In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Who Wrote This?: How AI and the Lure of Efficiency Threaten Human Writing by Naomi BaronLuke MunnBaron, Naomi. Who Wrote This?: How AI and the Lure of Efficiency Threaten Human Writing. Stanford University Press, 2023. 344pp.Who Wrote This? is Naomi Baron’s latest book exploring the emergence of AI language models and their potential implications for writing. A linguist, educator, and emeritus professor at American University, Baron should be (...) well placed to tease out the nuances of language, showcase the creativity of human literary canon across history, and deliver a set of findings and recommendations informed by deep experience. Indeed, since the release of ChatGPT in November 2022, there is a hunger for this kind of expertise and guidance both inside and outside academia, as the deluge of debates, panels, and forums in the past year have attested. This is the perfect storm, one that should be harnessed by a linguist with two decades of work on digital writing and reading into a powerful book. Instead, the reader must wade through a morass of truisms, historical anecdotes, and wiki-like summaries of research studies, interspersed with surface-level ponderings.The problems begin almost immediately with the structure of the book itself. While the book is ostensibly divided into chapters, each chapter is atomized into dozens of subsections, some no more than a paragraph or two in length. In this whirlwind tour, the reader is swiftly confronted with a jumble of quotes from journalists, jokes about language, historical events, a “laypersons AI roadmap,” and recent news stories, flitting from one to the next with barely a pause. The “AI Applications” section takes this same piecemeal approach, laying out the possible uses of artificial intelligence and then painstakingly stepping through each one. Each subsection features a few paragraphs that regurgitates material you might find, more cohesively and in-depth, in a Wikipedia article. The material on the history of artificial intelligence is handled simplistically and uncritically, skipping across a handful of heroic figures like Yann LeCun, Yoshua Bengio, and Fei-Fei Li.While anecdotes and quotes abound, an argument is much harder to locate. On the one hand, Baron seems to uncritically echo the AI hype that has dominated news headlines in the past year. “We’ve come a long way,” she opines, “we have vacuum cleaning robots, drones, and self-driving [End Page 156] cars” (59). The reader learns that AI can generate poetry, correct grammar, play games, and beat masters (AlphaGo), and even identify cancer (machine learning for mammograms). These use-cases, and the ambitious claims that accompany them, are never really challenged in any sustained theoretical or methodological way. On the other hand, Baron seems to be highly conservative when it comes to the proper domain of “the human” and of writing in particular. “AI isn’t up for self-expression or thinking” (14) she says in passing, dismissing the recent revolutionary improvements in large language models that she’s just hyped up.The possibility of machinic intelligence and creativity, in fact, is a fascinating if messy one. Turing was not so interested in demonstrably “proving” that computers could be intelligent, but instead (and very much in line with his everyday navigation of British sexual norms) in developing something that could “pass” as human. Intelligence was not an acid-test, but a set of gestures that could be performed or pantomimed, emulated until they were “good enough” to deceive. N. Katherine Hayles has continued this trajectory in her work on “non-conscious cognition,” removing the barriers to what qualifies as intelligence and subsequently extending cognition down to unicellular organisms and plants. Within my own work on language models alongside colleagues (Munn et al. 2023; Magee, Munn, and Arora 2023), we highlight how models like GPT 3.5 achieve an approximation of natural speech. What is important is not the authenticity or actual intelligence of this feat, but that this “good enough” ability means that users treat language models as an interlocutor, a listener, a subject. This projection, even if misplaced, opens up this interaction and requires a conceptualization that is at least as attuned to memory, affect, and subjectivity as software interfaces and linear... (shrink)

Can AI systems such as ChatGPT think? We present an argument from rationality for the negative answer to this question. The argument is founded on two central ideas. The first is that if ChatGPT thinks, it is not rational, in the sense that it does not respond correctly to its evidence. The second idea, which appears in several different forms in philosophical literature, is that thinkers are by their nature rational. Putting the two ideas together yields the result that ChatGPT (...) is not a thinker. The paper sets out the argument in detail and shows how it can withstand numerous objections. We end the paper by contrasting the argument from rationality with a more well-known argument often taken to show that ChatGPT doesn’t think, due to Bender and Koller. (shrink)

From Fall 2016 to Fall 2019 Marcelo Gleiser conducted a series of nine public dialogues between eminent scientists and humanists on challenging topics and ideas whose very definitions and meanings are disputed. Sponsored by the Institute for Cross-Disciplinary Engagement at Dartmouth College, founded by Gleiser, these events were held in theaters and universities across the US and immediately followed by workshops, open to the public, at which attendees could converse directly with participants. Great Minds Don't Think Alike collects edited versions (...) of these discussions, which set an example, in a time when public discourse is seriously threatened, of how people can engage in a fruitful exchange of ideas even when they strongly disagree. The cast of characters is stellar: David Chalmers and Antonio Damasio on consciousness, Sean Carroll and Alan Wallace on reality, Patricia Churchland and Jill Tarter on the future of intelligence, Rebecca Goldstein and Alan Lightman on faith, Jimena Canales and Paul Davies on time, Ed Boyden and Mark O'Connell on transhumanism, Elizabeth Kolbert and Siddhartha Mukherjee on immortality, and Jeremy DeSilva, David Grinspoon, and Tasneem Zehra Husain on being human. (shrink)

This edited volume presents perspectives from computer science, information theory, neuroscience and brain imaging, aesthetics, social sciences, psychiatry, and philosophy to answer frontier questions related to artificial intelligence and human experience. Can a machine think, believe, aspire and be purposeful as a human? What is the place in the machine world for hope, meaning and transformative enlightenment that inspires human existence? How, or are, the minds of machines different from that of humans and other species? These questions are responded to (...) along with questions in the intersection of health, intelligence and the brain. It highlights the place of consciousness by attempting to respond to questions with the help of fundamental reflections on human existence, its life-purposes and machine intelligence. The volume is a must-read for interdisciplinary and multidisciplinary researchers in humanities and social sciences and philosophy of science who wish to understand the future of AI and society. (shrink)

