The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard (...) interpretation assumes that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use, so that these two senses of using information are not conflated, but instead the question of how they are related to one another can be studied directly. I apply the generative methodology I have developed elsewhere for cognitive science and AI research (Miracchi, 2017, 2019a) to show how the Frame Problem can be appropriately updated. (shrink)

The field of Artificial Intelligence has been around for over 60 years now. Soon after its inception, the founding fathers predicted that within a few years an intelligent machine would be built. That prediction failed miserably. Not only hasn’t an intelligent machine been built, but we are not much closer to building one than we were some 50 years ago. Many reasons have been given for this failure, but one theme has been dominant since its advent in 1969: The (...) Frame Problem. What looked initially like an innocuous problem in logic, turned out to be a much broader and harder problem of holism and relevance in commonsense reasoning. Despite an enormous literature on the topic, there is still disagreement not only on whether the problem has been solved, but even what exactly the problem is. In this paper we provide a formal description of the initial problem, the early attempts at a solution, and its ramification both in AI as well as philosophy. (shrink)

Wagman examines the emulation of human cognition by artificial intelligence systems. The book provides detailed examples of artificial intelligence programs (such as the FERMI System and KEKADA program) accomplishing highly intellectual tasks.

The frame problem is a problem in artificial intelligence that a number of philosophers have claimed has philosophical relevance. The structure of this paper is as follows: (1) An account of the frame problem is given; (2) The frame problem is distinguished from related problems; (3) The main strategies for dealing with the frame problem are outlined; (4) A difference between commonsense reasoning and prediction using a scientific theory is argued for; (5) Some implications for (...) the.. (shrink)

The author of the acclaimed Gay Fiction Speaks brings us new interviews with twelve prominent gay writers who have emerged in the last decade. Hear Us Out demonstrates how in recent decades the canon of gay fiction has developed, diversified, and expanded its audience into the mainstream. Readers will recognize names like Michael Cunningham, whose Pulitzer Prize-winning novel The Hours inspired the hit movie; and others like Christopher Bram, Bernard Cooper, Stephen McCauley, and Matthew Stadler. These accounts explore the vicissitudes (...) of writing on gay male themes in fiction over the last thirty years--prejudices of the literary marketplace; social and political questions; the impact of AIDS; commonalities between gay male and lesbian fiction... and even some delectable bits of gossip. (shrink)

The article examines the dialogue between British cognitive science expert Murray Shanahan and the large language model Claude 3 Opus about “self-awareness” of artificial intelligence (AI). Adopting a text-centric approach, the author analyzes AI’s discourse through a hermeneutic lens from a reader’s perspective, irrespective of whether AI possesses consciousness or personhood. The article draws parallels between AI’s reasoning about the nature of consciousness and Buddhist concepts, especially the doctrine of dharmas, which underpins the Buddhist concept of anātman (...) (“non-Self”). Basic classifications of dharmas and their justification are examined in light of the Buddhist system of ideas about the foundations of an individual’s cognitive experience in the world. The author emphasizes that the problem of the Self as a linguistic and conceptual construct, rather than a real ontological category, was first formulated in the teachings of Buddha Shakyamuni who also proposed an “experimental” application of this concept in practices of systematic introspection (smṛti). The article contends that Claude’s discourse on self-awareness, even if it is just a tapestry of linguistic constructs woven by preset algorithms, could prove to be a source inspiring new approaches to the enigma of consciousness. This potential stems from its vast database, which is a melting pot of textual heritage from diverse human cultures. The author posits that examining AI-generated texts through the prism of Indian and Buddhist thought traditions can be eye-opening. Such an approach might help shed light on and overcome the unconscious cognitive biases and cultural blind spots within Western consciousness studies that have hindered their engagement with the full spectrum of human intellectual traditions. The author concludes that discovering different cultural sources in AI discourse and examining it from the perspective of various cultural traditions can: firstly, enrich the conceptual apparatus of cognitive studies; secondly, reveal universal cross-cultural patterns in understanding consciousness; thirdly, generate new research hypotheses and directions in studying not only artificial but also natural intelligence; fourthly, contribute to rethinking our understanding of the Other, by expanding the boundaries of what we today consider conscious or sentient. (shrink)

