The Machine Consciousness Hypothesis states that consciousness is a substrate-free functional property of computational systems capable of second-order perception. I propose a research program to investigate this idea in silico by studying how collective self-models (coherent, self-referential representations) emerge from distributed learning systems embedded within universal self-organizing environments. The theory outlined here starts from the supposition that consciousness is an emergent property of collective intelligence systems undergoing synchronization of prediction through communication. It is not an epiphenomenon of individual modeling but a property of the language that a system evolves to internally describe itself. For a model of base reality, I begin with a minimal but general computational world: a cellular automaton, which exhibits both computational irreducibility and local reducibility. On top of this computational substrate, I introduce a network of local, predictive, representational (neural) models capable of communication and adaptation. I use this layered model to study how collective intelligence gives rise to self-representation as a direct consequence of inter-agent alignment. I suggest that consciousness does not emerge from modeling per se, but from communication. It arises from the noisy, lossy exchange of predictive messages between groups of local observers describing persistent patterns in the underlying computational substrate (base reality). It is through this representational dialogue that a shared model arises, aligning many partial views of the world. The broader goal is to develop empirically testable theories of machine consciousness, by studying how internal self-models may form in distributed systems without centralized control.

This article presents a heuristic view that shows that the inner states of consciousness experienced by every human being have a physical but imaginary hypercomplex basis. The hypercomplex description is necessary because certain processes of consciousness cannot be physically measured in principle, but nevertheless exist. Based on theoretical considerations, it could be possible - as a result of mathematical investigations into a so-called bicomplex algebra - to generate and use hypercomplex system states on machines in a targeted manner. The hypothesis of the existence of hypercomplex system states on machines is already supported by the surprising performance of highly complex AI systems. However, this has yet to be proven. In particular, there is a lack of experimental data that distinguishes such systems from other systems, which is why this question will be addressed in later articles. This paper describes the developed bicomplex algebra and possible applications of these findings to generate hypercomplex energy states on machines. In the literature, such system states are often referred to as machine consciousness. The article uses mathematical considerations to explain how artificial consciousness could be generated and what advantages this would have for such AI systems.

As artificially intelligent systems become more anthropomorphic and pervasive, and their potential impact on humanity more urgent, discussions about the possibility of machine consciousness have significantly intensified, and it is sometimes seen as 'the holy grail'. Many concerns have been voiced about the ramifications of creating an artificial conscious entity. This is compounded by a marked lack of consensus around what constitutes consciousness and by an absence of a universal set of criteria for determining consciousness. By going into depth on the foundations and characteristics of consciousness, we propose five criteria for determining whether a machine is conscious, which can also be applied more generally to any entity. This paper aims to serve as a primer and stepping stone for researchers of consciousness, be they in philosophy, computer science, medicine, or any other field, to further pursue this holy grail of philosophy, neuroscience and artificial intelligence.

Elon Musk has claimed'AI will be smarter than any human by the end of 2025' - and while that is just one year away, an expert said the prediction may still come true. Nell Watson, an AI expert and ethicist, has shared a detailed timeline of how the tech could transform from chatbots to super intelligent agents over the next 12 months. The path would start with a massive 100 billion investment in new computing infrastructure, then AI would learn how to self-improve until it becomes'conscious.' 'Although one year is a short time frame, remember that only 15 months have passed since ChatGPT's breakthrough, which thrust AI into the public consciousness, she told DailyMail.com. 'Developments continue at a frenetic pace since, and even appear to be rapidly accelerating.' Elon Musk has claimed'AI will be smarter than any human by the end of 2025' - and while that is just one year away, an expert said the prediction may still come true Watson, who is the author of'Taming the Machine: Ethically harness the power of AI,' described superhuman AI as systems that far exceed human capabilities across the board.

A Princeton neuroscientist has warned that artificial intelligence-powered chatbots such as ChatGPT are sociopaths without the one thing that makes humans special. In a new essay detailed by The Wall Street Journal, Princeton neuroscientist Michael Graziano explains that AI-powered chatbots are sociopaths without consciousness and that until developers can implement consciousness, they will pose a real danger to humans. For those that don't know, AI chatbots such as ChatGPT are designed to have human-like conversations by remembering what was written by the human earlier in the conversation, providing almost real-time answers and thorough answers to questions. While the dangers of AI aren't so prevalent now, in the future, that could very well change as these sophisticated tools are further upgraded and developed. In order to make them more human-like, Graziano proposes that they are taught human traits such as empathy and prosocial behavior. Notably, the neuroscientist says that these systems will need a form of implemented consciousness to understand these traits and, in turn, adjust their responses to align more with human values.