This paper makes the case for a sharper terminology regarding AIs cognitive abilities. In arguing that thinking requires more than content production, I offer a definition of meaning drawing on a clear distinction between living and machine intelligence. A pivotal argument is the re-use of the Turing Test (TT) for understanding which theories of meaning and consciousness are no longer plausible—because they have been reproduced by software without thereby gaining conscious experience. In following the few theories that have not (...) (yet) failed this reversed Turing Test (RTT), the focus turns towards rethinking the human condition in times of AI along the lines of three questions: What if a machine developed consciousness? What if AI proceeded without developing a consciousness? What, if machinic and human intelligence merged? These three questions in the end lead to examining three related possible futures of humanism as now determined by the relation between Human Intelligence and AI. (shrink)

It is generally assumed that existing artificial systems are not phenomenally conscious, and that the construction of phenomenally conscious artificial systems would require significant technological progress if it is possible at all. We challenge this assumption by arguing that if Global Workspace Theory (GWT) — a leading scientific theory of phenomenal consciousness — is correct, then instances of one widely implemented AI architecture, the artificial language agent, might easily be made phenomenally conscious if they are not already. Along the (...) way, we articulate an explicit methodology for thinking about how to apply scientific theories of consciousness to artificial systems and employ this methodology to arrive at a set of necessary and sufficient conditions for phenomenal consciousness according to GWT. (shrink)

This article argues that if panpsychism is true, then there are grounds for thinking that digitally-based artificial intelligence may be incapable of having coherent macrophenomenal conscious experiences. Section 1 briefly surveys research indicating that neural function and phenomenal consciousness may be both analog in nature. We show that physical and phenomenal magnitudes—such as rates of neural firing and the phenomenally experienced loudness of sounds—appear to covary monotonically with the physical stimuli they represent, forming the basis for an analog relationship (...) between the three. Section 2 then argues that if this is true and micropsychism—the panpsychist view that phenomenal consciousness or its precursors exist at a microphysical level of reality—is also true, then human brains must somehow manipulate fundamental microphysical-phenomenal magnitudes in an analog manner that renders them phenomenally coherent at a macro level. However, Sect. 3 argues that because digital computation abstracts away from microphysical-phenomenal magnitudes—representing cognitive functions non-monotonically in terms of digits—digital computation may be inherently incapable of realizing coherent macroconscious experience. Thus, if panpsychism is true, digital AI may be incapable of achieving phenomenal coherence. Finally, Sect. 4 briefly examines our argument’s implications for Tononi’s Integrated Information Theory theory of consciousness, which we contend may need to be supplanted by a theory of macroconsciousness as analog microphysical-phenomenal information integration. (shrink)

In the last several months, several major disciplines have started their initial reckoning with what ChatGPT and other Large Language Models (LLMs) mean for them – law, medicine, business among other professions. With a heavy dose of humility, given how fast the technology is moving and how uncertain its social implications are, this article attempts to give some early tentative thoughts on what ChatGPT might mean for bioethics. I will first argue that many bioethics issues raised by ChatGPT are similar (...) to those raised by current medical AI – built into devices, decision support tools, data analytics, etc. These include issues of data ownership, consent for data use, data representativeness and bias, and privacy. I describe how these familiar issues appear somewhat differently in the ChatGPT context, but much of the existing bioethical thinking on these issues provides a strong starting point. There are, however, a few “new-ish” issues I highlight – by new-ish I mean issues that while perhaps not truly new seem much more important for it than other forms of medical AI. These include issues about informed consent and the right to know we are dealing with an AI, the problem of medical deepfakes, the risk of oligopoly and inequitable access related to foundational models, environmental effects, and on the positive side opportunities for the democratization of knowledge and empowering patients. I also discuss how races towards dominance (between large companies and between the U.S. and geopolitical rivals like China) risk sidelining ethics. (shrink)

This article explores the challenges surrounding generative artificial intelligence (GenAI) in public administrations and its impact on human‒machine interactions within the public sector. First, it aims to deconstruct the reasons for distrust in GenAI in public administrations. The risks currently linked to GenAI in the public sector are often similar to those of conventional AI. However, while some risks remain pertinent, others are less so because GenAI has limited explainability, which, in return, limits its uses in public administrations. Confidentiality, marking (...) of GenAI outputs and errors are specific matters for which responses should be technical as well as cultural, as they are pushing the boundaries of our instrumental conceptions of machines. Second, this article proposes some paradigm shifts in the perspective of using GenAI in public administrations due to the radical change caused by its language-based nature. GenAI represents a profound break from the “numerical” nature of AI systems implemented in public administrations to date. The transformative impact of GenAI on the intellectual production of the state raises fears of the replacement, or rather enslavement, of civil servants to machines. The article argues for the development of critical thinking as a specific skill for civil servants who have become highly specialized and will have to think with a machine that is eclectic by nature. It anticipates a transformation in the political nature of public administrations, which should lead to more considerations for the strategic stake related to training corpus and for our conceptualization of the neutrality of AI. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:ChatGPT:a psychomachiaChristopher Norris (bio)The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question. On the contrary, the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute (...) correlations among data points but to create explanations.—Satya P. MohantyIt remains to be seen whether AI poems will eventually go beyond intended or unintended parody and trigger emotional responses in human readers that run deeper than wry amusement.... Current AI poets are in essence mining the metaphors that humans have already formed and planted in texts–in the view of Robert Frost, 'The richest accumulation of the ages is the noble metaphors we have rolled up." Could AI create genuinely novel metaphors, rather than only variations of those we humans have come up with already?—Keith HolyoakeNoteThis poem started out as a debate in clearly-marked dialogue form (alternating roman and italic print) between an advocate of ChatGPT who thinks that 'strong' AI is, or will soon be, capable of writing 'authentic' poetry, and a poet who resists that claim on various grounds, personal and philosophical. The opening, strict-form sonnet proposes that the issue be tested by running it (the poem itself) past the software and seeing what results as the sonnet-sequence unfolds from that speculative source-point. As it goes along, the sonnet-forms multiply, the contending viewpoints seem to merge or even change sides, the roman-italic alternation breaks down, and the project (theirs or mine?) loses its intended clarity of theme and treatment. I include the poem here as a kind of performative commentary on the way that thematic concerns or intentions may get lost once the AI software really starts doing its wondrous, intriguing, or humanly ominous thing.One way to test this thing, ChatGPT:Let's run a really strict-form poem on it,Like a Petrarchan or Shakespearean sonnet,Then input 'do thou likewise,' just to seeWhat comes out. Does the poem rhyme and scan [End Page 77] Correctly yet, as claimed, hit off the styleConvincingly, and thereby close the fileOn what's unique about the species 'man'?If so, you'll find it difficult to tellWhat's what with this example of the kind,Along with other cases you may findWorth putting to the online test as well.And don't get spooked if, like myself, you're proneTo think you're someone (something?) else's clone.You crease me up, you poets, with your fearsThat some new bit of software (albeit oneOf quite superior powers) might have outdoneYou in those self-apportioned special spheresOf excellence where you and some few peers,Each special, each unique, affect to shunAll thought that your fine poems may just runOn lines devised by AI gadgeteers.A nice idea, last refuge of the weakIn mind but good in heart, the valiant fewTo whose deluded faith it falls to holdOut still for what you fantasists may seekYet scarcely hope to find: new ways to doThe genius thing, transmute base stuff to gold!Read slowly, sensitively; don't permitYourself, dear reader, nobbled as no doubtYou've been by those who have the techno-clout,To take that stuff on board and let a bitOf pseudo-Keats, spoof Yeats, identikit'Stuffed-owl'-grade Wordsworth, or what else they toutAs the real deal demand you read withoutDiscerning what no moving finger's writ.Just hearken, let your quickened sense respondTo all that yet eludes the furthest reachOf AI artefacts. They had you connedAt first, though soon you missed the living speechUn-missed by those who cannot hear beyondWhat any tin-eared programmer could teach.Let's bring the reader in on this, I say.We've need of them, we disputants with much [End Page 78] At stake in different ways, they there to payOur... (shrink)

