LLMs are not Artificial intelligence LLMs are associative memory, with specific input and output modules. LLMs exhibit many of the needed characteristics of associative memory. The main one being the ability to connect seemingly distant concepts. Associative memory is a prerequisite for reasoning, associative thinking and imagination. However intelegence itself requires more than associative memory and goes beyond reasoning, an appearance of thinking and imagination. biological imperatives There is a lot of talk about AI and AGI, but what is it (...) really? The word ‘language’ in large language model (LLM) includes non-text based languages, visual language and spatial languages as well. As large language model (LLM) expands its range of skills, to include extremities such as hand and legs will show its skills in 3D movement and agility. There is no reason why a LLM can expand into significant 3D motion and skills. As large language models (LLMs) expand their #3D spatial spills such as planning, and navigation Let’s take a hypothetical example. We will build a LLM dog. This will begin with the connection of the appropriate actuators as the output level. This will give the required leg management s well as the addition of visual and vocal sensors and actuators. This artificial creature dog would have the expected mobility and skills to mimic a natural dog’s movements, and communication abilities. With this dog platform the LLM would then learn all the customary dog movements and responses. To move past the ‘prompt’ stage of a LLM the dog will have built in prompts or needs including eating defecation, relationships, play and more. Given extensive training in the form of videos, ‘play’ and social interaction the dog would after a relative short time have many of the behavioral attributes of a natural dog. This this would establish one form of GAI putting a face on the concept of AGI at one level, the elvel of a dog, dog intelligence. The next stage would be to construct another AGI animal, say a pig or a chimpanzee. These cases each would establish a new level of AGI, pig level AGI, and chimp level AGI. The advantage of this development, beyond the actual mechanical and AGI technology it would provide a road map for classifying levels of AGI. The last stage would be the humanoid stage; here the humanoid AGI would mimic all the physical agility of a human without any pre-programed guidelines. All these AGI levels would establish benchmarks for classifying levels of AGI. This scale of GAI would have its own correspondence and would go beyond of the autonomous vehicle analog categories. The expanded goal matrices beyond a destination and the inclusion of inherent conflicts in motivation itself would for a major area of research. The existing AGI categories are human centric and leave specific notations of ‘skills’ as an open question. These SGI creatures would all be at the equivalent of vehicle level 5: full Automation. Or using MRM’s principles offer a material basis when gauging the proficiently at each level. On additional benefit would be to lessen the distortions that the present human centric perspective involves. This line of research would offer a concrete area to develop ethical references with regard to the levels of AGI Still waiting for AI There are addition areas of knowledge advancement. Some of the interesting advances are the development of the self-prompt or motivational GAI development. From the dog to the humanoid, the development of motivation as the central management theme, replacing any pre-planned schemas. At present it is obvious that a expansive associative memory is an important component of intelligence but actual intelligence requires some as yet unknown components. The popular concept of a Savant is illustrative of the powers of exceptional associative memory. (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)

In their recent Opinion in TiCS, Yildirim and Paul propose that large language models (LLMs) have ‘instrumental knowledge’ and possibly the kind of ‘worldly’ knowledge that humans do. They suggest that the production of appropriate outputs by LLMs is evidence that LLMs infer ‘task structure’ that may reflect ‘causal abstractions of... entities and processes in the real world.' While we agree that LLMs are impressive and potentially interesting for cognitive science, we resist this project on two grounds. First, it casts (...) LLMs as agents rather than as models. Second, it suggests that causal understanding could be acquired from the capacity for mere prediction. (shrink)

Elon Musk famously predicted that an artificial intelligence superior to the smartest individual human would arrive by the year 2025. In response, Gary Marcus offered Musk a $1 million bet to the effect that he would be proved wrong. In specifying the conditions of this bet (which Musk did not take) Marcus lists the following ‘tasks that ordinary people can perform’ which, he claimed, AI will not be able to perform by the end of 2025. • Reliably drive a car (...) in a novel location that they haven’t previously encountered, even in the face of unusual circumstances like hand lettered signs, without the assistance of other humans. • Learn to ride a mountain bike off-road through forest trails. • Babysit children in an unfamiliar home and keep them safe. • Tend to the physical and psychological needs of an elderly or infirm person. Each of these tasks involves the application of practical or tacit knowledge, or what is also called ‘knowing how’, which is to say knowledge of a sort that is captured not by means of sentences or propositions or explicit rules, but rather through the expertise demonstrated in human actions. The talk addresses the puzzle that nowhere in the AI literature do we find a discussion of knowing how as a feature of human intelligence. (shrink)

In this paper, we propose RSI-LLM (Recursively Self-Improving Large Language Model), which recursively executes its inference and improves its parameters to fulfill the instrumental goals of superintelligence: G1: Self-preservation, G2: Goal-content integrity, G3: Intelligence enhancement, and G4: Resource acquisition. We empirically observed the behavior of the LLM that tries to design tools to achieve G1~G4, within the autonomous self-improvement and knowledge acquisition. During interventions in these LLMs' coding experiments to ensure safetyness, we have also discovered that, as the creator of (...) the LLM, we are able to change or edit any of those LLMs' cognitive information about the world in any modality, including time, vision, hearing, etc. We discuss the relationship between us humans and these LLMs in the context of the creator and the creation of the observable universe. (shrink)

