A persistent ambition in artificial intelligence research holds that large language models (LLMs) will eventually become embodied—through attachment to robots, augmentation with multimodal sensors, integration into vision–language–action architectures, or sheer scale until grounding emerges from training data. This ambition rests on a misunderstanding of embodiment. Embodiment is a constitutive condition of being a self-producing, self-maintaining living system whose continued existence is genuinely at stake in its couplings with the environment. Adding sensors, actuators, or physical instantiation to a computational substrate yields engineered coupling, which is something else entirely. We identify five conditions that any genuinely embodied system must satisfy—autopoietic closure, precariousness, constructive self-determination, intrinsic temporality, and mereological closure—and show that no computational artifact can satisfy any of them, let alone all five. The barrier is categorical incompatibility: the same property that makes a system computational (heteronomous design and maintenance through external specification) is the property that precludes it from being embodied. Coupling artifacts to environments through transducers achieves engineered interaction. Having a body and being a body are different conditions. We close by examining why the embodiment-equivalence claim persists despite its categorical impossibility, and by arguing that the confusion sustains two harms—overselling AI systems for tasks they cannot perform, and misdirecting public concern toward speculative futures while the actual harms of deployed systems accumulate without being addressed. Clarifying the categorical distinction is a precondition for taking the genuine stakes of AI deployment seriously rather than as a philosophical aside.
Floridi’s Certainty–Scope Conjecture holds that no artificial intelligence system can be simultaneously reliable on every input and broad enough to cover the full richness of unstructured data: reliability and breadth are in structural tension, imposed by the finite information-processing capacity of any mechanism implementing intelligence. The Conjecture has until now been a philosophical thesis without a proof. This article supplies one in the finite-classification regime—the simplest non-trivial setting in which the Conjecture admits an exact statement, a sharp converse, and a matching achievability result—and is the discrete half of a coordinated diptych whose continuous companion proves a matching rate–distortion converse for non-finite semantic spaces. The technical machinery is classical: taking certainty to be Bayes-optimal accuracy and scope to be the class count n, a Fano-type argument yields a converse binding certainty and scope against mechanism capacity, with the n-ary symmetric channel attaining the exact certainty form with equality; the simplified explicit upper bound has asymptotic slack ln 2/ln n on the certainty scale. Six structural extensions carry the philosophical weight. Multi-task composition compounds scope multiplicatively, so joint certainty decays as O(1/T); fixed-bandwidth side information yields vanishing gains; static post-processing of the mechanism’s output adds zero augmented capacity, placing chain-of-thought (so idealised) on the wrong side of the bound; sequential tool use requires logarithmically growing query counts; routing does not lift the local constraint; and context-wise feasibility need not compose into global feasibility. A synthetic bottleneck experiment illustrates the bound’s bite, with an analytical account of harder regimes via the Bhattacharyya bound. The diptych’s discrete-side engagement with Lissack, Immediato, and Watson and Sterkenburg is recorded.
In this paper, I develop a normative framework for evaluating the use of new ICTs in the realm of religious faith. To this end, in Sect. 2, I argue for a partial account of religious faith, according to which typical religious faith requires certain skills and entails religious responsibility. Section 3 examines two specific challenges arising from the use of advanced ICTs for religious purposes, focusing on the recent debate regarding the authenticity of Islamic prayer apps and the challenges associated with so-called religious robots. Finally, in Sect. 4, based on the suggested account of religious faith in Sect. 2, I introduce the problems of ICT-driven religious deskilling and religious irresponsibility, which help explain important aspects of our intuitions regarding the considered challenges. The upshot is the general recommendation that policymakers, designers, researchers, and users of religious ICTs must remain sensitive to the risks of religious deskilling and religious irresponsibility.
Predictive processing casts perception, action, and learning as probabilistic inference. This paper asks when a Bayesian description identifies a mechanism. I distinguish computational description, algorithm, and physical implementation. Mechanism requires a causal mapping from physical transitions to inferential roles; behavioural fit cannot establish it. Priors range over model-supplied alternatives; observation does not reveal the organism’s individuation. Bayesian algorithms may be high-dimensional, continuous, nonparametric, and representation-growing. Every specified model has a fixed reachable closure; neither closure nor size decides mechanism. The question is which differences a proposed controller state treats as irrelevant. If histories assigned to that state respond differently to intervention because of timing, phase, contact, or field structure, the mapping omits part of the mechanism. A prospectively richer Bayesian or hybrid account may include those differences. High-dimensional systems and flexible model families often resist binary falsification when an experiment’s projection loses distinctions required by the claim. I therefore propose convergent tests of specified internal, coupled, and hybrid mappings. Results reject only a mapping within a declared task, scale, horizon, and margin; post-hoc revision creates a new hypothesis. The resource-bounded coupling hypothesis predicts that human-level transfer on contact-rich tasks depends on distinctions retained in organism–environment relations. An adaptive internal controller counts against it when transfer has no preregistered resource disadvantage, relational-history effects vanish in state-collision tests, the relational block adds no meaningful intervention-predictive value, and internal-realiser interventions have distinctive predicted effects. Existing evidence has not established a Bayesian mechanism for unconstrained embodied behaviour.
This paper examines the implications of Bedau’s notion of weak emergence when combined with both a hierarchical ontological model and Wolfram’s principle of computational equivalence. I argue that if a system exhibits weak emergence, the very fact of weak emergence of some of its states must itself be a weakly emergent state. Interpreted through Wolfram’s principle of computational equivalence, this implies that a system with weakly emergent states would need to ‘decide’ whether its own dynamical trajectories are incompressible—which contradicts with the formal undecidability of the incompressibility predicate. I term this aporetic conclusion the paradox of weak emergence. After evaluating possible responses, I contend that Bedau’s approach exemplifies what I call the derivational account of metaphysical relations, a framework that I will show to be highly problematic insofar as it implies a mismatch between the intrinsic limitations of formal derivations and the explanatory ambitions of metaphysical notions.