Educators are increasingly concerned with the use of artificial intelligence (AI) in student writing. Much of the concern focuses on the issue of students using ChatGPT to complete their work. I introduce the CUPID model for instructors to use when thinking about how to pedagogically handle ChatGPT. The CUPID model lays out five general approaches: Catch, Utilize, Prevent, Ignore, and Disincentivize. I suggest that instructors should especially consider using certain assignments that fall under the approach “Disincentivize”. Philosophy instructors in particular (...) should assign more low-stakes writing, such as journals and reflection pieces. The adoption of low-stakes writing assignments aims to discourage students from relying on AI while promoting clarity of thought and deeper engagement with philosophical ideas. (shrink)

We appreciate the helpful commentaries on our paper, ‘AI and the falling sky: interrogating X-Risk’.1 We agree with many points commentators raise, which opened our eyes to concerns we had not previously considered. This reply focuses on the tension many commentators noted between AI’s existential risks (X-Risks) and justice here and now. In ‘Existential risk and the justice turn in bioethics’, Corsico frames the tension between AI X-Risk and justice here and now as part of a larger shift within bioethics.2 (...) They think the field is increasingly turning away from ‘big picture’ questions new technologies raise and focusing on narrower justice concerns of less significance. They compare our paper’s emphasis on justly transitioning to more AI-centred societies with the approach of environmentalists fretting about human protection against climate change while losing sight of the need to protect the planet and all living things. Corsico doubts there is much point to pressing for ‘justice on a dead planet’, so too Corsico questions our concern with just transitions. Presumably, that matters little if intelligent life on Earth is destroyed. Corsico’s recommends bioethicists return to big questions, like: ‘Should we develop AI at all, given AI X-Risk?’. Yet this question is increasingly moot. The genie is already out of the bottle. The Future of Life Institute’s 2023 call for a temporary pause on training AI systems more powerful than ChatGPT fell on deaf ears, in part because freely accessible source codes enable anyone to train systems and create AI applications.3 The focus now must be managing the genie. While we take AI X-Risk seriously, …. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Consciousness and Machines:A Commentary Drawing on Japanese PhilosophyS. D. Noam Cook (bio)Viewed from within the great unity of consciousness, thinking is a wave on the surface of a great intuition.Kitarō NishidaIntroductionRecent developments in AI have made the long-standing debate about what computers can and can't do a major public concern. What we understand the properties of such machines to be, and consequently how we design [End Page (...) 305] and deploy them, will increasingly shape how we live and how we understand ourselves. Thus, there is an urgent need for better understanding of these issues.Susan Schneider, in her book Artificial You: AI and the Future of Your Mind, offers an exceptionally careful and insightful examination of key issues entailed in how we understand AI and ourselves. One of her central concerns is how we might test for machine consciousness. Given the variations in design and function among current and projected AIs, Schneider sees no likely one-size-fits-all test. So, she offers a battery of different tests that she believes together will make such testing more reliable. This seems a wise approach, and reflects the conceptual and pragmatic care Schneider takes throughout the book.Similarly, it seems to me that the question of machine consciousness should also include various understandings of consciousness itself. What consciousness is taken to be has varied widely both historically and culturally. No commonly accepted single understanding has emerged, and may never emerge. Just as having a battery of tests might help us better assess possible machine consciousness, having different understandings of consciousness might give us a better understanding of what we are testing for. The two should work in tandem, each gauging and shaping the other.Like the development of computers themselves, the predominant thinking about AI has been shaped, in part, by key presuppositions of a scientific worldview, with its origins in Western thought and culture. Among these presuppositions are materialism, reductionism, and a primary focus on objective behavior. These are reflected in the common concern with observable behavior of physical aspects of the brain in efforts to understand human consciousness, which in turn has become a familiar model for work on machine consciousness. All this is important and valuable work. But, in the spirit of Schneider's cautions, it should not be taken as one-size-fits-all. Accordingly, I offer one view of consciousness from a different perspective.In what follows, I sketch out an understanding of consciousness that draws on Japanese philosophy, particularly that of the highly influential early twentieth-century Japanese philosopher Kitarō Nishida (1870–1945). I then explore how the tests for machine consciousness that Schneider proposes might be viewed from that perspective.Consciousness: A Perspective from Japanese PhilosophyConsciousness was a central concern for Nishida, whose philosophy is deeply informed by Japanese culture and by Zen theory and practice. As one might expect from such a philosopher, Nishida's exploration of consciousness is not concerned with properties of the brain, but with what we might discern through disciplined reflection on the experience of our own consciousness. Ultimately, he views consciousness as an organic whole [End Page 306] of systemically organized interrelationships of activities and sensations within our subjective experience. This may be understood by first considering 'nothingness'.For Nishida, nothingness does not suggest an ultimate and absolute vacuity, as is often understandably assumed. In Nishida's sense, it is a singular "place" that mirrors the multifaceted world of our common experience. In understanding this, I am told by native Japanese speakers that the English word 'nothingness' has an advantage over the Japanese equivalent, 'mu', in that 'nothingness' can usefully be broken down as 'nothing-ness'. This suggests that nothingness is a state of consciousness where what we commonly experience as distinct (self, other, world, blue, ache, joy, etc.) is indistinct, a state of unified experience. This is consciousness without our finding or discriminating in it the myriad "things" of common experience, but rather a "pure experience" of a singular whole. Nishida introduces his concept of "pure experience" as follows:To experience means to know facts just as they are, to know in accordance with facts by completely relinquishing one... (shrink)