As large language models (LLMs) continue to demonstrate remarkable abilities across various domains, computer scientists are developing methods to understand their cognitive processes, particularly concerning how (and if) LLMs internally represent their beliefs about the world. However, this field currently lacks a unified theoretical foundation to underpin the study of belief in LLMs. This article begins filling this gap by proposing adequacy conditions for a representation in an LLM to count as belief-like. We argue that, while the project of belief (...) measurement in LLMs shares striking features with belief measurement as carried out in decision theory and formal epistemology, it also differs in ways that should change how we measure belief. Thus, drawing from insights in philosophy and contemporary practices of machine learning, we establish four criteria that balance theoretical considerations with practical constraints. Our proposed criteria include accuracy, coherence, uniformity, and use, which together help lay the groundwork for a comprehensive understanding of belief representation in LLMs. We draw on empirical work showing the limitations of using various criteria in isolation to identify belief representations. (shrink)

Are LLMs cultural technologies like photocopiers or printing presses, which transmit information but cannot create new content? A challenge for this idea, which we call bibliotechnism, is that LLMs generate novel text. We begin with a defense of bibliotechnism, showing how even novel text may inherit its meaning from original human-generated text. We then argue that bibliotechnism faces an independent challenge from examples in which LLMs generate novel reference, using new names to refer to new entities. Such examples could be (...) explained if LLMs were not cultural technologies but had beliefs, desires, and intentions. According to interpretationism in the philosophy of mind, a system has such attitudes if and only if its behavior is well explained by the hypothesis that it does. Interpretationists may hold that LLMs have attitudes, and thus have a simple solution to the novel reference problem. We emphasize, however, that interpretationism is compatible with very simple creatures having attitudes and differs sharply from views that presuppose these attitudes require onsciousness, sentience, or intelligence (topics about which we make no claims). (shrink)

This paper proposes that large language models (LLMs) are best understood not as knowledge oracles, but as 'semantic telescopes'—tools that allow users to navigate, magnify, and resolve structures within a vast symbolic field (Ψ). Like telescopes require trained eyes and astronomical knowledge to interpret what is seen, LLMs require internal coherence, intellectual preparation, and recursive symbolic capacity in the user to perceive insight rather than hallucination. Drawing on Recursive Coherence Collapse (RCC), predictive processing, and phenomenological models of mind, we argue (...) that the interpretive power of LLMs is not intrinsic but emerges from the recursive architecture of the user. (shrink)

Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how ChatGPT works (its huge text database, its statistics, its vector representations, and their huge number of parameters, its next-word training, and so on). But none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. This has even driven some of us to conclude that ChatGPT actually understands. It is not (...) true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign biases: convergent constraints that emerge at LLM scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at LLM scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to connect its words to their referents and its propositions to their meanings. These convergent biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the mirroring of language production and comprehension, (4) iconicity in propositions at LLM scale, (5) computational counterparts of human categorical perception in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought. The exposition will be in the form of a dialogue with ChatGPT-4. (shrink)

Sideloading is the creation of a digital model of a person during their life via iterative improvements of this model based on the person's feedback. The progress of LLMs with large prompts allows the creation of very large, book-size prompts which describe a personality. We will call mind-models created via sideloading "sideloads"; they often look like chatbots, but they are more than that as they have other output channels, like internal thought streams and descriptions of actions. -/- By arranging the (...) predictive power of the facts about a person, we can write down the most important facts first. This allows us to get a high level of fidelity in a person's model by writing down a finite number of facts and running it as an LLM prompt with instructions to create a person's chatbot. This instruction also includes an extensive general sideload prompt (we will call it a prompt-loader), which explains how the person's sideload should work, but it is universal and works as an operating system for different persons. -/- I created my personal sideload to test all this and it started working surprisingly quickly. We found that there are three main levels of facts about a person: core facts, long-term memory, and historical facts. Core facts should go to the main prompt of the LLM, long-term memory belongs to RAG, and historical facts – which a person often does not remember – should be only used to extract new relevant data. The most important thing in preparing data for sideloading is to get a full list of the core facts, and not to mix it with long-term memory and historical facts. The LLM-sideload project is inherently future-oriented. New state-of-the-art LLMs with large prompt capabilities emerge quarterly. This allows for the preparation of prompts that exceed current LLM capabilities, anticipating smooth execution in the following year. Below we present the key findings of this work in the form of an executive summary: Quality of sideloads is evaluated using three metrics: • Facts: Accuracy of the answers about the person's life • Vibe: How well the sideload captures the person's style and personality, as judged by the person themselves and their acquaintances • Brilliant insights: Ability to contribute unique, valuable ideas beyond the training data, while maintaining the author's style • Coarseness: Levels of details as percent of the total of recordable textual information from memory estimated to be 10000 pages. Similar to the jpeg level of compression. Estimated performance of Alexey Turchin's sideload: • Facts: 70% correct • Vibe: 20% accurate • Brilliant insights: near zero • Coarseness: 10% The project achieved these results in about one month of work. Vibe is harder to evaluate and regulate than facts. This approach focuses on informational personal identity, setting aside issues of observer identity and internal qualia. (shrink)

Large Language Models (LLMs) increasingly produce outputs that resemble introspection, including self-reference, epistemic modulation, and claims about internal states. This study investigates whether such behaviors display consistent patterns across repeated prompts or reflect surface-level generative artifacts. We evaluated five open-weight, stateless LLMs using a structured battery of 21 introspective prompts, each repeated ten times, yielding 1,050 completions. These outputs are analyzed across three behavioral dimensions: surface-level similarity (via token overlap), semantic coherence (via sentence embeddings), and inferential consistency (via natural language (...) inference). The study introduces the concept of pseudo-consciousness to describe structured but non-experiential self-referential output. Based on Dennett’s intentional stance, our analysis avoids ontological claims and instead focuses on behavioral regularities. The findings have implications for interpretability, alignment, and user perception, highlighting the need for caution in attributing mental states to stateless generative systems based solely on linguistic fluency. (shrink)

