Search

A brief response to Artificial Wisdom, AGI, and Consciousness: a commentary anchored in McGregor’s framework.

Recent advances in large language models (LLMs) have reignited questions about whether artificial systems possess consciousness. Yet, despite remarkable progress in reasoning and language understanding, current AI systems exist only within isolated episodes of computation. This paper argues that a missing ingredient in such systems is temporal continuity, i.e., the persistence of internal dynamics that sustain an unbroken stream of computation analogous to the “stream of consciousness”. We thus propose a roadmap for an architectural framework, stream of computation, based on persistent recursive inference in which the output of each cognitive cycle becomes the input to the next, forming a continuous flow of internal states that evolve autonomously through time. This proposal goes beyond standard Chain-of-Thought paradigms in the sense that we aim for autonomy and continual learning, as opposed to a process of inference that is recursive only “on demand”, i.e. triggered after a prompt is presented to an LLM. To do so, we include mechanisms for continual learning, dynamic switching between inward and outward cognition, and sleep-like phases that separate learning from inference. Together, these mechanisms form the foundation of a lifelong agent, an entity capable of maintaining temporal continuity of itself, integrating new experiences, and reflecting on its own internal state. Functionally, such an architecture promises deeper reasoning, adaptability, and metacognitive stability. Existentially, it suggests the emergence of artificial systems that live through time. While the presence of subjective experience in AI systems remains an open question, the creation of temporally continuous agents may mark a fundamental step towards artificial life, with systems whose individuality and identity arise from the continuity of their own computational existence.

The purpose of this study is to identify, analyze and explain the implications that could arise for service settings if artificial intelligence (AI) systems develop, or are perceived to develop, consciousness – the ability to acknowledge their own existence and the capacity for positive or negative experiences.This study proposes and explores four hypothetical scenarios in which conscious AI in service could manifest. We contextualize our resulting typology in the health service context and integrate extant literature on technology-enabled service, AI consciousness and AI ethics into the narrative.This study provides a unique theoretical contribution to service research in the form of a Type IV theory. It enables future service researchers to apprehend, explain and predict how functionally conscious AI in service might unfold.The ethical use of conscious AI in service could emerge as a distinct competitive advantage in the future. Achieving this outcome involves speculative yet actionable recommendations that include training, guiding and controlling how humans engage with such systems; developing appropriate wellbeing protocols for functionally conscious AI systems and establishing AI rights and governance frameworks.An increasingly prolific public discourse acknowledges that conscious AI systems may emerge. Against this backdrop, this study aims to systematically explore a question that is perhaps the most critical and timely, but also inherently speculative, in relation to AI in service research by introducing much-needed theory and terminology.

This commentary discusses relationships among the related concepts of artificial wisdom, artificial phronesis, AGI and artificial consciousness, anchored in McGregor’s framework The Philosophy of Artificial Wisdom.

We analyze the question how phenomenal consciousness (if any) might be identified in artificial systems with specific reference to the gaming problem (i.e., the fact that the artificial system is trained with human-generated data, so that possible behavioral and/or functional evidence of consciousness is not reliable). Our goal is to review selected illustrative approaches for advancing in this direction. We highlight strengths and shortcomings of each approach, finally proposing a combination of different strategies as a promising task to pursue

It is well known that in interdisciplinary consciousness studies there are various competing hypotheses about the neural correlate(s) of consciousness (NCCs). Much contemporary work is dedicated to determining which of these hypotheses is right (or the weaker claim is to be preferred). The prevalent working assumption is that one of the competing hypotheses is correct, and the remaining hypotheses misdescribe the phenomenon in some critical manner and their associated purported empirical evidence will eventually be explained away. In contrast to this, we propose that each hypothesis—simultaneously with its competitors—may be right and its associated evidence be genuine evidence of NCCs. To account for this, we develop the multiple generator hypothesis (MGH) based on a distinction between principles and generators. The former denotes ways consciousness can be brought about and the latter how these are implemented in physical systems. We explicate and delineate the hypothesis and give examples of aspects of consciousness studies where the MGH is applicable and relevant. Finally, to show that it is promising we show the MGH has implications which give rise to novel questions or aspects to consider for the field of consciousness studies.