This essay comprises a first phenomenological semiotics of ChatGPT. I analyse how we experience the language signs generated by that AI. This task is accomplished in two steps. First, I introduce a conceptual scaffolding for the project, by introducing core tenets of Husserl's semiotics. Second, I mould Husserl's theory to develop my phenomenology of the passive and active consciousness of the language signs composed by ChatGPT. On the one hand, by discussing temporality, I demonstrate that ChatGPT can passively demand (...) me to understand its signs. On the other hand, I show that a conflict arises between active and passive consciousness when reading ChatGPT. While I actively know that there is no communicating subject, I still passively experience these signs as made by another. I argue that it is this conflict which lends ChatGPT its 'magical' character. I conclude by showing how these observations can inform future regulation of AI models. (shrink)

Nel 1956, in piena guerra fredda, una conferenza di scienziati al Dartmouth College negli Stati Uniti annunciò il lancio di un audace progetto scientifico, l’Intelligenza Artificiale (I.A.). Dopo l’iniziale fallimento degli sforzi della “Hard AI” di produrre un’intelligenza simile a quella umana, alla fine del XX secolo è emerso il movimento della “Soft AI”. Invece di essere orientato a imitare il comportamento umano in relazione a compiti specifici, ha preferito cercare modi alternativi di eseguire i compiti basati sulle particolari funzioni (...) e strutture della macchina. Il fattore decisivo è stata la combinazione della tecnologia di apprendimento automatico con le comunicazioni digitali online, l’incontro tra l’IA e Internet. Le applicazioni dell’IA “soft” sono quelle che vediamo nel mondo dei dispositivi automatici “intelligenti” che informano la nostra vita sociale digitale. Nel seguente articolo mi concentrerò su una particolare applicazione dell’IA per le discussioni online con utenti umani, ChatGPT, per mostrare le implicazioni della tecnologia digitale online nelle modalità storico-sociali dell’esistenza umana. ChatGPT è un’applicazione che combina l’IA con Internet, le possibilità della corrispondenza tra macchine e le potenzialità della tele-comunicazione umana. Il mio approccio si concentra sulle implicazioni fenomenologiche e storico-sociali della proliferazione della tecnologia AI per quanto riguarda la coscienza e la socialità umana. -/- Abstract (english) -/- In 1956, amidst the Cold War, a conference of scientists at Dartmouth College in the United States announced the launch of a bold, scientific project, Artificial Intelligence (A.I.). After the initial failure of “Hard AI” efforts to produce a human-like intelligence, the “Soft AI” movement emerged in the late 20th century. Instead of being oriented towards imitating human behaviour concerning specific tasks, it preferred to seek alternative ways of performing tasks based on the particular functions and structures of the machine. The decisive factor was the combination of Machine Learning technology with Digital Online Communications, the meeting between AI and the Internet. The applications of “Soft” AI are those we see in the world of “smart”, “intelligent” automatic devices that inform our digital social life. In the following article, I will focus on a particular application of AI for online discussions with human users, ChatGPT, in order to show the implications of online digital technology in the social-historical modalities of human existence. ChatGPT is an application that combines AI with the Internet, the possibilities of Machine correspondence with the potentialities of human telecommunications. My approach focuses on the phenomenological and social-historical implications of the proliferation of AI technology as regards human consciousness and sociality. (shrink)

In the era of generative AI and specifically large language models (LLMs), exemplified by ChatGPT, the intersection of artificial intelligence and human reasoning has become a focal point of global attention. Unlike conventional search engines, LLMs go beyond mere information retrieval, entering into the realm of discourse culture. Their outputs mimic well-considered, independent opinions or statements of facts, presenting a pretense of wisdom. This paper explores the potential transformative impact of LLMs on democratic societies. It delves into the concerns regarding (...) the difficulty in distinguishing ChatGPT-generated texts from human output. The discussion emphasizes the essence of authorship, rooted in the unique human capacity for reason—a quality indispensable for democratic discourse and successful collaboration within free societies. Highlighting the potential threats to democracy, this paper presents three arguments: the Substitution argument, the Authenticity argument, and the Facts argument. These arguments highlight the potential risks that are associated with an overreliance on LLMs. The central thesis posits that widespread deployment of LLMs may adversely affect the fabric of a democracy if not comprehended and addressed proactively and properly. In proposing a solution, we advocate for an emphasis on education as a means to mitigate risks. We suggest cultivating thinking skills in children, fostering coherent thought formulation, and distinguishing between machine-generated output and genuine, i.e., human, reasoning. The focus should be on the responsible development and usage of LLMs, with the goal of augmenting human capacities in thinking, deliberating and decision-making rather than substituting them. (shrink)

The integration of artificial intelligence into bioethics education represents a new pedagogical approach that addresses complex moral issues in healthcare. The use of AI-driven platforms like ChatGPT in bioethics education can enhance critical thinking and decision-making skills among students by providing a diverse range of perspectives and solutions. To assess the ability of ChatGPT-4 to understand and resolve ethical dilemmas using validated clinical case vignettes, thereby determining its suitability as a teaching aid in bioethics. Ten clinical scenarios, each with inherent (...) ethical issues, were presented to ChatGPT-4. These scenarios were expert-validated to ensure relevance and complexity. ChatGPT's responses were evaluated by two blinded external raters to eliminate bias. The assessment criteria included identification of bioethical principles, recognition of stakeholders and their outcomes, and comprehension of ethical and legal frameworks. Additionally, the quality of responses was scored on a scale of 1–10. ChatGPT showed proficiency in identifying the pertinent bioethical principles and stakeholders involved in the ethical dilemmas presented. It achieved an overall score of 84.5%, with high marks for its analytical capabilities in complex scenarios. However, the study identified areas for improvement, particularly in proposing comprehensive solutions and alternative actions. ChatGPT demonstrates potential as an effective tool for bioethics education, capable of guiding learners through the resolution of ethical issues in clinical practice. However, to fully integrate ChatGPT into bioethics education, it requires enhancements in delivering detailed, actionable guidance in ethical decision-making. Future development should focus on refining ChatGPT’s ability to offer nuanced solutions aligned with patient values and clinician needs. (shrink)