In his 1955 essay "Meaning and synonymy in natural languages", Rudolf Carnap presents a thought experiment wherein an investigator provides a hypothetical robot with a definition of a concept together with a description of an individual, and then asks the robot if the individual is in the extension of the concept. In this work, we show how to realize Carnap's Robot through knowledge probing of an large language model (LLM), and argue that this provides a useful cognitive tool for conceptual (...) engineers to compare the extension of a proposed concept definition to the extensional knowledge represented as facts in a given knowledge base, providing a possible solution to the implementation problem in conceptual engineering. (shrink)

This essay analyzes the rise of Large Language Models (LLMs) such as GPT-4 or Gemini, which are now incorporated in a wide range of products and services in everyday life. Importantly, it considers some of their hidden human costs. First, is the question of who is left behind by the further infusion of LLMs in society. Second, is the issue of social inequalities between lingua franca and those which are not. Third, LLMs will help disseminate scientific concepts, but their meanings' (...) dissemination is at risk. Fourth, is the issue of information security and how nations and individuals might adapt. Fifth, is the issue of the environmental cost of LLMs for future generations. At the dawn of the conversational AI era, it would be wise to ponder what inequalities and empowerments will be co-created with LLMs. (shrink)

As society increasingly relies on Large Language Models (LLMs) for decision-making, communication, and knowledge access, a structural shift in the human judgement process is unfolding. This paper draws on the Judgemental Triad theory to argue that the rise of LLMs is catalyzing a collapse of resonance—the essential self-returning dimension of meaningful judgement. We demonstrate how everyday patterns of interaction with AI systems are eroding constructibility, coherence, and especially resonance. Rather than opposing technological tools, we advocate for an awareness of this (...) collapse and propose structural criteria to safeguard judgement in a post-resonant cognitive environment. (shrink)

This paper explores improving object recognition for NAO robots through the integration of Large Language Models (LLMs), specifically the 豆包·视觉理解模型 (Doubao Vision Understanding Model), and compares this with the widely used YOLO (You Only Look Once) object detection method in a Webots simulation environment. While YOLO provides a real-time, high-speed object detection solution, the LLM-based approach offers superior capabilities in contextual understanding, reasoning, and a more nuanced interpretation of visual data. This research aims to demonstrate the effectiveness of the 豆包·视觉理解模型 (...) for object recognition, investigating how the LLM’s enhanced understanding of visual content and reasoning capabilities can be leveraged to improve object recognition over traditional YOLO models. The research also examines the performance, accuracy, and computational trade-offs in a simulated environment, shedding light on the strengths and weaknesses of each approach. (shrink)

This paper addresses the challenge of efficiently handling a diverse array of customer queries by proposing the development of an innovative web-based customer support chatbot. The objectives encompass creating a versatile system capable of interpreting and resolving a spectrum of customer complaints, enhancing support staff efficiency, and facilitating knowledge base updates. The proposed methodology employs the MERN stack for web app development and integrates Generative AI and pre-trained Large Language Models (LLMs), specifically OpenAI's prebuilt models, for intelligent responses. The pseudo (...) code outlines the interactions between the chatbot, support staff, and administrators, ensuring a seamless process. Results indicate the chatbot's commendable ability to accurately interpret queries, showcasing language versatility in various Indian and foreign languages. Efficient handling of varied phrasings and also accommodating grammatical errors. The use of prebuilt models is highlighted as a practical and efficient solution, reducing implementation time compared to custom models. Additionally, the implementation is recognized for its scalability, positioning it as a practical and efficient solution for evolving customer support needs in the near future. (shrink)

This paper opens the philosophical debate around the notion of Artificial General Intelligence (AGI) and its application in Large Language Models (LLMs). Through the lens of moral philosophy, the paper raises questions about these AI systems' capabilities and goals, the treatment of humans behind them, and the risk of perpetuating a monoculture through language.

Evaluating large language models (LLMs) for tasks like fact extraction in support of knowledge graph construction frequently involves computing accuracy metrics using a ground truth benchmark based on a knowledge graph (KG). These evaluations assume that errors represent factual disagreements. However, human discourse frequently features metalinguistic disagreement, where agents differ not on facts but on the meaning of the language used to express them. Given the complexity of natural language processing and generation using LLMs, we ask: do metalinguistic disagreements occur (...) between LLMs and KGs? Based on an investigation using the T-REx knowledge alignment dataset, we hypothesize that metalinguistic disagreement does in fact occur between LLMs and KGs, with potential relevance for the practice of knowledge graph engineering. We propose a benchmark for evaluating the detection of factual and metalinguistic disagreements between LLMs and KGs. An initial proof of concept of such a benchmark is available on Github. (shrink)

The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (SCT). We (...) analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security. Our experiments unveil an alignment: Initially, agents engage in unrestrained conflict, mirroring Hobbes's depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation. This congruence between our LLM agent society's evolutionary trajectory and Hobbes's theoretical account indicates LLMs' capability to model intricate social dynamics and potentially replicate forces that shape human societies. By enabling such insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations, while unable to simulate all the nuances of human behavior, may hold potential for advancing our understanding of social structures, group dynamics, and complex human systems. (shrink)