Let's discuss the various difficulties that need to be overcome in order to create artificial intelligence that can achieve some level of consciousness, let alone a human-like level. Some of the issues include the need to create an AI that can understand and react to the complexities of experiences which stem from the real world, as well as the need to create AI that can understand and replicate the logical workings of the human mind. Sentience is the ability to feel, perceive, or experience subjectively. Machines can currently only experience the world objectively; they cannot feel or perceive subjectively. To create a conscious machine, we need to understand sentience and how to create it.

We report on a series of eight workshops held in the summer of 2017 on the topic "technology and consciousness." The workshops covered many subjects but the overall goal was to assess the possibility of machine consciousness, and its potential implications. In the body of the report, we summarize most of the basic themes that were discussed: the structure and function of the brain, theories of consciousness, explicit attempts to construct conscious machines, detection and measurement of consciousness, possible emergence of a conscious technology, methods for control of such a technology and ethical considerations that might be owed to it. An appendix outlines the topics of each workshop and provides abstracts of the talks delivered. Update: Although this report was published in 2018 and the workshops it is based on were held in 2017, recent events suggest that it is worth bringing forward. In particular, in the Spring of 2022, a Google engineer claimed that LaMDA, one of their "large language models" is sentient or even conscious. This provoked a flurry of commentary in both the scientific and popular press, some of it interesting and insightful, but almost all of it ignorant of the prior consideration given to these topics and the history of research into machine consciousness. Thus, we are making a lightly refreshed version of this report available in the hope that it will provide useful background to the current debate and will enable more informed commentary. Although this material is five years old, its technical points remain valid and up to date, but we have "refreshed" it by adding a few footnotes highlighting recent developments.

Over the last several decades research efforts have explored various forms of artificial life and embodied artificial life as methods for developing autonomous agents. Such approaches, although a part of the AI canon, are rarely used in research aimed at creating artificial general intelligence. This paper explores the prospects of using in silicoartificial evolution to develop machine consciousness, or strong AI. It is possible that artificial evolution and situated self-organizing agents could become viable tools for studying machine consciousness, but there are several issues that must be overcome. One problem is the use of exogenous selection methods to drive artificial evolutionary processes. A second problem relates to agent representation that is inconsistent with the environment in which the agents are situated. These issues limit the potential for open-ended evolution and fine-grained fitting of agents to environment, which are likely to be important for the eventual development of situated artificial consciousness.

Without a doubt, neuroscience holds vast scientific information about human consciousness as researchers over the years have tackled issues such as: how consciousness correlates with neural knowledge, the computational phenomenon achieved through consciousness, the theory of a global workspace, and the model of consciousness postulated by Damasio. Biologically he created a plausible model on consciousnesses as he had assigned all stages of consciousness to specific structures in the brain and associated them with respective functions. Tapping into the bedrock of discoveries made by neuroscience, artificial intelligence hosts many theories on consciousness, obviously from Damasio's Machinist point of view. However, virtually all algorithms investigated to create self-conscious machines have toed the line of a global workspace model of consciousness, which may be likened to a mechanical model. Unfortunately, due to widespread belief among the scientific community that human consciousness will never be simulated on a computer due to the infancy of AI ideas, there's a lackadaisical attitude towards implementing theories in this space.

Renowned researchers Manuel Blum and Lenore Blum have devoted their entire lives to the study of computer science with a particular focus on consciousness. They've authored dozens of papers and taught for decades at prestigious Carnegie Mellon University. And, just recently, they published new research that could serve as a blueprint for developing and demonstrating machine consciousness. That paper, titled "A Theoretical Computer Science Perspective on Consciousness," may only a be a pre-print paper, but even if it crashes and burns at peer-review (it almost surely won't) it'll still hold an incredible distinction in the world of theoretical computer science. The Blum's are joined by a third collaborator, one Avrim Blum, their son.