This article poses a simple question: can AI lie? In response to this question, the article examines, as its point of inquiry, popular AI chatbots, such as, ChatGPT. In doing so, an examination of the psychoanalytic, philosophical, and technological significance of AI and its complexities are located in relation to the dynamics of truth, falsity, and deception. That is, by critically exploring the chatbot’s capacity to engage in natural language conversations and deliver contextually relevant responses, it is argued that what (...) distinguishes the AI chatbot from anthropocentric discussions, which suggest a form of conscious awareness in AI, is the importance of the lie—an importance which a psychoanalytic approach can reveal. Indeed, while AI technologies undoubtedly blur the line between lies and truth-speaking, in the case of the AI chatbot, it is detailed how such technology remains unable to lie authentically; or, in other words, is unable to lie like a human. For psychoanalysis, the capacity to lie bears witness to the unconscious, and, thus, plays an important role in determining the subject. It is for this reason that rather than uncritically accepting the chatbot’s authority—an authority that is easily reflected in its honest responses and frank admissions—a psychoanalytic (Lacanian) perspective can highlight the significance of the unconscious as a distorting factor in determining the subject. To help elucidate this argument, specific attention is given to introducing and applying Lacan’s subject of enunciation and subject of the enunciated. This is used to assert that what continues (for now) to set us apart from AI technology is not necessarily our ‘better knowledge’, but our capability to consciously engage in acts of falsehood that function to reveal the social nuances and significances of the lie. (shrink)

An unbiased metaphysics of consciousness allows us to put forward a thesis that con­sciousness is not an object, but a fundamental problem of modern philosophy and differ­ent solutions of this problem define the history of European philosophy. For Descartes, consciousness is Cogito, the personalization of substance by the mind acting in a variety of cogital, conscious acts. Kant, for whom substance is a mode of thinking the unity of experience, Cogito is not a thing (Res), but a transcendental (...) condition for the forma­tion of notions in the process of categorical synthesis. The resurgent phenomenology and analytical philosophy of consciousness in the 21st century aim at studying consciousness, respectively, in the transcendental-theoretical and pragmatic-naturalistic settings. Contin­uing the Kantian line of thinking, one can see the processuality of consciousness as data acquisition process, but the verb formed from a noun “consciousness” (for this processu­ality) is difficult to adequately translate into English. To achieve completeness in identi­fying the problem area of the philosophy of consciousness, it is necessary to take into ac­count one more type of conscious activity referred to as “osoznanie” in Russian (“aware­ness”, “coming into consciousness about something”). The Russian verb formed from the noun Smysl cannot be adequately translated into English as the main language of the ana­lytical philosophy of consciousness and this term can mean the opposite of data acquisi­tion process and can refer us to the idea of Integrity (A.V. Smirnov). Once the most im­portant, meaningful and existential activity of the mind is identified as the denotation of this term, the problem of Smysl acquires the potential of changing the perspective on the possibility of creating a “strong” AI. (shrink)

Cognitive psychology and cognitive science are concerned with a domain of cognition that is much broader than the realm of judgement, belief, and inference. The idea of states with semantic content is extended far beyond the space of reasons and justification. Within this broad class of states we should, however, differentiate between the states distinctive of thinking persons — centrally, beliefs, desires, and intentions — and other states. The idea of consciousness does not furnish a principle of demarcation. But (...) the distinction between states whose content is conceptualized by the person whose states they are and states for which this is not so is more promising. This principle of demarcation contains the seeds of a problem for distributed connectionism. The article ends with some more general reflections about cognitive science. (shrink)

Many social trends are conspiring to drive the adoption of greater automation in society, and we will certainly see a greater offloading of human decisionmaking to robots in the future. Many of these decisions are morally salient, including decisions about how benefits and burdens are distributed. Roboticists and ethicists have begun to think carefully about the moral decision making apparatus for machines. Their concerns often center around the plausible claim that robots will lack many of the mental capacities that are (...) indispensable in human moral decision making, such as empathy. To the extent that robots may be robustly artificially intelligent, these concerns subside, but they give way to new worries about creating artificial agents to do our bidding, if those artificial agents have moral standing. We suggest that the question of AI consciousness poses a dilemma. Whether artificially intelligent agents will be conscious or not, we will face serious difficulties in programming them to reliably make moral decisions. (shrink)

In this article, I develop a loosely Wittgensteinian conception of what it takes for a being, including an AI system, to understand language, and I suggest that current state of the art systems are closer to fulfilling these requirements than one might think. Developing and defending this claim has both empirical and conceptual aspects. The conceptual aspects concern the criteria that are reasonably applied when judging whether some being understands language; the empirical aspects concern the question whether a given being (...) fulfills these criteria. On the conceptual side, the article builds on Glock’s concept of intelligence, Taylor’s conception of intrinsic rightness as well as Wittgenstein’s rule-following considerations. On the empirical side, it is argued that current transformer-based NNLP models, such as BERT and GPT-3 come close to fulfilling these criteria. (shrink)

AI systems are expected to impact the ways we communicate, learn, and interact with technology. However, there are still major concerns about their commonsense reasoning, and personalization. This article computationally explains causal (vs. statistical) inference, at different levels of abstraction, and provides three examples of how we can use do-operator, a mathematical operator for intervention, to address some of these concerns. The first example is from an educational module that I developed and implemented for undergraduate engineering students, as part of (...) an educational research project with the US National Science Foundation. For the first time, to the best of my knowledge, 117 students could successfully use do-operator in a cybersecurity investment decision, according to Bloom’s learning taxonomy. Gender did not make a significant difference in the students’ performance, according to the Mann–Whitney U test. The second example explains using do-operator in assessing the effectiveness of intelligent tutoring systems, ITS, in receiving higher grades. The third example sheds light on combining online learning and offline learning, in reinforcement learning, to find the optimal policy that maximizes reward. To shed light on future research on explainability and personalization, I offer two recommendations: 1- Learn like System 2, the conscious learner (based on Bengio’s proposal for deep learning 2.0), and 2- Preference, a process, not an object (based on preference analysis of 25,646 registrants, entities and individuals purchasing domain names). In conclusion, this article contributes to achieving the goal of human–AI: Machines that think that learn and that create. (shrink)