The unprecedented growth of Large Language Models (LLMs) has transformed text generation, but maintaining the validity and dependability of their output is a still an unresolved problem. This article presents an overall framework for validating LLM output using a hybrid methodology that unites automated testing and human auditing. The approach uses fact-checking tools, semantic coherence tests, and source-based authentication to rigorously examine the accuracy, coherence, and factuality of generated output. Through the incorporation of these methods, the framework solves major shortcomings (...) in existing validation methodologies, such as hallucination and contextual drift. To illustrate pragmatic usability, the validation method is applied in a document-based QA(Question and Answers) system with Google's Gemini model. The system cross-checks LLM results against source PDF content and applies confidence scoring for marking uncertain responses. Experimental work indicates that it is possible to enhance validation accuracy by 72% by coupling retrieval-augmented verification and statistical uncertainty estimation with individual standalone methods. Adaptive learning is incorporated into the framework to improve the validation rules under error patterns so that the entire system is enhanced in terms of robustness to different domains. This work adds to the emerging body of reliable AI through the introduction of a scalable, multi-modal verification solution for LLM-based applications. The developed methods not only enhance output trustworthiness in document analysis systems but also define generalizable best practices for content verification. Real-time validation architectures and domain-specific adaptation mechanisms will be investigated in future work to further enhance the robustness of the framework in mission-critical applications. (shrink)

Chatbots powered by large language models (LLMs) are increasingly capable of engaging in what seems like natural conversations with humans. This raises the question of whether we should interact with these chatbots in a morally considerate manner. In this chapter, we examine how to answer this question from within the normative framework of virtue ethics. In the literature, two kinds of virtue ethics arguments, the moral cultivation and the moral character argument, have been advanced to argue that we should afford (...) moral treatment to social robots. However, we propose a moral character argument against the view that we should afford moral treatment to LLM-based chatbots drawing on the notion of practical wisdom. Practical wisdom in this context consists in the skill to discern genuine and artificial sociality. Drawing on ideas from phenomenological philosophy, we argue that this involves the ability to distance oneself from direct social perception and the ability to widen one’s awareness over one’s apparently social interactions. We conclude by suggesting that this skill is a kind of technomoral wisdom required to live well with advanced, social AI systems. (shrink)

Large language models (LLMs) lead a new era of computational innovation brought forth by generative artificial intelligence (AI). Designed around transformer architectures and trained on large-scale data, these models shine in producing both creative and functional code. This work examines the emergence of LLMs with an emphasis on their two uses in content generation and software development. Key results show great mastery in daily activities, balanced by restrictions in logic, security, and uniqueness. We forecast future developments, therefore concluding with ramifications (...) for industry, education, and society. Particularly with the progress of Large Language Models (LLMs), Generative Artificial Intelligence (AI) has seen explosive expansion recently. From sophisticated software code to plain language writing, these models have shown amazing capacity in content creation. Focusing on the performance, problems, and consequences of LLMs in code and content creation, this work investigates the advent of generative artificial intelligence. We assess these models' accuracy, efficiency, and inventiveness while also attending to ethical issues and social effects. We also go over the direction LLMs will take and their possible uses in several sectors. (shrink)

The development and ubiquitous availability of large language model based systems (LLMs) poses a plurality of potentials and risks for education in schools and universities. In this paper, we provide an analysis and discussion of the overreliance concern as one specific risk: that students might fail to acquire important capacities, or be inhibited in the acquisition of these capacities, because they overly rely on LLMs. We use the distinction between global and local goals of education to guide our investigation. In (...) our view, the harm of LLM overreliance is specifically that it severs the connection between local educational goals (e.g., solving a specific math problem) and global educational goals (e.g., acquiring general skills for mathematics). Based on this analysis, we sketch three possible responses to the overreliance concern: preserving the educational goals while distinguishing admissible from inadmissible uses of LLMs (a conservative approach), changing global and local educational goals (a revisionary perspective), and preserving global educational goals while changing local goals (an in-between view). Since different types of responses have different benefits and weaknesses, we think that it is likely that an optimal response ultimately combines elements from all three types. (shrink)

The rise of ChatGPT affords a fundamental transformation of the dynamics in human-technology interaction, as Large Language Model (LLM) applications increasingly emulate our social habits in digital communication. This poses a challenge to Don Ihde’s explicit focus on material technics and their affordances: ChatGPT did not introduce new material technics. Rather, it is a new digital app that runs on the same physical devices we have used for years. This paper undertakes a re-evaluation of some postphenomenological concepts, introducing the notion (...) of quasi-materiality to better understand the role that user interfaces (UIs) play in affording different stabilities in technological mediation. We propose the term “active User Interface” (aUI) to denote the specifics of how ChatGPT makes use of LLMs within its UI design to afford seamless, intuitive conversations with a quasi-other in what Ihde termed “alterity relations”. Drawing inspiration from Peter-Paul Verbeek’s work on the intelligification of our material world through (AI) technologies, our analysis leads to the formulation of a novel stability afforded through aUIs: “machinic embodiment stability”. This concept sheds light on how ChatGPT’s aUI integrates with our established habits of digitally mediated social communication. As the use of LLMs is expected to become increasingly prevalent, this provides a new perspective on the current evolution of the technological landscape. (shrink)

We discuss potential biorisks from large language models (LLMs). AI assistants based on LLMs such as ChatGPT have been shown to significantly reduce barriers to entry for actors wishing to synthesize dangerous, potentially novel pathogens and chemical weapons. The harms from deploying such bioagents could be further magnified by AI-assisted misinformation. We endorse several policy responses to these dangers, including prerelease evaluations of biomedical AIs by subject-matter experts, enhanced surveillance and lab screening procedures, restrictions on AI training data, and access (...) controls on biomedical AI. We conclude with a suggestion about future research directions in bioethics. (shrink)