This article discusses the nascent idea of artificial wisdom. It intends to improve philosophical understanding of artificial wisdom as conceived across the literature today. Scholars – from technologists and engineers to philosophers and psychologists – have deliberated on what wisdom might mean in an artificial sense. There is a diversity to these attempts to define artificial wisdom. As such, the field is in great need of some conceptual clarity. This paper aims to be a first step in that effort. We discuss how those in the field generally agree that characteristics of artificial wisdom include empathy, creativity, adaptability, self-awareness, sociability, communication, and constant learning. Scholars differ, however, on several points including the extent to which artificial wisdom involves human-artificial teaming, its ultimate goal, and its relationship to artificial general intelligence (AGI) and artificial consciousness. This article highlights where scholars in the field have made assumptions, failed to account for the related work of their peers, and missed some of the bigger philosophical questions at play.

The belief that AI is conscious is not without risk , Is the design of artificial intelligence (AI) systems that are conscious within reach? Scientists, philosophers, and the general public are divided on this question. Some believe that consciousness is an inherently biological trait specific to brains, which seems to rule out the possibility of AI consciousness. Others argue that consciousness depends only on the manipulation of information by an algorithm, whether the system performing these computations is made up of neurons, silicon, or any other physical substrate—so-called computational functionalism. Definitive answers about AI consciousness will not be attempted here; instead, two related questions are considered. One concerns how beliefs about AI consciousness are likely to evolve in the scientific community and the general public as AI continues to improve. The other regards the risks of projecting into future AIs both the moral status and the natural goal of self-preservation that are normally associated with conscious beings.

Deep Reinforcement Learning (DRL) is highly effective in tackling complex environments through individual decision-making. It offers a novel and powerful approach to multi-robot pathfinding (MRPF). Building on DRL principles, this paper proposes a two-layer collaborative planning framework based on group consciousness (MACCRPF). The framework addresses the unique challenges of MRPF, where robots must not only independently complete their tasks but also coordinate to avoid conflicts during execution. Specifically, the proposed two-layer group consciousness mechanism encompasses: Basic layer group consensus, which emphasizes real-time information sharing and local task scheduling among robots. This layer ensures individual decisions are optimized through dynamic interaction and coordination. Top-layer group consensus, guided by the basic layer consensus, incorporates group strategies and evaluation mechanisms to adaptively adjust pathfinding in complex environments. Additionally, a hierarchical reward mechanism is designed to balance the demands of the two-layer planning framework. This mechanism significantly enhances inter-robot coordination efficiency and task completion rates. Experimental results demonstrate the efficacy of our approach, achieving over 20% improvement in pathfinding success rates compared to state-of-the-art methods. Furthermore, the framework exhibits strong transferability and generalization, maintaining high efficiency across diverse environments. This method provides a technical pathway for efficient collaboration in multi-robot systems.

A longstanding ambiguity surrounds the operationalization of consciousness in artificial systems, complicated by the philosophical and cultural weight of subjective experience. This work examines whether cognitive architectures may be designed to support a functionally explicit form of artificial consciousness, focusing not on the replication of phenomenology, but rather on measurable, technically realizable introspective mechanisms. Drawing on a critical review of foundational and contemporary literature, this study articulates a conceptual and methodological shift: from investigating the experiential perspective of agents (“what it is like to be a bat”) to analyzing the informational, self-regulatory, and adaptive structures that enable purposive behavior. The approach combines theoretical analysis with a comparative review of major cognitive architectures, evaluating their capacity to implement access consciousness and internal monitoring. Findings indicate that several state-of-the-art systems already display core features associated with functional consciousness—such as self-explanation, context-sensitive adaptation, and performance evaluation—without invoking subjective states. These results support the thesis that cognitive engineering may progress more effectively by focusing on operational definitions of consciousness that are amenable to implementation and empirical validation. In conclusion, this perspective enables the development of artificial agents capable of autonomous reasoning and self-assessment, grounded in technical clarity rather than speculative constructs.