The advent of conversational agents with increasingly human-like behaviour throws old philosophical questions into new light. Does it, or could it, ever make sense to speak of AI agents built out of generative language models in terms of consciousness, given that they are ‘mere’ simulacra of human behaviour, and that what they do can be seen as ‘merely’ role play? Drawing on the later writings of Wittgenstein, this paper attempts to tackle this question while avoiding the pitfalls of dualistic (...) thinking. (shrink)

This article investigates recent shifts in the landscape of Artificial Intelligence (ai), focusing on the emergence and controversies surrounding Large Language Models (llm s). Tracing the historical trajectory of ai enthusiasm from the 1960s to the present, the paper delves into the paradigm shifts influenced by symbolic ai, parallel distributed processing, and the rise of llm s, with particular emphasis on the transformative impact of OpenAI’s ChatGPT 3.5 release in late 2022. The paper contributes to the ongoing discourse on ai (...) ethics, societal implications, and the intricate debate surrounding the perceived intelligence and consciousness of contemporary language models. (shrink)

In AI, consciousness of self consists in a program having certain kinds of facts about its own mental processes and state of mind. We discuss what consciousness of its own mental structures a robot will need in order to operate in the common sense world and accomplish the tasks humans will give it. It's quite a lot. Many features of human consciousness will be wanted, some will not, and some abilities not possessed by humans have already been (...) found feasible and useful in limited contexts. We give preliminary fragments of a logical language a robot can use to represent information about its own state of mind. A robot will often have to conclude that it cannot decide a question on the basis of the information in memory and therefore must seek information externally. Gödel's idea of relative consistency is used to formalize non-knowledge. Programs with the kind of consciousness discussed in this article do not yet exist, although programs with some components of it exist. Thinking about consciousness with a view to designing it provides a new approach to some of the problems of consciousness studied by philosophers. One advantage is that it focusses on the aspects of consciousness important for intelligent behavior. (shrink)

I argue that, given the way that AI models work and the way that ordinary human rationality works, it is very likely that people are anthropomorphizing AI, with potentially serious consequences. There are good reasons to doubt that LLMs have anything like human understanding, and even if they have representations or meaningful contents in some sense, these are unlikely to correspond to our ordinary understanding of natural language. However, there are natural, and in some ways quite rational, pressures to anthropomorphize (...) or personify LLMs and other AI systems in biased ways. This includes not only the classical or obvious ways of personifying AI — taking them to be sentient or have consciousness and understanding — but also taking AI to simulate or function like understanding, track the meaning of our language and our reasoning or logic, or have a model of the world our language is about. These more subtle or unobvious forms of anthropomorphism or personification can contribute to automation bias, AI Hype, and all their attendant risks, and encourage stronger anthropomorphisms as multi-modal AI assistants are developed further. (shrink)

A widely shared view in the literature on first-person thought is that the ability to entertain first-person thoughts requires prior non-conceptual forms of self-consciousness. Many philosophers maintain that the distinctive awareness which accompanies the use of the first person already presupposes a non-conceptual consciousness of the fact that oneself is the owner of a first-person thought. I call this argument The Argument for Non-Conceptual Self-Consciousness based on the Meaning of “I” and will demonstrate that most proponents of (...) the presented argument fail to establish their conclusion, even though I believe that the conclusion is actually true. The argument only justifies the claim that contextual information is needed in order to recognize oneself as the referent of an occurrence of the first person. However, it does not justify the thesis that this information needs to be given consciously and thus the existence of any non-conceptual self-consciousness. Finally, I shall present an argument for the assumption that some form of self-consciousness is needed to reflexively think about oneself via the first-person concept. In order to do so, I will draw on the general discussion about the functional role of consciousness. In particular, I will rely on results recently proposed by Frith and Metzinger (2016), Frith (Pragmatics & Cognition 18:497–551, 2010, Cognitive Neuroscience 2:117–118, 2011), Graziano (2013, Frontiers in Robotics and AI 4:article 60, 2017), and Graziano and Kastner (Cognitive Neuroscience 2:98–113, 2011), which suggest that one of the central functions of consciousness is to provide information about mental states for communication. (shrink)

Could computers ever be conscious? Will they ever have ideas that one could attribute to them and not to the programmer? Will robots be able to 'feel pain', instead of processing bits from sensors informing about danger? Will they have true emotions? These questions may never be answered, but it makes sense to ask whether humans will ever attribute mind to artifacts. This paper suggests introducing a third level of claims regarding artificial intelligence (AI), which is in between 'weak AI' (...) and 'strong AI', the so-called 'attributed AI'. This level requires more than weak AI ('behave as if', which could be said of any desktop calculator), but is less presumptuous than strong AI ('computers that think', a claim that is hard to prove). Attributed AI can be measured. This paper discusses behavioral paradigms for measurements of attributed AI and presents first experimental data. (shrink)

*[[I wrote this paper in January of 1990, but did not publish it because I was never entirely happy with it. My ideas on consciousness were in a state of flux, ultimately evolving into those represented in my book _The Conscious Mind_ (Oxford University Press, 1996). I now think that some parts of this paper are unsatisfactory, especially the positive theory outlined at the end, although a successor to that theory is laid out in the book. Nevertheless, I think (...) the paper raises issues that need to be addressed. ]]. (shrink)

Artificial intelligence is the new technological buzzword. Everything from camera apps on your mobile phone to medical diagnosis algorithms to expert systems are now claiming to be ‘AI’, and many more facets of our lives are being colonized by the application of AI/ML systems (henceforth, ‘AI’). But what does this entail to designers, users and to society at large? Most of the philosophical discourse in this context has focused on the analysis and clarification of the epistemological claims of intelligence within (...) AI and on the moral implications of AI. Philosophical critiques of the plausibility of artificial intelligence do not have much to say about the real-world repercussions of introducing AI systems into every possible domain in the name of automation and efficiency; similarly, most of the moral misgivings about AI have to do with conceiving them as autonomous agents beyond the control of human actors. These discussions have clarified the debate surrounding AI to a great extent; however, certain crucial questions remain outside the ambit of these debates. Arguments in support of AI often take advantage of this void by emphasizing that AI systems are no different than previously existing ‘unintelligent’ technologies, thereby implying that the economic, existential, and ethical threats posed by these systems are either similar in kind as those posed by any other technology or grossly misplaced and exaggerated. In this chapter, we shall think through this assumption by investigating into the ontology of AI systems vis-à-vis ordinary (non-AI) technical artefacts to see wherein lies the distinction between the two. I shall examine how contemporary ontologies of technical artefacts (e.g., intentionalist and non-intentionalist theories of function) apply to AI. Hence, clarifying the ontology of AI is crucial to understand their normative and moral significance and the implications therefrom. (shrink)