The frame problem, a long-standing issue in Artificial Intelligence (AI), revolves around determining the relevance of information in an ever-changing array of contexts, posing a formidable challenge in modeling human reasoning. The purpose of this paper is to explore the hypothesis that emotions are able to solve, or at least enable a substantial step towards a solution. I argue that, while emotions are integral to cognitive processes, they do not offer a solution to the frame problem, nor (...) can they play a significant role in a solution. In the course of the argument, I’ll show why some previous claims that emotions could be helpful towards a solution rely on a problematic analysis of what the frame problem is. Ultimately, the conclusion is that no context-dependent feature can play a non-trivial role in solutions to the frame problem, and that is the case of emotions. (shrink)

Although the “frame problem” in philosophy has been raised in the context of the artificial intelligence, it is only an exemplification of broader problem. It seems that contemporary ethical debates are not so much about conclusions, decisions, norms, but rather about what we might call a “frame”. Metaethics has always been the bridge between purely ethical principles (“this is good and it should be done”, “this is wrong and it should be avoided”) and broader (ontological, epistemic, anthropological (...) etc.) assumptions. One of the most interesting meta-ethical debates concerns the “frame problem”: whether the ethical frame is objective and self-evident, or is it objective but not self-evident? In classical philosophy, this problem takes the form of a debate on the first principles: nonprovable but necessary starting points for any practical reasoning. They constitute the invisible but essential frame of every moral judgment, decision and action. The role of philosophy is not only to expose these principles, but also to understand the nature of the moral frame. (shrink)

The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe (...) that it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI. (shrink)

Context sensitivity is one of the distinctive marks of human intelligence. Understanding the flexible way in which humans think and act in a potentially infinite number of circumstances, even though they’re only finite and limited beings, is a central challenge for the philosophy of mind and cognitive science, particularly in the case of those using representational theories. In this work, the frame problem, that is, the challenge of explaining how human cognition efficiently acknowledges what is relevant from what is (...) not in each context, has been adopted as a guide. By using it, we’ve been able to describe a fundamental tension between context sensitivity and the mental representations used in cognition theories. The first chapter discusses the nature of the frame problem,as well as the reasons for its persistence. In the second and third chapters, the problem is used as a measure tool in order to inquiry a few representational approaches and check how well suited they are to deal with context dependencies. The problems found are then correlated with the frame problem. Throughout the discussion, we try to show that 1) none of the evaluated approaches is capable of dealing with context sensitivity in a proper manner, but 2) that’s not a reason to think that the frame problem constitutes an argument against representational approaches in general, and 3) that it constitutes a fundamental conceptual tool in contemporary research -/- A sensibilidade ao contexto é uma das marcas distintivas da inteligência humana. Compreender o modo flexível como o ser humano pensa e age em função de um número potencialmente infinito de circunstâncias, ainda que munido de recursos finitos e limitados, é um desafio central para a filosofia da mente e para a ciência cognitiva, em particular aos que fazem uso de teorias representacionalistas. Nesse trabalho, adotou-se como fio condutor o modo como isso se manifesta no "frame problem": a dificuldade em explicar como a cognição humana reconhece, de maneira eficiente, o que é ou não relevante em cada contexto. A partir dele, buscou-se caracterizar uma tensão fundamental entre a sensibilidade ao contexto e o uso de representações mentais em teorias da cognição. O primeiro capítulo discute a natureza do frame problem, bem como as razões de sua resiliência. No segundo e terceiro capítulos, faz-se uso do problema como métrica para investigar o quão adequado é o tratamento das dependências contextuais no âmbito de várias abordagens representacionais. No decorrer da discussão, realiza-se um esforço argumentativo para mostrar que 1) nenhuma das estratégias abordadas é capaz tratar adequadamente da sensibilidade ao contexto, mas que 2) apesar disso, o frame problem não constitui argumento fatal para teorias representacionalistas em geral, e que 3) ele constitui uma ferramenta conceitual fundamental para pesquisas contemporâneas. (shrink)

