into the competition as a highly |weight|ed chunk. If no other chunks are highly |weight|ed, the chunk wins with high probability and becomes globally broadcast to all processors. Processors with the interest, expertise and time to work on the problem will respond with appropriately |weight|ed chunks. In this way, unexpected ideas (from unexpected sources) may turn out to have relevance to solving the problem, and useful collaborations can emerge. [KM15] A Central Executive would have to know which processors had the inclination, expertise, and resources to solve problems as they arise, and figure this out quickly. Baars’ does not say how a Central Executive could do this. [KM15] More generally, we predict that a Central Executive is not needed for consciousness or for general intelligence; indeed, it might prove to be an impediment. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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Conscious Turing Machine Robots as a Framework for Artificial General Intelligence

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