The grammatical manipulation and production of language is a great deceiver. We have become habituated to accept the use of well-constructed language to indicate intelligence, understanding and, consequently, intention, whether conscious or unconscious. But we are not always right to do so, and certainly not in the case of large language models (LLMs) like ChapGPT, GPT-4, LLaMA, and Google Bard. This is a perennial problem, but when one understands why it occurs, it ceases to be surprising that it so stubbornly (...) persists. This paper will have three main sections. In the Introduction I will say a little about language, its aetiology, and useful sub-divisions into natural and cultural. In the second section I will explain the current situation with regard to large language models and fill in the background debates which set the problem up as one of increased complexity rather than one of a qualitatively different kind from narrow or specific AI. In the third section I will present the case for the missing phenomenological background and why it is necessary for the co-creation of shared meaning in both natural and cultural language, and I will conclude this section by presenting a rationale for why this situation arises and will continue to arise. Before we do any of this, I need to clarify one point: I do not wish to challenge the ascription of artificial general intelligence (AGI) to LLMs, indeed I think agnosticism is best in this respect, but I do challenge the more serious, and erroneous, ascription of understanding, intention, reason, and consciousness to them. And so, I am making two points: an epistemological one about why we fall into error in our ascription of a mental life to LLMs, and an ontological one about the impossibility of LLMs being or becoming conscious. (shrink)

With recent advancements in technology and computer science, we have reached a point where we can clearly state that thinking is no longer the exclusive privilege of living minds. Artificial intelligence can gather and process information in a manner fairly similar or even superior to our thinking process. AI can use this processed information in a reasoning process to make decisions and execute them. However, what makes our mind distinct from AI is the addition of “I,” that is, an entity (...) composed of two mental processes of awareness-based choice selection and discretionary selection of information for attention that are the fundamental core function of decision making and awareness processes, respectively. ABCS allows the coexistence of free will alongside—but unrestrained by—the chain of causality in the decision-making process with the help of awareness. “I” also allows for DSIA, which is a key step in the awareness process. This intertwined process is crucial for our consciousness. Similar to AI, the mind is devoid of any form of self-recognition without awareness. Self-awareness is a collaborative function of “I” and the mind. “I” is instrumental in the sense of self-awareness, but on its own, it is selfless. Additionally, “I” is the heart of the decision-making process. Therefore, AI is missing “I,” a selfless master of the mind. (shrink)

Recent proposals for human-centered AI (HCAI) help avoid the challenging task of specifying an objective for AI systems, since HCAI is designed to learn the objectives of the humans it is trying to assist. We think the move to HCAI is an important innovation but are concerned with how an instrumental, economic model of human rational agency has dominated research into HCAI. This paper brings the philosophical debate about human rational agency into the HCAI context, showing how more substantive ways (...) to understand human objectives may improve HCAI. We contrast the economic approach with two more substantive modern alternatives: the realist and constructivist approaches to human rational agency. The realist approach holds that rational agents pursue what is good, while the constructivist approach holds that rational agents make autonomous choices. We illustrate each by considering its implications for fine-tuning large language models for chatbots such as ChatGPT. (shrink)

A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...) based on the idiosyncratic notions of symbol systems and the representational abilities they give rise to, in particular with respect to knowledge. While focusing on the period 1956-1982, this review cites both earlier and later literature and it attempts to make visible their potential relevance to today's greatest unifying AI challenge, to wit, the design of wholly autonomous artificial agents (a.k.a. robots) that are not only rational and ethical, but also self-conscious. (shrink)

After reading the essays of Mr. Ts'ai and Mr. Chu, I have a few immature opinions. Generally speaking, I feel that in dealing with the errors of their opponents, both Ts'ai I in his criticism of Huang Yüeh-mien and Chu Kuang-ch'ien in his criticism of Ts'ai I are quite accurate and convincing. However, in presenting their own arguments of what is right, both of them are on shaky ground and in error. That is because in one way or another, consciously (...) or unconsciously, they either deny the objective aspect of the existence of beauty or deny the social aspect of the existence of beauty . All of them consider the objective and the social aspects of beauty as being either this or that, as mutually exclusive and irreconcilable opposites. They think that if we acknowledge the social aspect of beauty, we then have to deny the objective aspect, the fact that the existence of beauty does not depend on the subjective conditions of a person ; or if we acknowledge the objective aspect, we have to deny the social aspect, the fact that the existence of beauty depends on the social life of human beings. But in fact it is not like that. On the one hand, beauty cannot be separated from human society; on the other hand, it can have an objective existence which is independent of man's subjective consciousness. It is this question that I shall discuss. (shrink)

Why do minds exist? How did mud and stone develop into beings that can experience longing, regret, love, and compassion-beings that are aware of their own experience? Until recently, science offered few answers to these existential questions. Journey of the Mind is the first book to offer a unified account of the mind that explains how consciousness, language, the Self, and civilization emerged incrementally out of chaos. The journey begins three billion years ago with the emergence of the simplest (...) possible mind, a nanoscopic archeon, then ascends through amoebas, worms, frogs, birds, monkeys, and AI, examining successively smarter ways of thinking. The authors explain the mathematical principles generating conscious experience and show how these principles led cities and democratic nations to develop new forms of consciousness-the self-aware "superminds." Journey of the Mind concludes by contemplating a higher stage of consciousness already emerging-and the ultimate fate of all minds in the universe. (shrink)

Many think that a specific aspect of phenomenal consciousness—valenced or affective experience—is essential to consciousness’s moral significance (valence sentientism). They hold that valenced experience is necessary for well-being, or moral status, or psychological intrinsic value (or all three). Some think that phenomenal consciousness generally is necessary for non-derivative moral significance (broad sentientism). Few think that consciousness is unnecessary for moral significance (non-necessitarianism). In this paper, I consider the prospects for these views. I first consider the prospects (...) for valence sentientism in light of Vulcans, beings who are conscious but without affect or valence of any sort. I think Vulcans pressure us to accept broad sentientism. But I argue that a consideration of explanations for broad sentientism opens up possible explanations for non-necessitarianism about the moral significance of consciousness. That is, once one leans away from valence sentientism because of Vulcans, one should feel pressure to accept a view on which consciousness is not necessary for well-being, moral status, or psychological intrinsic value. (shrink)