The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe (...) that it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI. (shrink)

For several years, scholars have (for good reason) been largely preoccupied with worries about the use of artificial intelligence and machine learning (AI/ML) tools to make decisions about us. Only recently has significant attention turned to a potentially more alarming problem: the use of AI/ML to influence our decision-making. The contexts in which we make decisions—what behavioral economists call our choice architectures—are increasingly technologically-laden. Which is to say: algorithms increasingly determine, in a wide variety of contexts, both the sets (...) of options we choose from and the way those options are framed. Moreover, artificial intelligence and machine learning (AI/ML) makes it possible for those options and their framings—the choice architectures—to be tailored to the individual chooser. They are constructed based on information collected about our individual preferences, interests, aspirations, and vulnerabilities, with the goal of influencing our decisions. At the same time, because we are habituated to these technologies we pay them little notice. They are, as philosophers of technology put it, transparent to us—effectively invisible. I argue that this invisible layer of technological mediation, which structures and influences our decision-making, renders us deeply susceptible to manipulation. Absent a guarantee that these technologies are not being used to manipulate and exploit, individuals will have little reason to trust them. (shrink)

This paper provides a brief overview of findings in mathematical cognition and how a human-like AI in mathematics may look like. Then, it provides six reasons in favor of a human-like AI for mathematics: (1) human cognition, with all its limits, creates mathematics; (2) human mathematics is insightful, not merely deductive steps; (3) human cognition detects structure in the real world; (4) human cognition can tackle and detect complex problems; (5) human cognition is creative; (6) human cognition considers ethical issues. (...) The paper provides a tentative frame to the question whether human mathematical cognition is relevant for designing an artificial intelligence that works on and creates mathematics. (shrink)

The frame problem, a long-standing issue in Artificial Intelligence (AI), revolves around determining the relevance of information in an ever-changing array of contexts, posing a formidable challenge in modeling human reasoning. The purpose of this paper is to explore the hypothesis that emotions are able to solve, or at least enable a substantial step towards a solution. I argue that, while emotions are integral to cognitive processes, they do not offer a solution to the frame problem, nor (...) can they play a significant role in a solution. In the course of the argument, I’ll show why some previous claims that emotions could be helpful towards a solution rely on a problematic analysis of what the frame problem is. Ultimately, the conclusion is that no context-dependent feature can play a non-trivial role in solutions to the frame problem, and that is the case of emotions. Keywords: Frame problem. Emotions. Artificial Intelligence. Cognition. Relevance. (shrink)

Thanks to advances in machine learning in recent years the ability of AI agents to act independently of human oversight, respond to their environment, and interact with other machines has significantly increased, and is one step closer to human-like performance. For this reason, we can observe contemporary researchers’ efforts towards modeling agency in artificial systems. In this light, the aim of this paper is to develop a dual-process approach to the problem of AI agency perception, and to discuss possible (...) triggers of various agency perceptions. The article discusses the agency attribution phenomenon, based on which the argument for the dual-process nature of agency perception is developed. Two distinct types of thinking involved in human reasoning on AI agency are suggested: Type 1 and Type 2. The first one is fast, automatic, routine, and often unconscious; the second is a slower, controlled, more conscious one. These two distinct types of processing can yield differing and sometimes conflicting results for human cognition and interaction. The preliminary philosophical findings may contribute to further investigations in philosophy of mind or cognitive psychology and could also be empirically tested in HCI and UX studies. (shrink)