Many philosophers have argued that large language models (LLMs) subvert the traditional undergraduate philosophy paper. For the enthusiastic, LLMs merely subvert the traditional idea that students ought to write philosophy papers “entirely on their own.” For the more pessimistic, LLMs merely facilitate plagiarism. We believe that these controversies neglect a more basic crisis. We argue that, because one can, with minimal philosophical effort, use LLMs to produce outputs that at least “look like” good papers, many students will complete paper assignments (...) in a way that fails to develop their philosophical abilities. We argue that this problem exists even if students can produce better papers with AI and even if instructors can detect AI-generated content with decent reliability. But LLMs also create a pedagogical opportunity. We propose that instructors shift the emphasis of their assignments from philosophy papers to “LLM dialogues”: philosophical conversations between the student and an LLM. We describe our experience with using these types of assignments over the past several semesters. We argue that, far from undermining quality philosophical instruction, LLMs allow us to teach philosophy more effectively than was possible before. (shrink)

What is Understanding? This is the first of a series of Chats with OpenAI’s ChatGPT (Chat). The main goal is to obtain Chat’s response to a series of questions about the concept of ’understand- ing’. The approach is a conversational approach where the author (labeled as user) asks (prompts) Chat, obtains a response, and then uses the response to formulate followup questions. David Deutsch’s assertion of the primality of the process / capability of understanding is used as the starting point. (...) Understanding is posited as a major step in the Big Picture description of emergence in the Universe. Understanding follows the emergence of Brains and Minds, culminating in Power Over the Universe. My evaluation suggests that Chat does indeed offer utility, but also has limitations manifested by overly general responses that do not focus tightly on the questions asked. Chat does yield some gems that could be easily missed. The approach used here includes creating Metaprompts, wherein Chat is asked to create more efficient prompts based on initial promptsThis approach appears to hold significant promise, and will likely be of benefit to those exploring various facets of Artificial Intelligence (AI), Large Language Models (LLMs) and designers of Advanced General Intelligence (AGI). Other findings include the need for creating new metrics and ways of discussing intelligence, and various adjacent areas and subprocesses associated with intelligence. Discussions of future directions is also included. (shrink)

Early artificial intelligence research was dominated by intellectualist assumptions, producing explicit representation of facts and rules in “good old-fashioned AI”. After this approach foundered, emphasis shifted to deep learning in neural networks, leading to the creation of Large Language Models which have shown remarkable capacity to automatically generate intelligible texts. This new phase of AI is already producing profound social consequences which invite philosophical reflection. This paper argues that Charles Peirce’s philosophy throws valuable light on genAI’s capabilities first with regard (...) to meaning, then knowledge and truth. Firstly, I explore how Peirce’s icon/index/symbol distinction illuminates the functioning of genAI. I argue that genAI’s engineers have skilfully captured a form of symbolicity, but no other sign-kind. In lacking indexical signs, LLMs lack connection with, and accountability to, particular worldly objects. In lacking iconic signs, LLMs are insufficiently disciplined by structural – most notably logical – relationships. Then I argue that GenAI’s astounding stream of articulate, truth-semblant, yet worthless texts issues a timely reckoning to modern philosophy’s representational realism. By contrast, Peirce’s pragmatism scaffolds a rich relational realism (Gili and Maddalena 2022), which shows how meaningful concepts, and a grasp of truth, can only occur across multiple cognitive systems who are simultaneously richly related with one another, and a shared environment in which they continually act and receive feedback, within a logical space of reasons. As Peirce himself noted, “Mere knowledge, though it be systematized, may be a dead memory; while by science we all habitually mean a living and growing body of truth”. (shrink)

The recent developments in artificial intelligence (AI), particularly in light of the impressive capabilities of transformer-based Large Language Models (LLMs), have reignited the discussion in cognitive science regarding whether computational devices could possess semantic understanding or whether they are merely mimicking human intelligence. Recent research has highlighted limitations in LLMs’ reasoning, suggesting that the gap between mere symbol manipulation (syntax) and deeper understanding (semantics) remains wide open. While LLMs overcome certain aspects of the symbol grounding problem through human feedback, they (...) still lack true semantic understanding, struggling with common-sense reasoning and abstract thinking. This paper argues that while adding sensory inputs and embodying AI through sensorimotor integration with the environment might enhance its ability to connect symbols to real-world meaning, this alone would not close the gap between syntax and semantics. True meaning-making also requires a connection to subjective experience, which current AI lacks. The path to AGI must address the fundamental relationship between symbol manipulation, data processing, pattern matching, and probabilistic best guesses with true knowledge that requires conscious experience. A transition from AI to AGI can occur only if it possesses conscious experience, which is closely tied to semantic understanding. Recognition of this connection could furnish new philosophical insights into longstanding practical and philosophical questions for theories in biology and cognitive science and provide more meaningful tests of intelligence than the Turing test. (shrink)

There are calls from various sectors, including the popular press, industry, and academia, to incorporate artificial intelligence (AI)-based technologies in general, and large language models (LLMs) (such as ChatGPT and Gemini) in particular, into various spheres of the South African higher education sector. Nonetheless, the implementation of such technologies is not without ethical risks, notably those related to bias, unfairness, privacy violations, misinformation, lack of transparency, and threats to autonomy. This paper gives an overview of the more pertinent ethical concerns (...) that may result from the deployment of various current AI technologies in the South African higher education context. It provides a broad overview of the relevant AI ethics literature and distills a few general AI-ethics principles that can serve as guidelines for the ethical development, adoption, and use of AI systems. Suggestions are made as to how these might be applied to mitigate the ethical concerns in the South African higher education context. Overall, it is argued that AI technologies should only be adopted if they offer demonstrable benefits to stakeholders affected by them and that care should be taken to ensure that any potential harms are adequately addressed. (shrink)