Despite recent breakthroughs in the field of artificial intelligence (AI) – or more specifically machine learning (ML) algorithms for object recognition and natural language processing – it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related (...) to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states)1 and allows for understanding (e.g., taking insight into causal relationships instead of ‘blindly’ relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to ‘true’ agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on ‘bits of meaning’ instead of information in the sense of data, quite unlike both the ‘good old fashioned’, symbolic AI of the past, as well as the current wave of deep neural network based, ‘sub-symbolic’ AI, which both share the idea of thinking as (only) information processing: In symbolic AI, the name explicitly references to its formal system based, i.e. essentially rule-based, nature, but also sub-symbolic AI is (implicitly) rule-based, only now via globally parametrized, nested functions. In the following I propose an alternative view of NI as information processing plus ‘bundle pushing’, discuss an example which illustrates how bundle pushing can cut information processing short,and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations. (shrink)

Throughout much of the modern period, the human mind has been regarded as a property of the brain and therefore something confined to the inside of the head—a view commonly known as ‘internalism’. But recent works in cognitive science, philosophy, and anthropology, as well as certain trends in the development of technology, suggest an emerging view of the mind as a process not confined to the brain but spread through the body and world—an outlook covered by a family of views (...) labelled ‘externalism’. In this paper, we will suggest there is now sufficient momentum in favour of externalism of various kinds to mark a historical shift in the way the mind is understood. We dub this emerging externalist tendency the ‘New Mind’. Key properties of the New Mind will be summarised and some of its implications considered in areas such as art and culture, technology, and the science of consciousness. (shrink)

From his preliminary analysis in 1965, Hubert Dreyfus projected a future much different than those with which his contemporaries were practically concerned, tempering their optimism in realizing something like human intelligence through conventional methods. At that time, he advised that there was nothing “directly” to be done toward machines with human-like intelligence, and that practical research should aim at a symbiosis between human beings and computers with computers doing what they do best, processing discrete symbols in formally structured problem domains. (...) Fast-forward five decades, and his emphasis on the difference between two essential modes of processing, the unconscious yet purposeful mode fundamental to situated human cognition, and the “minded” sense of conscious processing characterizing symbolic reasoning that seems to lend itself to explicit programming, continues into the famous Dreyfus–McDowell debate. The present memorial reviews Dreyfus’ early projections, asking if the fears that punctuate current popular commentary on AI are warranted, and in light of these if he would deliver similar practical advice to researchers today. (shrink)

This essay proposes a cultural and historical explanation for the American Military's fascination with computing. Three key elements of post-WWII US political culture — apocalyptic struggle with the USSR, subsuming all other conflicts: a long history of antimilitarist sentiment in American politics; and the rise of science-based military power — contributed to a sense of the world as a closed system accessible to American technological control. A developing scientific systems discourse, centrally including computer science and AI, was adopted for strategic (...) thinking and military technology. The Strategic Computing and Strategic Defense Initiatives are discussed as contemporary examples of this conjunction. (shrink)

We propose four policies of ethical design of human-grade Artificial Intelligence. Two of our policies are precautionary. Given substantial uncertainty both about ethical theory and about the conditions under which AI would have conscious experiences, we should be cautious in our handling of cases where different moral theories or different theories of consciousness would produce very different ethical recommendations. Two of our policies concern respect and freedom. If we design AI that deserves moral consideration equivalent to that of human (...) beings, that AI should be designed with self-respect and with the freedom to explore values other than those we might impose. We are especially concerned about the temptation to create human-grade AI pre-installed with the desire to cheerfully sacrifice itself for its creators’ benefit. (shrink)

'if AI is outside your field, or you know something of the subject and would like to know more then Artificial Intelligence: The Basics is a brilliant primer.' - Nick Smith, Engineering and Technology Magazine November 2011 Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which (...) have blurred the boundaries. Topics covered include: how intelligence can be defined whether machines can 'think' sensory input in machine systems the nature of consciousness the controversial culturing of human neurons. Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject. (shrink)

In November 2022, OpenAI released ChatGPT, an incredibly sophisticated chatbot. Its capability is astonishing: as well as conversing with human interlocutors, it can answer questions about history, explain almost anything you might think to ask it, and write poetry. This level of achievement has provoked interest in questions about whether a chatbot might have something similar to human intelligence or even consciousness. Given that the function of a chatbot is to process linguistic input and produce linguistic output, we consider (...) the question whether a sophisticated chatbot might have inner speech. That is: Might it talk to itself, internally? We explored this via a conversation with ‘Playground’, a chatbot which is very similar to ChatGPT but more flexible in certain respects. We asked it questions which, plausibly, can only be answered if one first produces some inner speech. Here, we present our findings and discuss their philosophical significance. (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)

The rapid development of programs based on the principles of machine learning (ML) and artificial intelligence (AI) signals significant changes in the components of education, namely in the provider, the tool of transmission, and the recipient of knowledge. Historical data analysis regarding the key functions of education serves as the basis for identifying fundamental innovations introduced through AI and ML. The impact of writing, printing, and the Internet has significantly altered the tool for knowledge transmission, influencing the volume of information (...) and the number of knowledge recipients. The implementation of AI and ML transforms not only the tool but also the provider of knowledge itself, which can become impersonal thanks to the corresponding computer programs. With the historically justified increase in the volume of knowledge possessed by humanity, there is a transformation observed in education systems. This is especially true for democratic societies, where the emphasis is increasingly shifting from providing a large amount of knowledge to developing critical thinking. It has been researched that programs based on AI and ML, applying linguistic models, are capable of effectively systematizing knowledge. This lays the foundation for personalizing the entire education process for a specific knowledge recipient, without burdening the provider. However, there have been cases when such imitation misleads scientists, who perceive it as attempts at communication between programs that have an equivalence to the human level. The conclusions drawn indicate a significant transformation of the education system caused by AI and ML-based programs. However, intelligent programs are unable to evolve into independent knowledge recipients due to their inability to consciously attribute meaning to information, transforming it into knowledge. (shrink)