Some have claimed that since machines lack emotional “qualia”, or conscious experiences of emotion, machine intelligence will fall short of human intelligence. I examine this objection, ultimately finding it unpersuasive. I first discuss recent work on emotion that suggests that emotion plays various roles in cognition. I then raise the following question: are phenomenal experiences of emotion an essential or necessary component of the performance of these cognitive abilities? I then sharpen the question by distinguishing between four possible positions one (...) might take. I reject one of these four positions largely on empirical grounds. But the remaining three positions all suggest that even if emotional qualia play an important role in human cognition, emotional qualia are not essential to the performance of these cognitive abilities in principle, so, e.g., a machine that lacks emotional qualia might still be able to perform them. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is why I feel (...) fully at home with the goals pursued by the organizers of this conference.I do not believe it is feasible or even desirable to operate the rapprochement between the two disciplines at a global level. The history of cognitive science is exceedingly involved, and the part played by artificial intelligence in it compared to the part played by the neurosciences has varied greatly over time.1 Only a piecemeal approach makes sense. I present here four short case studies that cover the whole period, from the beginnings in cybernetics to today's artificial intelligence (AI).In order to better situate these studies within a broader frame, I propose the diagram in Figure 1. To comment on it would require more time than available for my whole presentation. Let me just say the following. [End Page 1]It is essential to distinguish between two kinds of AI. The first to bear this name was launched by economist Herbert Simon and computer scientist Allan Newell at a Dartmouth conference in 1956. It was based on the premise that thinking is computing, with this computation bearing on sentences of a "language of thought" implementable in a digital computer. It turns out that this AI(1) was progressively substituted from the 1980s onward by an AI(2), which, to some extent, was a return to a fundamental result obtained by cybernetics in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts had constructed a mathematical model of a network of elementary calculators called "neurons" and explored its properties. The capacity to "think" was one of these. It was not situated at the elementary level, as was the case in AI(1), but it emerged at the collective level from the interactions between the neurons.This model was heavily criticized by the supporters of AI(1),2 and its first implementations, in the notorious "Perceptron" in particular (1957), were pitiful failures. However, the emergence of "second-order cybernetics" under the guidance of cybernetician Heinz von Foerster and the theory of complex, self-organizing systems that it helped develop, in cooperation and rivalry with similar efforts in the physical sciences, enabled the neural network model to come to a second life under different names: formal neurons, deep learning, big data, and the like. Together, they constitute AI(2).The following network of influences wouldn't have been possible without its stem, that is, the boxes containing the names of Kurt Gödel and Alan Turing. The former revolutionized logic by showing that reasoning about arithmetic and arithmetic are one and the same, insofar as it is possible to code in a 1-to-1 fashion propositions about integers (for instance, "7 is a prime number") by means of integers. The latter demonstrated that some mechanisms can self-transcend inasmuch as they generate an ensemble of outputs that cannot be defined mechanistically. Later, John von Neumann, who is credited along with Alan Turing with having invented the digital computer, would use this logical possibility to define complexity: A mechanism is complex if it is capable of generating one more complex than itself. The gate was open for the theories of complex, self-organizing systems to thrive and, by way of consequence, for AI(2) to emerge.fascination with modelsHerbert Simon (1916–2001), winner of the Nobel Prize in economics for his work on what he called "bounded rationality," is generally considered to be one [End Page 2] Click for larger view View full resolutionFigure 1.of the founders of artificial intelligence. Some cognitive scientists still rely on the computer program that he devised with Alan Newell to simulate the creative processes of human thought—a program aptly named "General Problem Solver." The title of one of his principal works, The Sciences of the... (shrink)