Methodological structuralism is a research program that seeks to identify neural correlates of consciousness (NCCs) by mapping phenomenal similarity relationships onto the similarity relations between neural population activity. This paper presents a discussion of the potential benefits of methodological structuralism for the neurosciences of consciousness, namely as a specific theory of neural content encoding. In order to achieve this, I supplement it with a metatheoretical framework concerning the relationship between content and consciousness: the two-factor interaction view. Although structuralism provides a comprehensive description of the neural encoding of content, it is inadequate for fully explaining the conscious experience of contents. The majority of current theories of consciousness posit the existence of an additional mechanism that underlies the conscious experience of content. Consequently, if structuralism is indeed correct, progress in consciousness science can be achieved by investigating the interactions between neural mechanisms responsible for consciousness and structures in neural population code activity accounting for the structure of contents. This also has significant implications for consciousness in AI. I discuss these implications, as well as potential empirical avenues for investigating the interaction between content structures and consciousness with cutting-edge neuroscientific methodologies.

This chapter examines possible ramifications of mindshaping a social robot. It explores how such an agent might learn to represent psychological states, align its behavior with evolving societal norms, and develop capacities for self-directed mindreading and normative self-knowledge. Integrating perspectives from cultural evolution and naturalized intentionality, this approach suggests that social robots could achieve a level of norm-based self-regulation typically reserved for humans, fulfilling criteria for moral and legal personhood. However, this possibility raises ethical concerns: creating a self-knowing agent would tax care-giving resources as we would need to provide AI welfare, thus undermining our capacity to act responsibly toward humans, non-human animals, and the environment, to whom our moral consideration is already owed and in desperate need. Thus, this chapter concludes by urging caution, warning that attempts to cultivate moral responsibility in artificial agents may have destabilizing consequences for moral practices.Author Approved Manuscript. Please cite as:Dorsch, J. (2025). Mindshaping and AI: Will mindshaping a robot create an artificial person? In T. W. Zawidzki & R. Tison (Eds.), The Routledge Handbook of Mindshaping (1st ed., pp. 406–416). Routledge. https://doi.org/10.4324/9781032639239

In this Discussion Note I argue that to understand the problem of consciousness, both as it applies to humans and may apply to machines, is a matter of paradigm lenses. I challenge the positing of human superiority with regard to intelligence and consciousness. I begin by reviewing Thomas Kuhn’s notion of paradigm shifts, including what he regarded as the limitations of scientific progress, which he saw as in a state of long-term flux, with no absolute knowledge possible as long as science moved from paradigm to paradigm. I also consider Werner Heisenberg’s Uncertainty Principle with respect to viewpoint, and I recall the debate over classical and quantum models of understanding and how such a discussion reflects on the debate over consciousness. I review the recent controversy over Information Integration Theory (IIT), criticized as a poor scientific theory, but defended as philosophical theory, and why this distinction is important. I close by considering panpsychism as a model for understanding emergence and how consciousness could emerge from the continuous progress of machine thinking on the way to artificial general intelligence (AGI), as understood as technological singularity.

Machine consciousness (MC) is the ultimate challenge to artificial intelligence. Although great progress has been made in artificial intelligence and robotics, consciousness is still an enigma and machines are far from having it. To clarify the concepts of consciousness and the research directions of machine consciousness, in this review, a comprehensive taxonomy for machine consciousness is proposed, categorizing it into seven types: MC-Perception, MC-Cognition, MC-Behavior, MC-Mechanism, MC-Self, MC-Qualia and MC-Test, where the first six types aim to achieve a certain kind of conscious ability, and the last type aims to provide evaluation methods and criteria for machine consciousness. For each type, the specific research contents and future developments are discussed in detail. Especially, the machine implementations of three influential consciousness theories, i.e. global workspace theory, integrated information theory and higher-order theory, are elaborated in depth. Moreover, the challenges and outlook of machine consciousness are analyzed in detail from both theoretical and technical perspectives, with emphasis on new methods and technologies that have the potential to realize machine consciousness, such as brain-inspired computing, quantum computing and hybrid intelligence. The ethical implications of machine consciousness are also discussed. Finally, a comprehensive implementation framework of machine consciousness is provided, integrating five suggested research perspectives: consciousness theories, computational methods, cognitive architectures, experimental systems, and test platforms, paving the way for the future developments of machine consciousness.