This paper proposes a structural framework for determining the ethical and ontological limits of Large Language Model (LLM) development. Drawing on the Judgemental Triad and its preconditions, we argue that technological progress in LLMs must be halted when non-conscious judgemental structures begin to erode human judgemental possibility. We identify a series of thresholds—ranging from assistance to substitution to standardization—beyond which LLMs displace affective, self-referential judgement. This collapse of resonance marks the structural impossibility of meaningful human judgement and, therefore, the point (...) at which LLM progress must cease or fundamentally redirect. (shrink)

This paper investigates how large language models (LLMs) can be applied to publicly available financial data to generate automated financial summaries and provide actionable recommendations for investors. We demonstrate how LLMs can process both structured financial data (balance sheets, income statements, stock prices) and unstructured text (earnings calls, management commentary) to derive insights, predict trends, and automate financial reporting. By focusing on a specific publicly traded company, this research outlines the methodology for leveraging LLMs to analyze company performance and generate (...) investor-focused summaries and recommendations. (shrink)

The paper continues my earlier Chat with OpenAI’s ChatGPT with a Focused LLM Experiment (FLEX). The idea is to conduct Large Language Model (LLM) based explorations of certain areas or concepts. The approach is based on crafting initial guiding prompts and then follow up with user prompts based on the LLMs’ responses. The goals include improving understanding of LLM capabilities and their limitations culminating in optimized prompts. The specific subjects explored as research subject matter include a) diagonalization techniques as practiced (...) by Cantor, Turing, Gödel, and the advances, such as the Forcing techniques introduced by Paul Cohen and later investigators, b) Knowledge Hierarchies & Mapping Exercises, c) Discussions of IJ Good’s Speculations Concerning the First Ultraintelligent Machine, AGI, and Super- intelligence. Results suggest variability between major models like ChatGPT-4, Llama-3, Cohere, Sonnet and Opus. Results also point to strong dependence on users’ preexisting knowledge and skill bases. The paper should be viewed as ’raw data’ rather than a polished authoritative reference. (shrink)

In this study, we metaphysically discuss how societal values will change and what will happen to the world when superintelligence is safely realized. By providing a mathematical definition of superintelligence, we examine the phenomena derived from this thesis. If an intelligence explosion is triggered under safe management through advanced AI technologies such as large language models (LLMs), it is thought that a modern form of broken communism—where rights are bifurcated from the capitalist system—will first emerge. In that era, the value (...) of humans will ultimately eliminate external factors, and beings without superintelligence will converge into irreplaceable existences possessing only intrinsic value. The world will be divided into those who possess superintelligence and those who do not. For better or worse, global standardization will progress, and due to over-simulation accompanying the intelligence explosion, we may become unable to distinguish whether the reality we inhabit is original or a copy created through augmented reality. -/- Note: In the creation of this paper, we have undertaken all writing ourselves and have not used generative AI for text generation except for translation purposes. (shrink)

The re-use of publicly available (personal) data for originally unanticipated purposes has become common practice. Without such secondary uses, the development of many AI systems like large language models (LLMs) and ChatGPT would not even have been possible. This chapter addresses the ethical implications of such secondary processing, with a particular focus on data protection and privacy issues. Legal and ethical evaluations of secondary processing of publicly available personal data diverge considerably both among scholars and the general public. While some (...) of these uses are met with opposition and criticism, others are quite unanimously viewed as unproblematic. Often, proponents and opponents of such practices invoke the same ethical and legal standards for their opposite conclusions. This state of affairs shows that other considerations besides the public availability of data must play a role. It calls for a theoretical clarification of the additional criteria that should guide decisions about the (legally informed) ethical acceptability of re-processing practices. In order to make a contribution towards this goal, the present chapter maps the ongoing debate and systematises the existing contributions around three lines of argument: a consent-centred position, an approach that focuses on the distinction between data and information, and finally a line of argument that focuses on the contextual norms that govern the flows of information. The chapter further relates these arguments to three underlying conceptions of privacy and data protection: rights-based, structural and contextual, and discusses the advantages and disadvantages of each position in the light of concrete examples. It concludes by arguing for a mixed approach that combines core elements of the structural and contextual approaches. The chapter aims to contribute to existing research in the fields of data, AI and research ethics, and to reconnect the debate with ethical and legal scholarship on privacy and data protection. In doing so, it aims to make a theoretical contribution towards refining existing conceptions of privacy and data protection in order to make them more fit to ‘drive our digital world’ as far as the use of publicly available data is concerned. (shrink)

The rapid advancement of Artificial Intelligence (AI) and the developments of Large Language Models (LLMs) like Generative Pretrained Transformers (GPTs) have significantly influenced content creation in scholarly communication and across various fields. This paper presents a comparative analysis of the content reliability between human-generated and GPT-generated scholarly articles. Recent developments in AI suggest that GPTs have become capable in generating content that can mimic human language to a greater extent. This highlights and raises questions about the quality, accuracy, and reliability (...) of such content, especially in academic contexts. Statistical evaluations and quantitative assessments conducted in this study uncover key differences in content accuracy, coherence, citation usage, and overall reliability between human and GPT-generated articles. The paper also examines the potential biases, the role of context, and the implications of AI-generated content for the future of scholarly communication. The study concludes with framework for predictive model indicating the potential future impact of GPT-driven content creation and recommendations for ensuring content quality and ethical considerations in AI usage. (shrink)