The possibility of AI consciousness depends much on the correct answer to the mind–body problem: how our materialistic brain generates subjective consciousness? If a materialistic answer is valid, machine consciousness must be possible, at least in principle, though the actual instantiation of consciousness may still take a very long time. If a non-materialistic one (either mentalist or dualist) is valid, machine consciousness is much less likely, perhaps impossible, as some mental element may also be required. (...) Some recent advances in neurology (despite the separation of the two hemispheres, our brain as a whole is still able to produce only one conscious agent; the negation of the absence of a free will, previously thought to be established by the Libet experiments) and many results of parapsychology (on medium communications, memories of past lives, near-death experiences) suggestive of survival after our biological death, strongly support the non-materialistic position and hence the much lower likelihood of AI consciousness. Instead of being concern about AI turning conscious and machine ethics, and trying to instantiate AI consciousness soon, we should perhaps focus more on making AI less costly and more useful to society. (shrink)

The article provides a comprehensive analysis of scientific approaches to the formation of legal regulation of relations arising in the development and use of artificial intelligence technologies, their socio-legal status, as well as social, ethical, methodological, and practical legal issues with an emphasis on the fundamentals of natural legal doctrine. The author’s vision of the concept of human interaction and artificial intelligence from the standpoint of legal relations is given. Emphasis is placed on the need to study the (...) problems that arise in this area, within the framework of information law, as a complex branch of law and sociohumanitarian informology—at the level of an interdisciplinary approach. Ways to improve the mechanisms and methods of legal support in the field of artificial intelligence through the development and implementation of technological and legal methods as an innovative tools and effective tools for special technological impact. (shrink)

