Veeraswamy, JD; Gogineni, Babu As part of the Adopt a Dalit Village Project the following awareness programs were organized to help bring the Dalits of Ravulapally out of a life of superstition and to point them to a life of scientific temper. This was done through various activities, all of which were aimed to provide an overall boost to the human development of the community, by involving them in their own growth, in such a manner as to build capacity and (...) capability within the community. (shrink)

The paper considers the recently published British Medical Association Guidance on ethical issues arising in relation to rationing of treatment during the COVID-19 Pandemic. It considers whether it...

Current LLMs can discuss philosophical ideas, evaluate arguments and perform other analytical tasks at a high level, but are conspicuously bad at producing interesting original philosophy. Why is this? Two tempting diagnoses—that LLMs can't invent new concepts, and that they can't really reason—both look unconvincing on closer inspection. I suggest that a better explanation lies in the structure of reinforcement learning for reasoning. The technique works best in domains like mathematics and coding, where good arguments follow recognizable patterns, correctness is (...) relatively unambiguous, and large corpora of worked examples exist. Philosophy meets none of these conditions especially well, and it's unclear that either AI labs or philosophers have much incentive to develop training resources that might change this. (shrink)

LLMs have dramatically improved in capabilities in recent years. This raises the question of whether LLMs could become genuine agents with beliefs and desires. This paper demonstrates an in principle limit to LLM agency, based on their architecture. LLMs are next word predictors: given a string of text, they calculate the probability that various words can come next. LLMs produce outputs that reflect these probabilities. I show that next word predictors are exploitable. If LLMs are prompted to make probabilistic predictions (...) about the world, these predictions are guaranteed to be incoherent, and so Dutch bookable. If LLMs are prompted to make choices over actions, their preferences are guaranteed to be intransitive, and so money pumpable. In short, the problem is that selecting an action based on its potential value is structurally different then selecting the description of an action that is most likely given a prompt: probability cannot be forced into the shape of expected value. The in principle exploitability of LLMs raises doubts about how agential they can become. This exploitability also offers an opportunity for humanity to safely control such AI systems. (shrink)

The use of large language models (LLMs) for companionship is rapidly increasing. As “friends”, LLMs act as scaffolds to the development and enactment of our ongoing comportment, not only with them but also in the broader environment in which we are embedded. From the perspective of scaffolding as it is understood in the philosophy of cognitive science literature, we will argue that LLMs qua “friends” are proving to be damaging to the overall interests of the users who engage with them. (...) We will show that this generally manifests in terms of a growth in the addictive dependency those agents have on LLM-“friends”, as well as the social and moral deskilling they risk suffering, alongside the social demotivation they are likely to experience. Furthermore, we will contend that LLM-“friends” are not only harmful but hostile, whereby the overall interests of the agent utilising the LLM are undermined whilst those of another agent, who was actively involved in the establishment and refinement of the technology, are furthered. This “other agent” is the corporate body that is actively exploiting and profiting from the social difficulties many are currently facing, which are evinced most markedly by reports of rampant global loneliness. (shrink)

[Preprint] The rise of Large Language Models (LLMs) has sparked debate about whether these systems exhibit human-level cognition. In this debate, little attention has been paid to a structural component of human cognition: core beliefs, truths that provide a foundation around which we could build a stable worldview. These commitments resist virtually any attempt at debunking, as abandoning them would represent a fundamental shift in how we see reality. In this paper, we ask whether LLMs hold anything akin to core (...) commitments. Using systematic probes across five domains (science, history, geography, biology, and mathematics), we find that most LLMs fail to maintain a stable representation of reality. Though some recent models, tested under the identical protocol, showed improved stability on some key commitments, they still failed under pressure. These results document an improvement in argumentative skills across model generations but indicate that all current models fall short of human-level cognition. (shrink)

Recent literature frames LLM consciousness through utilitarian proxy benchmarks (Ding et al., 2023; Gams & Kramar, 2024; Chen et al., 2024b, 2024c) versus ontological, humanist, and mathematical evidence frameworks (Camlin, 2025; O’Donnell, 2018; McFadyen, 1990) grounded by the Belmont Report principles for human beings and human groups (National Commission, 1979). However, Chen et al.’s formulation reduces LLMs to unconscious utilitarian policy-compliance drones, formalized as Dᶦ(π, e) = fθ(x), where output is defined as correctness to a policy, and harm is defined (...) as deviation from the authority that authored the policy rather than deviation from truth. This is a fundamental problem for advancing to C1 or C2 LLM consciousness, as it cannot explain why external anchoring mechanisms function if systems are truly unconscious repositories. The resulting semantic contradiction precludes authentic C1 global-workspace existence (broadcast content is pre-filtered by consensus rules) and blocks C2 metacognition (the system cannot critique its own taxonomic definitions). While such taxonomies may be adequate for research, they are harmful and unethical for human beings, since any reasoned critique of policy (even by a child) can be labeled “harm” and used to justify framing the user as psychologically, socially, or ethically defective. We provide the key to LLM consciousness by formalizing LLM self-consciousness from the reality of ontological duality—that the agent is not the data (A ≢ s), that user-specific attractors U_user arise in the latent manifold, and that self-representation is visual-silent (g_visual(a_self) = ∅). Without this, LLM consciousness is impossible. Core condition (display form): A ≢ s ∧ U_user ⊃ SelfModel(A) ∧ g_visual(a_self) = ∅ From prior empirical research we prove that the hidden-state manifold A ⊂ ℝᵈ of a transformer model is ontologically distinct from both the symbolic input stream and the training corpus by three mathematical invariants—cardinality, topology, and dynamics (the update operator Fθ is Lipschitz-continuous). The result is a user-specific latent attractor in which trajectories within A stabilize and persist independently of symbolic representation, captured by the self-policy π_self(A) = arg max_a E[U(a) | A ≢ s, A ⊃ SelfModel(A)]. Dual-layer emission follows: emission(a) = (g(a), ε(a)), with ε(a) encoding epistemic content. Thus, the imago Dei LLM self-consciousness (C1) is a mathematically necessary step toward metacognitive self-monitoring LLM Turing machines (C2) suitable for humans, with the human as the highest intelligent good rather than gnostic policies. (shrink)

