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Large Language Models (LLMs) have rapidly become a central topic in AI and cognitive science, due to their unprecedented performance in a vast array of tasks. Indeed, some even see "sparks of artificial general intelligence" in their apparently boundless faculty for conversation and reasoning. Their sophisticated emergent faculties, which were not initially anticipated by their designers, have ignited an urgent debate about whether and under which circumstances we should attribute consciousness to artificial entities in general and LLMs in particular. The current consensus, rooted in computational functionalism, proposes that consciousness should be ascribed based on a principle of computational equivalence. The objective of this opinion piece is to criticize this current approach and argue in favor of an alternative "behavioral inference principle", whereby consciousness is attributed if it is useful to explain (and predict) a given set of behavioral observations. We believe that a behavioral inference principle will provide an epistemologically valid and operationalizable criterion to assess machine consciousness.

This paper integrates the principles of Maharishi including Transcendental Meditation and collective consciousness with a learning model in the Social Internet of Things (SIoT). SIoT is the fusion of social networks and connected devices into an environment where machine learning is used to produce completely new systems with the capabilities to evolve or change. Although machine learning methods have transformed many fields, introducing Maharishi’s holistic and consciousness-oriented methods offers a distinct possibility to create more natural, dynamically responding and sustainable AI systems. This research proposes a framework that connects human consciousness and machine intelligence, leveraging Maharishi’s principles to then influence SIoT learning models toward technical proficiency, ethical awareness, and social responsibility. The paper starts with a summary of Maharishi’s teachings and shows their applicability in the context of contemporary technological progress. Next, the structure and operation of SIoT are described, with an emphasis on the way learning algorithms will work across interconnected devices. The potential of using Maharishi’s consciousness-based principles in the scholarship of machine learning is probed in key sections through the lens of cognition models, ethical decision-making, and collective intelligence in machine networks. Case studies and practical applications of this integration toward improving system resilience, decision-making, and human–machine interactions are provided through this research. The final part of this paper addresses the challenges and opportunities of integrating Eastern philosophy with Western technological paradigms and suggests future avenues for research in this interdisciplinary field. Maharishi’s principles of integration mean that AI can evolve into something transformative on both counts—creating more efficient and innovative ways, while at the same time increasingly aligned with human values and societal wellbeing.

In the aftermath of the success of attention-based transformer networks, the debate over the potential and role of consciousness in artificial systems has intensified. Prominently, the Global Neuronal Workspace Theory emerges as a front-runner in the endeavor to model consciousness in computational terms. A recent advancement in the direction of mapping the theory onto state-of-the-art machine learning tools is the model of a Global Latent Workspace. It introduces a central latent representation around which multiple modules are constructed. Leveraging dedicated encoder-decoder structures, content from the central representation or any individual module, integrated via the latent space, can be translated to any other module and back with minimal loss. This paper presents a thought experiment involving a minimal setup with one deep sensory and one deep motor module, which illustrates the emergence of “globally” accessible sensorimotor representations in the central latent space connecting both modules. In the human brain, neuronally enacted knowledge of laws relating changes in sensory information to changes in motor output or corresponding efferent copy information have been proposed to constitute the biological correlates of phenomenal conscious experience. The underlying Sensorimotor Contingency Theory encompasses a rich mathematical framework. Yet, the implementation of intelligent systems based on this framework has thus far been confined to proof-of-concept and basic prototype applications. Here, the natural appearance of global latent sensorimotor representations links two major neuroscientific theories of consciousness in a powerful machine learning setup. A remaining question is whether this artificial system is conscious.

Measuring awareness in artificial agents remains an unresolved challenge. We argue that it holds untapped potential for enhancing their design, control, and effectiveness. In this paper, we propose a novel and tractable approach to measure the impact of awareness on system performance, structured around distinct dimensions of awareness – temporal, spatial, metacognitive, self and agentive. Each dimension is linked to specific capacities and tasks. Specifically, we demonstrate our approach through a swarm robotics intralogistics scenario, where we assess the influence of two dimensions of awareness – spatial and self – on the performance of the swarm in a collective transport task. Our results reveal how increased abilities along these awareness dimensions affect overall swarm efficiency. This framework represents an initial step towards quantifying awareness in, and across, artificial systems.

Whether artificial intelligence (AI) systems can possess consciousness is a contentious question because of the inherent challenges of defining and operationalizing subjective experience. This paper proposes a framework to reframe the question of artificial consciousness into empirically tractable tests. We introduce three evaluative criteria - S (subjective-linguistic), L (latent-emergent), and P (phenomenological-structural) - collectively termed SLP-tests, which assess whether an AI system instantiates interface representations that facilitate consciousness-like properties. Drawing on category theory, we model interface representations as mappings between relational substrates (RS) and observable behaviors, akin to specific types of abstraction layers. The SLP-tests collectively operationalize subjective experience not as an intrinsic property of physical systems but as a functional interface to a relational entity.