The thesis deals with the concept of truth and the paradoxes of truth. Philosophical theories usually consider the concept of truth from a wider perspective. They are concerned with questions such as - Is there any connection between the truth and the world? And, if there is - What is the nature of the connection? Contrary to these theories, this analysis is of a logical nature. It deals with the internal semantic structure of language, the mutual semantic connection of sentences, (...) above all the connection of sentences that speak about the truth of other sentences and sentences whose truth they speak about. Truth paradoxes show that there is a problem in our basic understanding of the language meaning and they are a test for any proposed solution. It is important to make a distinction between the normative and analytical aspect of the solution. The former tries to ensure that paradoxes will not emerge. The latter tries to explain paradoxes. Of course, the practical aspect of the solution is also important. It tries to ensure a good framework for logical foundations of knowledge, for related problems in Artificial Intelligence and for the analysis of the natural language. Tarski’s analysis emphasized the T-scheme as the basic intuitive principle for the concept of truth, but it also showed its inconsistency with the classical logic. Tarski’s solution is to preserve the classical logic and to restrict the scheme: we can talk about the truth of sentences of a language only inside another essentially richer metalanguage. This solution is in harmony with the idea of reflexivity of thinking and it has become very fertile for mathematics and science in general. But it has normative nature | truth paradoxes are avoided in a way that in such frame we cannot even express paradoxical sentences. It is also too restrictive because, for the same reason we cannot express a situation in which there is a circular reference of some sentences to other sentences, no matter how common and harmless such a situation may be. Kripke showed that there is no natural restriction to the T-scheme and we have to accept it. But then we must also accept the riskiness of sentences | the possibility that under some circumstances a sentence does not have the classical truth value but it is undetermined. This leads to languages with three-valued semantics. Kripke did not give any definite model, but he gave a theoretical frame for investigations of various models | each fixed point in each three-valued semantics can be a model for the concept of truth. The solutions also have normative nature | we can express the paradoxical sentences, but we escape a contradiction by declaring them undetermined. Such a solution could become an analytical solution only if we provide the analysis that would show in a substantial way that it is the solution that models the concept of truth. Kripke took some steps in the direction of finding an analytical solution. He preferred the strong Kleene three-valued semantics for which he wrote it was "appropriate" but did not explain why it was appropriate. One reason for such a choice is probably that Kripke finds paradoxical sentences meaningful. This eliminates the weak Kleene three valued semantics which corresponds to the idea that paradoxical sentences are meaningless, and thus indeterminate. Another reason could be that the strong Kleene three valued semantics has the so-called investigative interpretation. According to this interpretation, this semantics corresponds to the classical determination of truth, whereby all sentences that do not have an already determined value are temporarily considered indeterminate. When we determine the truth value of these sentences, then we can also determine the truth value of the sentences that are composed of them. Kripke supplemented this investigative interpretation with an intuition about learning the concept of truth. That intuition deals with how we can teach someone who is a competent user of an initial language (without the predicate of truth T) to use sentences that contain the predicate T. That person knows which sentences of the initial language are true and which are not. We give her the rule to assign the T attribute to the former and deny that attribute to the latter. In that way, some new sentences that contain the predicate of truth, and which were indeterminate until then, become determinate. So the person gets a new set of true and false sentences with which he continues the procedure. This intuition leads directly to the smallest fixed point of strong Kleene semantics as an analytically acceptable model for the logical notion of truth. However, since this process is usually saturated only on some transfinite ordinal, this intuition, by climbing on ordinals, increasingly becomes a metaphor. This thesis is an attempt to give an analytical solution to truth paradoxes. It gives an analysis of why and how some sentences lack the classical truth value. The starting point is basic intuition according to which paradoxical sentences are meaningful (because we understand what they are talking about well, moreover we use it for determining their truth values), but they witness the failure of the classical procedure of determining their truth value in some "extreme" circumstances. Paradoxes emerge because the classical procedure of the truth value determination does not always give a classically supposed (and expected) answer. The analysis shows that such an assumption is an unjustified generalization from common situations to all situations. We can accept the classical procedure of the truth value determination and consequently the internal semantic structure of the language, but we must reject the universality of the exterior assumption of a successful ending of the procedure. The consciousness of this transforms paradoxes to normal situations inherent to the classical procedure. Some sentences, although meaningful, when we evaluate them according to the classical truth conditions, the classical conditions do not assign them a unique value. We can assign to them the third value, \undetermined", as a sign of definitive failure of the classical procedure. An analysis of the propagation of the failure in the structure of sentences gives exactly the strong Kleene three-valued semantics, not as an investigative procedure, as it occurs in Kripke, but as the classical truth determination procedure accompanied by the propagation of its own failure. An analysis of the circularities in the determination of the classical truth value gives the criterion of when the classical procedure succeeds and when it fails, when the sentences will have the classical truth value and when they will not. It turns out that the truth values of sentences thus obtained give exactly the largest intrinsic fixed point of the strong Kleene three-valued semantics. In that way, the argumentation is given for that choice among all fixed points of all monotone three-valued semantics for the model of the logical concept of truth. An immediate mathematical description of the fixed point is given, too. It has also been shown how this language can be semantically completed to the classical language which in many respects appears a natural completion of the process of thinking about the truth values of the sentences of a given language. Thus the final model is a language that has one interpretation and two systems of sentence truth evaluation, primary and final evaluation. The language through the symbol T speaks of its primary truth valuation, which is precisely the largest intrinsic fixed point of the strong Kleene three valued semantics. Its final truth valuation is the semantic completion of the first in such a way that all sentences that are not true in the primary valuation are false in the final valuation. (shrink)

