Journal of Advances in Artificial Intelligence and Machine Learning

Journal of Advances in Artificial Intelligence and Machine Learning

Open Access
ISSN: 3069-8316
Review Article

Machines with Artificial Intelligence Cannot Attain Consciousness and Cannot Think: Evidence from Neuroscience

Authors: Richard Ambron.

DOI: 10.33425/3069-8316.1015


Abstract

Artificial intelligence programs have created machines with a remarkable ability to store, integrate, and retrieve information. That they can converse gives them human-like qualities and there is the fear that they will become conscious and threaten humanity. The premise underlying these fears is that consciousness is an attribute of the neuronal circuits in the brain and that once the functions of these circuits are completely understood, they can be mimicked by artificial neural networks and consciousness will emerge. However, this traditional view cannot explain how the electrical activity in the brain creates consciousness or how the brain can store billions of images in memory. Recent evidence indicates that the traditional view is not correct; the brain and consciousness are both functionally and spatially separate. Thus, the brain acquires information from our senses, encoded in action potentials (APs) that induce a long-term potentiation (LTP) in follower cells. The LTP results in the transformation of the information from the APs into EM waves that then communicate that information to consciousness where it creates an awareness of the qualities of the sensation. EM waves contain information about their source that is contained in their frequency, amplitude and phase and since these can vary there is essentially an inexhaustible number of unique waves conveying sensory information. Consequently, consciousness, and other higher mental attributes cannot be attained by replicating circuits in the brain because they are not properties of the brain.

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Citation: Richard Ambron. Machines with Artificial Intelligence Cannot Attain Consciousness and Cannot Think: Evidence from Neuroscience. J Adv Artif Intell Mach Learn. 2026; 2(1). DOI: 10.33425/3069-8316.1015
Editor-in-Chief
Jose Luis Verdegay Galdeano
Jose Luis Verdegay Galdeano
Department of Computer Science and Artificial Intelligence | University of Granada

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