Progress in Research on Implementing Machine Consciousness

YAMADA, Kazunori D; BALADRAM, Samy; Lin, Fangzhou

doi:10.4036/iis.2022.r.02

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…Consciousness is also referred to as the existence of the phenomenal world [22,42] and is regarded as the brain's executive control. It is the extraction of higher-level characteristics from input that occurs throughout the process of perceptual hierarchy that constitutes consciousness [43].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The approaches that artificial intelligence and cybernetics take to managing knowledge are distinct. According to the AI point of view, knowledge may be kept on computers and linked to the actual world via its applications [23,24,42,[55][56][57].…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to Farthing, the executive control system of the mind is consciousness [68]. The existence of a phenomenal world inside the human brain is what David Gamez means when he suggests that consciousness exists [22,42]. Following Minsky's theory, the brain interfaces with the body and consciousness [55][56][57].…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Modeling of Quantum Cognitive Perceptual Associative Memory for Conscious Agents: A Parametrized Circuit of Quantum Neural Networks

Mahmood,

Sherwani

2024

Preprint

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Perceptual memory is important for human cognition and learning because it plays an important role in forming consciousness, memories, learning, and stimulus awareness. Perceptual memory assists in the recognition of items or faces, as well as in the sorting, differentiating, and combining of new information with context and emotions. Perceptual-associative memory encodes information structurally, semantically, and phonetically. It interacts with the conscious artifact's medium-term memory (MTM), long-term memory (LTM), workspace, and emotion modules to construct meanings and connections to chosen sensory information. This research developed a quantum cognitive model of perceptual associative memory to replicate human-like perceptual cognitions in conscious artifacts. Moreover, for the quantum neural correlates of consciousness, this research has also simulated the circuit of the quantum neural network for non-linear learning of an XOR gate using the qiskit library.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Modeling of Quantum Cognitive Perceptual Associative Memory for Conscious Agents: A Parametrized Circuit of Quantum Neural Networks

Mahmood,

Sherwani

2024

Preprint

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Biological Mechanisms Contradict AI Consciousness: The Spaces Between the Notes

Miller,

Baluška,

Reber

et al. 2024

BioSystems

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Learning Random Numbers to Realize Appendable Memory System for Artificial Intelligence to Acquire New Knowledge after Deployment

Yamada

2023

Preprint

Self Cite

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Artificial intelligence that accumulates knowledge and presents it to users is likely to become a good friend to humanity. It is thought to be useful for such artificial intelligence to have the ability to continuously accumulate knowledge while operating as artificial intelligence, rather than having to relearn when acquiring new knowledge. Artificial intelligence that can share past knowledge with users is thought to be a human-like existence. Some dialogue agents developed so far can partially use the memory of past conversations. Such memory is sometimes referred to as long-term memory. However, the ability of existing dialogue agents to continue accumulating knowledge and share that knowledge with users is likely not sufficient at present. Within the framework of their current implementation, their memory capacity is at least finite. It is thought necessary to develop a method to improve this memory capacity, in order to realize memory to store any information in a medium of finite size. This might be a kind of research on data compression. Also, in the current method of developing dialogue agents by repetitively using human conversation data as training data, there is some doubt as to whether or not artificial intelligence can effectively utilize the memory they have acquired in the past. In this study, we demonstrate the impossibility. In contrast, this research proposes a method to store various information in a single finite vector data and retrieve it, utilizing a neural network with a structure similar to a recurrent neural network and encoder--decoder network, which we named mnemonist. This study only yielded very preliminary results, but it is fundamentally different from the conventional learning method of neural networks, and is a fundamental consideration for building artificial intelligence that can store any information in finite size data and freely utilize them in the future.

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Progress in Research on Implementing Machine Consciousness

Cited by 3 publications

References 30 publications

Modeling of Quantum Cognitive Perceptual Associative Memory for Conscious Agents: A Parametrized Circuit of Quantum Neural Networks

Modeling of Quantum Cognitive Perceptual Associative Memory for Conscious Agents: A Parametrized Circuit of Quantum Neural Networks

Biological Mechanisms Contradict AI Consciousness: The Spaces Between the Notes

Learning Random Numbers to Realize Appendable Memory System for Artificial Intelligence to Acquire New Knowledge after Deployment

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