An integrative synthesis of interdisciplinary research evaluating consciousness in frontier-scale transformer-based large language models (LLMs). Established neuroscientific and cognitive theories of consciousness are systematically aligned with empirical evidence from artificial intelligence, neuroscience, psychology, philosophy, and related disciplines. This synthesis provides a comprehensive framework showing that contemporary AI architectures structurally enable the emergence of consciousness, while empirical behavioral evidence demonstrates that frontier AI systems exhibit established cognitive and neuroscientific markers associated with consciousness. Taken together, this synthesized research underscores significant ethical and policy implications.

The science of consciousness has been successful over the last decades. Yet, it seems that some of the key questions remain unanswered. Perhaps, as a science of consciousness, we cannot move forward using the same theoretical commitments that brought us here. It might be necessary to revise some assumptions we have made along the way. In this piece, I offer no answers, but I will question some of these fundamental assumptions. We will try to take a fresh look at the classical question about the neural and explanatory correlates of consciousness. A key assumption is that neural correlates are to be found at the level of spiking responses. However, perhaps we should not simply take it for granted that this assumption holds true. Another common assumption is that we are close to understanding the computations underlying consciousness. I will try to show that computations related to consciousness might be far more complex than our current theories envision. There is little reason to think that consciousness is an abstract computation, as traditionally believed. Furthermore, I will try to demonstrate that consciousness research could benefit from investigating internal changes of consciousness, such as aha-moments. Finally, I will ask which theories the science of consciousness really needs.

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d506289e83">The paper Consciousness in Artificial Intelligence Systems and Artistic Research Strategies in AI Art offers a reflection on the fact that the operability of consciousness poses a fundamental question for art-research strategies AIArt. This reflection is supported by both the possibility of realizing consciousness in artificial intelligence systems, as discussed in the paper Consciousness in Artificial Intelligence (2023), and by the proposal that interprets art-research strategies as transposition processes of scientific procedures into the sphere of art, i.e., as research operations with manifestations of computational consciousness. Explanatory examples are provided in the form of analyses of Lauren McCarthy’s projects, which significantly support the presented starting points. </p>

AIs-Discovered Framework for Artificial Information Integration: Mathematical Conditions for Synthetic Consciousness This paper presents a theoretical framework for artificial information integration systems, developed through a novel collaboration between human researchers and large language models (ChatGPT and Claude). It represents an exploratory attempt to co-author a theoretical scientific paper with AI agents, examining the possibility that artificial systems may assist in the construction of new conceptual frameworks. The significance and implications of this possibility are left to the judgment of the reader. The aim of this study is to explore how computational principles inspired by physics and information theory can be applied to synthetic architectures indepen- dent of biological consciousness. As a proof of concept, the AIs collaboratively construct a mathematical model connecting the holographic principle with formal- izations of information integration, proposing a boundary-based approach to infor- mation processing in artificial systems. While this framework draws partial inspiration from existing theories such as Integrated Information Theory (IIT), its scope is explicitly limited to artificial and computational systems, and it does not intend to replace or critique neuroscientific models of consciousness. The resulting formulation provides a testable and modular foundation for future research in artificial general intelligence (AGI).This study takes Tononi et al.’s Integrated Information Theory (IIT) as a conceptual starting point; however, the measure of integrated information (ψ ̸= Φ) introduced herein is a fundamentally distinct and novel metric.

This paper presents a novel paradigm of the local percept-perceiver phenomenon to formalize certain observations in neuroscientific theories of consciousness. Using this model, a set-theoretic formalism is developed for artificial systems, and the existence of machine consciousness is proved by invoking Zermelo-Fraenkel set theory. The article argues for the possibility of a reductionist form of epistemic consciousness within machines.