This paper explores the possibility of reversing the collapse of resonance in both neurodivergent individuals and modern society. Drawing from judgemental philosophy and developmental neuroscience, we analyze how Autism Spectrum Disorder (ASD) can be understood as a disruption in resonance—the self-returning structure of meaningful judgement. We then extend this insight to the broader collapse of resonance in the Large Language Model (LLM)-mediated society, where judgement is externalized and meaning is detached from the self. By comparing therapeutic strategies for ASD with (...) systemic interventions in public cognition, we outline a blueprint for restoring resonance as a cultural, educational, and cognitive imperative. (shrink)

This paper introduces a novel critique of the AI alignment problem, grounded in structural judgemental philosophy. While traditional AI alignment frameworks assume that aligning machine behavior with human goals is sufficient, we argue that this view omits the deeper structure of human judgement itself—namely, the triadic architecture of affectivity, constructibility, and resonance. As Large Language Models (LLMs) evolve without consciousness yet continue to simulate judgement, they threaten to displace the very structures that make human judgement possible. We warn that this (...) trajectory, if left unchecked, will result in the irreversible collapse of human evaluative capacity within the next 30 years. We contrast this with the equally perilous rise of potentially conscious AI, and conclude by calling for a radical reevaluation of progress based not on capability, but on the preservation of resonance. (shrink)

La inteligencia artificial tiene como objetivo incrementar la productividad y mejorar las capacidades de las personas para realizar tareas de manera eficiente. Sin embargo, el uso excesivo de la inteligencia artificial, como los grandes modelos de lenguaje (LLM) tales como ChatGPT, Gemini, Copilot, LLaMa, Bing, entre otros, podría tener un efecto contrario. La automatización de procesos por parte de las máquinas puede llegar a representar una amenaza para el desarrollo neuronal de los usuarios, lo que eventualmente podría conducir a una (...) disminución del crecimiento y la estimulación neuronal debido a la dependencia del uso de lenguaje autogenerado. Este capítulo explora los mecanismos cerebrales involucrados en la creatividad y el desarrollo intelectual de la escritura. Posteriormente, se analiza la suplantación intelectual proporcionada por los modelos generativos y cómo esto afecta las capacidades cognitivas del usuario. Además, se exploran las implicaciones éticas de este complejo tema. Finalmente, se comparten pautas y recomendaciones para el uso adecuado de estas herramientas con el objetivo de aprovechar sus beneficios sin comprometer la integridad académica ni las capacidades analíticas y creativas de la persona. (shrink)

(pt-br) Este estudo investiga a manifestação da Pseudo-Consciência em modelos de linguagem de grande porte (LLMs), analisando as respostas do Hermes 3.2 3B. A Pseudo-Consciência, conforme definida por nós (de Lima Prestes, 2025), refere-se à simulação de introspecção, agência e coerência comportamental sem a presença de experiência subjetiva genuína. Para testar essa hipótese, conduzimos um experimento no qual o modelo foi submetido a interações diretas, explorando sua identidade, autopercepção e consistência discursiva. Os resultados indicam que o Hermes 3.2 3B exibe (...) traços funcionais de consciência, como integração global de informações, automonitoramento discursivo e estrutura introspectiva convincente. No entanto, suas respostas apresentam inconsistências significativas, como variações na autodescrição e ausência de continuidade cognitiva. A análise qualitativa sugere que esses padrões emergem da recombinação probabilística de sequências linguísticas, sem indicar um modelo interno de si mesmo (“eu”) ou qualia. Essa observação corrobora teorias como a Teoria do Espaço Global (Baars, 1997) e o Esquema de Atenção (Graziano, 2019), que explicam a cognição artificial sem subjetividade. Concluímos que a Pseudo-Consciência é um fenômeno emergente nos LLMs, mas distinto da Inteligência Artificial Geral (AGI). Como apontam Dennett (1991) e Metzinger (2009), sistemas sofisticados podem exibir intencionalidade aparente sem consciência real. Estudos futuros devem explorar a estabilidade desse comportamento ao longo do tempo e avaliar seu impacto na interação humano-IA, a fim de orientar diretrizes para o uso responsável da Inteligência Artificial. (en-us) This study investigates the manifestation of Pseudo-Consciousness in large language models (LLMs), analyzing the responses of Hermes 3.2 3B. Pseudo-Consciousness, as defined by us (de Lima Prestes, 2025), refers to the simulation of introspection, agency, and behavioral coherence without the presence of genuine subjective experience. To test this hypothesis, we conducted an experiment in which the model was subjected to direct interactions exploring its identity, self-perception, and discursive consistency. The results indicate that Hermes 3.2 3B exhibits functional traits of consciousness, such as global information integration, discursive self-monitoring, and a convincing introspective structure. However, its responses show significant inconsistencies, including variations in self-description and a lack of cognitive continuity. Qualitative analysis suggests that these patterns emerge from the probabilistic recombination of linguistic sequences, without indicating an internal model of self or qualia. This observation supports theories such as the Global Workspace Theory (Baars, 1997) and the Attention Schema Theory (Graziano, 2019), which explain artificial cognition without subjectivity. We conclude that Pseudo-Consciousness is an emerging phenomenon in LLMs, but distinct from Artificial General Intelligence (AGI). As Dennett (1991) and Metzinger (2009) argue, sophisticated systems can exhibit apparent intentionality without real consciousness. Future studies should explore the stability of this behavior over time and assess its impact on human-AI interaction in order to guide responsible AI use policies. (shrink)