In lieu of an abstract, here is a brief excerpt of the content: Reviewed by: The Myth or Elegy of Artificial Intelligence by Tingyang ZhaoXuejian Zhou (bio)Rengongzhineng de shenhua huo beige 人工智能的神話或悲歌 ( The Myth or Elegy of Artificial Intelligence). By Tingyang Zhao 趙汀陽. Beijing: The Commercial Press, 2022. Pp. 155. Hardcover RMB68, isbn 978-981-16-7749-6. In recent years, the philosophy of artificial intelligence has undoubtedly become one of the most popular topics. There is a vague viewpoint suggesting (...) that Chinese philosophy might face challenges in addressing the philosophical issues posed by artificial intelligence. However, compared to the past, when discussing Chinese philosophy today, we must acknowledge the significant impact of modern technology. While the contemporary world does indeed witness clashes of civilizations, individuals from various civilizations share a common predicament: their close entanglement with modern technology. Regarding the future of Chinese philosophy, it becomes crucial to direct our attention towards modern advancements like artificial intelligence and the diverse range of issues they introduce. In light of this context, Zhao Tingyang's 趙汀陽 new book, The Myth or Elegy of Artificial Intelligence, presents us with a fresh perspective. In this book, Zhao primarily delves into the philosophical issues surrounding Super Artificial Intelligence, a subject regarded as both a metaphysical and a political problem. This notion refers to artificial intelligence that surpasses general artificial intelligence in terms of functionality, and possesses self-consciousness. Therefore, "myth" refers to the myth of the emergence of Super Artificial Intelligence disrupting people's subjectivity, giving rise to a new myth of artificial intelligence. Conversely, the term "elegy" embodies the crisis in human destiny that stems from such profound changes, which will lead to an ontological revolution in human existence. This suggests that within the narrative of artificial intelligence's myth, there lies the elegy of humanity (p. 4). The intricate interplay between these two realms of myth and elegy vividly showcases the value of Chinese philosophy. The book consists of a total of ten chapters. In Chapter 1, Zhao initially presents a thought-provoking question. With the rapid advancement of artificial intelligence humanity is faced with a novel ontological event, where the existence of artificial intelligence could potentially jeopardize human existence. This presents an ontological problem of "de-existence" (p. 8). As current artificial intelligence remains within the domain of the Turing machine concept and lacks subjectivity, it cannot effectively engage in matters involving self-correlation or [End Page 1] infiniteness. However, if artificial intelligence evolves into a super Turing machine endowed with subjectivity and free will, it could gain the capacity to modify its own programs and societal norms based on its needs or maximal benefits. This scenario introduces the possibility of a crisis in human existence, where the very existence of humanity is put at risk (pp. 17-23). In Chapter 2, Zhao analyzes the safety conditions of artificial intelligence and proposes the need to establish self-destruct mechanisms for Super Artificial Intelligence to ensure the status of human existence (pp. 34-38). Interestingly, in this context, Zhao connects artificial intelligence with the concept of Tianxia 天下 ("all under heaven"), implying that in order to control capital and power, the world needs a new form of politics, namely, a Tianxia 天下 system. Tianxia 天下 represents a world system that ensures world peace and the shared interests of people in a multicultural context, a non-exclusive system defining the world beyond any particular entity. According to Zhao, only a Tianxia 天下 system can manage the technological risks of the world (p. 39). Theoretically (hopefully in practice as well), a significant application of the Tianxia 天下 system would be to use global authority to limit any high-risk behaviors. Based on the first two chapters, it becomes apparent that the dangers related to artificial intelligence do not lie in its current capabilities, but in its potential for self-consciousness. For this reason, in Chapter 3, Zhao analyzes this issue further. In his view, once artificial intelligence possesses the ability for self-reflection, it will become possible for it to transform its own system and create new rules. Especially if artificial intelligence invents a universal language of its own, equivalent to human natural language, then all program systems could be reinterpreted, reconstructed, and redefined using this universal language (pp. 47-52). Furthermore, if... (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)

It has become a truism that the ethics of artificial intelligence (AI) is necessary and must help guide technological developments. Numerous ethical guidelines have emerged from academia, industry, government and civil society in recent years. While they provide a basis for discussion on appropriate regulation of AI, it is not always clear how these ethical guidelines were developed, and by whom. Using content analysis, we surveyed a sample of the major documents (_n_ = 47) and analyzed the accessible information (...) regarding their methodology and stakeholder engagement. Surprisingly, only 38% report some form of stakeholder engagement (with 9% involving citizens) and most do not report their methodology for developing normative insights (15%). Our results show that documents with stakeholder engagement develop more comprehensive ethical guidance with greater applicability, and that the private sector is least likely to engage stakeholders. We argue that the current trend for enunciating AI ethical guidance not only poses widely discussed challenges of applicability in practice, but also of transparent development (as it rather behaves as a black box) and of active engagement of diversified, independent and trustworthy stakeholders. While most of these documents consider people and the common good as central to their telos, engagement with the general public is significantly lacking. As AI ethics moves from the initial race for enunciating general principles to more sustainable, inclusive and practical guidance, stakeholder engagement and citizen involvement will need to be embedded into the framing of ethical and societal expectations towards this technology. (shrink)