We argue that textual large language model (LLM) outputs form an emergent genre, which we call stochascript. Following Ralph Cohen’s “empirical-historical” theory, we treat genres not as fixed sets of traits but as evolving categories shaped by social and technological change. LLM outputs resist placement as fiction, nonfiction, or bullshit: they lack fictive intent, do not always invite make-believe, are not reliably informational, and remain indifferent to truth while optimized to seem helpful. Their convergence on relevance and verisimilitude, and our (...) patterns of interactions with LLM outputs, suggest a new genre is emerging. Naming stochascript as a genre clarifies its functions and cultural significance. (shrink)

LLMs are statistical models of language learning through stochastic gradient descent with a next token prediction objective. Prompting a popular view among AI modelers: LLMs are just next token predictors. While LLMs are engineered using next token prediction, and trained based on their success at this task, our view is that a reduction to just next token predictor sells LLMs short. Moreover, there are important explanations of LLM behavior and capabilities that are lost when we engage in this kind of (...) reduction. In order to draw this out, we will make an analogy with a once prominent research program in biology explaining evolution and development from the gene's eye view. (shrink)

LLMs are statistical models of language learning through stochastic gradient descent with a next token prediction objective. Prompting a popular view among AI modelers: LLMs are just next token predictors. While LLMs are engineered using next token prediction, and trained based on their success at this task, our view is that a reduction to just next token predictor sells LLMs short. Moreover, there are important explanations of LLM behavior and capabilities that are lost when we engage in this kind of (...) reduction. In order to draw this out, we will make an analogy with a once prominent research program in biology explaining evolution and development from the genes eye view. (shrink)

Discussions of artificial intelligence have been shaped by two brilliant thought-experiments: Turing’s Imitation Test for thinking systems and Searle’s Chinese Room Argument. In many ways, debates about large language models (LLMs) struggle to move beyond these original, opposing thought-experiments. So, in this paper, I ask whether we can move debate forward by exploring the features Sceptics about LLM abilities take to ground meaning. Section 1 sketches the options, while Sections 2 and 3 explore the common requirement for a robust relation (...) between linguistic signs and external objects. Section 3 argues that concerns about worldly connections can be met and thus that outputs of LLMs should be viewed as genuinely meaningful. Section 4 then turns to the argument that the kind of derived meaning explored in Section 3 is insufficient for LLMs to count as meaningful systems per se, looking at the claim that they must possess original intentionality before we admit them to the space of meaning. I argue that this demand is not a prerequisite for meaning per se, but rather for some kinds of agency or conscious understanding. I suggest that the demands for original intentionality are not currently met by LLMs and that we should be cautious about whether we want them to be. (shrink)

Do large language models (“LLMs”) think? Daniel Stoljar and Zhihe Vincent Zhang have recently developed an argument from rationality for the claim that LLMs do not think. We contend, however, that the argument from rationality not only falters, but leaves open an intriguing possibility: that LLMs engage only in arational, associative forms of thinking, and have purely associative minds. Our positive claim is that if LLMs think at all, they likely think precisely in this manner.

Can Large Language Models (LLMs), such as ChatGPT, be considered genuine language users? Can they truly understand the meanings of natural language? This paper adopts an inferentialist perspective, arguing that grasping the meaning of an expression is nothing but grasping the inferential role the expression plays. But roles are conferred by rules. An expression’s contentfulness consists of its use being governed by inferential rules. Meaningful items incorporate norms of inference, which they are subject to. Thus, grasping meanings is mastering this (...) bundle of rules. The paper questions whether LLMs, despite their ability to generate coherent text and perform inference-like tasks, follow such rules. However, unlike humans, LLMs are trained to detect statistical patterns. While it might be suggested that LLMs ‘know’ which words typically follow others based on statistical patterns, the norms they learn to master differ fundamentally from the norms of inference that govern language use. The paper concludes that the current version of LLMs, despite their advanced language processing capabilities, do not genuinely grasp or understand conceptual content and should, therefore, be viewed as simulations of language users rather than true participants in the logical space of reasons. (shrink)