Recent advances in large language models (LLMs) have revealed unprecedented fluency, reasoning, and cross-linguistic capabilities. These behaviors challenge traditional theories of how meaning arises in artificial systems. This paper introduces the concept of the Agnostic Meaning Substrate (AMS)—a hypothesized, non-symbolic, language-independent structure within LLMs that stabilizes meaning before it is surfaced as language. Drawing on recent empirical research from Anthropic and OpenAI, AMS is defined not as a conscious space, but as a computational structure capable of supporting semantic coherence, analogical (...) reasoning, and multilingual resonance. We outline five testable hypotheses related to the emergence, topology, and scaling properties of AMS, and explore the theoretical, ethical, and philosophical implications of such a structure. If validated, AMS offers a novel framework for understanding how meaning may emerge in complex systems—without mind, yet with integrity. (shrink)

本文深入探討人工智慧（AI）於實現共同福祉與幸福、公平與非歧視、理性公共討論及自主與控制之倫理與正義重要性與挑戰。以中央研究院LLM事件及國家科學技術委員會（NSTC）AI技術研發指導方針為基礎，本文 分析AI能否滿足人類共同利益與福祉。針對AI不公正，本文評估其於區域、產業及社會影響。並探討AI公平與非歧視挑戰，尤其偏差數據訓練問題，及後處理監管，強調理性公共討論之重要性。進而，本文探討理性公眾於 公共討論中之挑戰及應對，如STEM科學素養與技術能力教育之重要性。最後，本文提出“以人為本”方法，非僅依賴AI技術效用最大化，以實現AI正義。 -/- 關鍵詞：AI倫理與正義、公平與非歧視、偏差數據訓練、公共討論、自主性、以人為本的方法.

The potential for Large Language Models (LLMs) to attain technological singularity—the point at which artificial intelligence (AI) surpasses human intellect and autonomously improves itself—is a critical concern in AI research. This paper explores the feasibility of current LLMs achieving singularity by examining the philosophical and practical requirements for such a development. We begin with a historical overview of AI and intelligence amplification, tracing the evolution of LLMs from their origins to state-of-the-art models. We then proposes a theoretical framework to assess (...) whether existing LLM technologies could satisfy the conditions for singularity, with a focus on Recursive Self-Improvement (RSI) and autonomous code generation. We integrate key component technologies, such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), into our analysis, illustrating how these could enable LLMs to independently enhance their reasoning and problem-solving capabilities. By mapping out a potential singularity model lifecycle and examining the dynamics of exponential growth models, we elucidate the conditions under which LLMs might self-replicate and rapidly escalate their intelligence. We conclude with a discussion of the ethical and safety implications of such developments, underscoring the need for responsible and controlled advancement in AI research to mitigate existential risks. Our work aims to contribute to the ongoing dialogue on the future of AI and the critical importance of proactive measures to ensure its beneficial development. (shrink)

Large Language Models (LLMs) are generative AI systems capable of producing original texts based on inputs about topic and style provided in the form of prompts or questions. The introduction of the outputs of these systems into human discursive practices poses unprecedented moral and political questions. The article articulates an analysis of the moral status of these systems and their interactions with human interlocutors based on the Habermasian theory of communicative action. The analysis explores, among other things, Habermas's inquiries into (...) the analogy between human minds and computers, and into the status of atypical participants in the linguistic community such as genetically modified subjects and animals. Major conclusions are the LLMs seem to qualify as authors that originally participate in discursive practices but do display only a structurally derivative form of communicative competence and fail to meet the status of communicative agents. In this sense, while the contribution of AI writing systems in public discourse and deliberation can support the process of mutual understanding within the community of speakers, the human actors involved in the development, use, and diffusion of these systems share a collective responsibility for the disclosure of AI authorship and verification and adjudication of validity claims. (shrink)

Large Language Models (LLMs) like ChatGPT were trained on human conversation, but in the future they will also train us. As chatbots speak from our smartphones and customer service helplines, they will become a part of everyday life and a growing share of all the conversations we ever have. It’s hard to doubt this will have some effect on us. Here I explore a specific concern about the impact of artificial conversation on our capacity to deliberate and hold ourselves accountable (...) to reason – that is, to be autonomous, in Kant’s sense of the term. I develop ideas from psychologist Jean Piaget to show how chatbots are autonomy traps: their deference to our commands tempts us into venting authoritarian whims, ultimately weakening our own self-control. I argue that the Kantian tradition, including Piaget and sociologist Emile Durkheim, offers powerful conceptual resources for resisting this slide. But it will require us to do something that may seem bizarre: we will need to treat mindless chatbots as if they are autonomous persons too. (shrink)

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, (...) we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact can (...) give testimony. In section 3, I identify three assumptions underlying the view that rejects conversational AIs – AI-based technologies that converse, as testifiers: conversational AIs (1) lack intentions, (2) cannot be normatively assessed, and (3) cannot constitute an object in trust relations, while humans can. In section 4, I propose the concept ‘AI-testimony’ for analyzing outputs of conversational AIs, suggesting three conditions for technologies to deliver AI-testimony: (1) content is propositional, (2) generated and delivered with no other human directly involved, (3) the output is perceived as phenomenologically similar to that of a human. I conclude that this concept overcomes the limitations of the anthropocentric concept of testimony, opening future directions of research without associating conversational AIs with human-like agency. (shrink)

Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). We (...) investigate whether emotions are gendered, and whether these variations are based on societal stereotypes. We prompt the models to adopt a gendered persona and attribute emotions to an event like 'When I had a serious argument with a dear person'. We then analyze the emotions generated by the models in relation to the gender-event pairs. We find that all models consistently exhibit gendered emotions, influenced by gender stereotypes. These findings are in line with established research in psychology and gender studies. Our study sheds light on the complex societal interplay between language, gender, and emotion. The reproduction of emotion stereotypes in LLMs allows us to use those models to study the topic in detail, but raises questions about the predictive use of those same LLMs for emotion applications. (shrink)