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Seth opposes computational views of consciousness to views that emphasise biology, because they imply different verdicts about hypothetical AIs that replicate information-processing without being biologically alive. But if we accept a background theory like panpsychism, on which consciousness is abundant in nature, this opposition becomes less central: even if a non-biological system could not have our consciousness, we should still default to accepting any self-ascriptions of consciousness it makes.

I argue that we are not epistemically justified to judge whether the neurobiological details of computational mechanisms are relevant to consciousness or not. It is unclear how the medium independence of neural computation required for Computational Functionalism could be established. But it is also unclear whether predictive processing depends on life-sustaining biological processes and whether consciousness is medium dependent.

Are chatbots AI zombies or conscious minds? I suspect they are AI zombies, at least when implemented on today’s standard hardware. I argue that today’s Large Language Mode’s (LLM’s) consciousness-like behaviors do not suggest they are conscious, because there is an error theory – a theory explaining why they behave as if they are conscious in absence of actual felt experience. LLMs function as “crowdsourced neocortices” – as they scale up, they come to mirror human conceptual structures, leading to human-like behaviors, including those involving consciousness. By contrast, bio-computers, quantum computers, and neuromorphic systems present more serious candidates for AI consciousness.

A minimally autopoietic artificial intelligence (AI) system could be designed according to current engineering principles. Although Seth suggests that standard computational systems are not autopoietic and therefore not conscious, autopoiesis is a high-level, functional concept, and nothing appears to prevent autopoietic processes in a standard computational system. Autopoietic requirements do not in principle rule out consciousness in standard AI systems.

I was delighted, challenged, and somewhat overwhelmed by the breadth and depth of the commentaries elicited by the target article. I am very grateful to every one of my commentators for offering such thoughtful perspectives. Here, I attempt to isolate some common themes and offer some thoughts in response. One conclusion is immediately evident: it is increasingly urgent to think clearly about the prospects for conscious AI and about the consequences of seemingly conscious AI. The commentaries also flesh out the potential for an exciting research programme investigating the substrate dependence of both consciousness and cognition.

In his defence of biological naturalism and rejection of computational functionalism, Seth shows himself to be in the grip of dualistic intuitions. Thankfully, we can read Seth’s paper as a worthwhile contribution to the ongoing society-wide conversation on how to talk about, think about, and treat advanced AI, while setting aside the metaphysical framing of his arguments.

Seth mounts a rich and persuasive case that human consciousness cannot be understood without life’s distinctive machinery – autopoiesis, allostatic control, predictive processing under the free-energy principle, and substrate-entangled dynamics. I agree that this undercuts the expectation that humanlike consciousness will “come for free” with more intelligent machines. However, we should also be cautious about drawing extrapolations from features of biological consciousness – even essential features – to the possibility of exotic forms of consciousness in artificial systems.

A.I. isn’t conscious because consciousness requires properties that only biological agents possess, which A.I. lacks. What A.I. lacks that is relevant for consciousness is not predictive processing or the minimization of free energy. A.I. lacks embodiment, direct coupling with the environment, multiscale complexity, and self-organization, e.g., plasticity, degeneracy, robustness, and the properties that enable living organisms to be highly adaptive to environmental changes.

There is a curious contradiction in Seth’s argument, or a degree of ambiguity which proves to be fatal. He endorses “biological naturalism,” but he says also that it is possible to engineer “real artificial consciousness.” Such an artificially consciousness system need not be carbon-based, he says, so long as it is embodied in a system that is artificially “alive.”

Does the free energy principle support the view that consciousness is substrate-dependent? Based on previous work (Wiese, 2024), I argue that the free energy principle can be used to clarify conditions under which a system realises, rather than merely simulates, a causally relevant property (such as consciousness). This helps to address a weakness in Seth’s argument.

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.