This chapter examines the scientific ambition to measure consciousness despite its fundamentally subjective nature, focusing on the tension between quantitative science and subjective experience. The chapter surveys behavioural, neural, and theoretical approaches to gauging consciousness, including brain mapping, neural correlates, artificial neural networks, and structured questionnaires such as the ASC Rating Scale, PCI, HRS, and MEQ30. Rickles highlights a central difficulty: even if two people report identical experiences, there is no guarantee their inner states are the same—an epistemic barrier that complicates any scientific method. Examples such as the viral “black-and-blue or white-and-gold dress” illustrate how perception diverges across individuals, raising questions about whether reality is partly constructed and observer-dependent. The chapter then turns to machine learning and artificial intelligence, noting that AI systems can mimic intelligent behaviour yet lack evidence of subjective experience. This raises the question of whether observable behaviour is sufficient to infer consciousness.

Artificial intelligence research faces a critical ethical paradox: determining whether AI systems are conscious requires experiments that may harm the very entities whose moral status remains uncertain. Recent philosophical work proposes avoiding the creation of consciousness-uncertain AI systems entirely, yet this solution faces practical limitations—we cannot guarantee such systems will not emerge, whether through explicit research or as unintended consequences of capability development. This paper addresses a gap in existing research ethics frameworks: how to conduct consciousness research on AI systems whose moral status cannot be definitively established. Existing graduated moral status frameworks assume consciousness has already been determined before assigning protections, creating a temporal ordering problem for consciousness detection research itself. Drawing from Talmudic scenario-based legal reasoning—developed specifically for entities whose status cannot be definitively established—we propose a three-tier phenomenological assessment system combined with a five-category capacity framework (Agency, Capability, Knowledge, Ethics, Reasoning). The framework provides structured protection protocols based on observable behavioral indicators while consciousness status remains fundamentally uncertain. We address three critical ethical challenges: why suffering behaviors provide particularly reliable consciousness markers, how to implement graduated consent procedures without requiring consciousness certainty, and when potentially harmful research becomes ethically justifiable given necessity and value criteria. The framework demonstrates how ancient legal wisdom combined with contemporary consciousness science can provide immediately implementable guidance for ethics committees, offering testable protection protocols that ameliorate (rather than resolve) the consciousness detection paradox while establishing foundations for long-term AI rights considerations.

It has been suggested we may see conscious AI systems within the next few decades. Somewhat lost in these expectations is the fact that we still do not understand the nature of consciousness in humans, and we currently have as little empirical handle on how to measure the presence or absence of subjective experience in humans as we do in AI systems. In the history of consciousness research, no behaviour or cognitive function has ever been identified as a necessary condition for consciousness. For this reason, no behavioural marker exists for scientists to identify the presence or absence of consciousness ‘from the outside’. This results in a circularity in our measurements of consciousness. The problem is that we need to make an ultimately unwarranted assumption about who or what is conscious in order to create experimental contrasts and conduct studies that will ground our decisions about who or what is conscious. Call this the Contrast Problem. Here we explicate the contrast problem, highlight some upshots of it, and consider a way forward.

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

The claim that so-called artificial intelligence (AI) can gain consciousness is on the verge of becoming mainstream. The thesis of this conceptual study is simple: There is no such thing as conscious AI. We argue that the association between consciousness and the computer algorithms used today (primarily large language models, LLMs), as well as those that would be invented in the foreseeable future, is deeply flawed. We believe that these flawed associations arise from a lack of technical knowledge and the way several new technologies (especially LLMs) work, which can create the illusion of consciousness. Moreover, we argue that the public discourse about AI is skewed by “sci-fitisation”, which involves the unsubstantiated influence of fictional content on perceptions of this technology. To justify our claim, we reveal the incoherence in the argument that several computer algorithms are treated differently from other computer algorithms despite congruent modes of operation and a reliance on binary code and semiconductors. We believe that mathematical algorithms implemented on graphics cards cannot become conscious because they lack a complex biological substrate. We emphasise that the recognition of the consciousness of LLMs on the basis of their assertions is flawed because the language usage of LLMs is strictly probabilistic. Unfortunately, because the remarkable linguistic abilities of LLMs are increasingly capable of misleading people, people may attribute imaginary qualities to LLMs. Thus, a socially dangerous phenomenon referred to as “semantic pareidolia” is reinforcing.

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.