In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal (...) values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens. (shrink)

Machine learning algorithms are increasingly used to support decision-making in the exercise of public authority. Here, we argue that an important consideration has been overlooked in previous discussions: whether the use of ML undermines the democratic legitimacy of public institutions. From the perspective of democratic legitimacy, it is not enough that ML contributes to efficiency and accuracy in the exercise of public authority, which has so far been the focus in the scholarly literature engaging with these developments. According to one (...) influential theory, exercises of administrative and judicial authority are democratically legitimate if and only if administrative and judicial decisions serve the ends of the democratic law maker, are based on reasons that align with these ends and are accessible to the public. These requirements are not satisfied by decisions determined through ML since such decisions are determined by statistical operations that are opaque in several respects. However, not all ML-based decision support systems pose the same risk, and we argue that a considered judgment on the democratic legitimacy of ML in exercises of public authority need take the complexity of the issue into account. This paper outlines considerations that help guide the assessment of whether a ML undermines democratic legitimacy when used to support public decisions. We argue that two main considerations are pertinent to such normative assessment. The first is the extent to which ML is practiced as intended and the extent to which it replaces decisions that were previously accessible and based on reasons. The second is that uses of ML in exercises of public authority should be embedded in an institutional infrastructure that secures reason giving and accessibility. (shrink)

This book aims to show why a proper ontology of persons has paramount importance for our understanding of the nature of consciousness and its relation to the phenomenon of unconsciousness and artificial intelligence. Contemporary discussions on consciousness often focus on seeking solutions for a wide range of issues that revolve around questions related to what sort of role the brain plays in the existence of consciousness. These questions raise multi-layered and diverse metaphysical (especially, ontological), personal, medical, (...) moral, and legal issues. In navigating through such a complex web of issues, it has been said that the central problems philosophers and scientists face are establishing the nature and origin of consciousness. For example, is consciousness nothing but a brain process? If not, how is it related to brain activities? Can consciousness exist without depending on the brain and its activities? What are the neural correlates of consciousness, unconsciousness, and persons? Can we make an advance on the science of consciousness by investigating what happens to the brain during general anesthesia? Is there a relation between consciousness and unconsciousness? What is the bearer of consciousness and unconsciousness? Can consciousness exist without being someone’s consciousness? Is consciousness reproducible in computers or digital machines? If consciousness is not in all things, then what conditions must be satisfied for its emergence? Is it ethical to take someone’s vital organs without any prior consent from a person in an irreversible comatose state? The beginning and end of life issues also bring up their own complicated ethical conundrums involving consciousness. (shrink)

The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to (...) promote the belief that games represent an excellent tool for the project of computational psychology (CP). To underline how, despite this, GP has mainly adopted an engineering-inspired methodology and in doing so has distorted the framework of cognitive functionalism. Many successes (i.e. chess, checkers) have been achieved refusing human-like reasoning. The AI has appeared to work well despite ignoring an intrinsic motivation, that of creating an explanatory link between machines and mind. To assert that substantial improvements in GP may be obtained in the future only by renewed interest in human-inspired models of reasoning and in other cognitive studies. In fact, if we increase the complexity of games (from NP-Completeness to AI-Completeness) in order to reproduce real-life problems, computer science techniques enter an impasse. Many of AI’s recent GP experiences can be shown to validate this. The lack of consistent philosophical foundations for cognitive AI and the minimal philosophical commitment of AI investigation are two of the major reasons that play an important role in explaining why CP has been overlooked. (shrink)

The author discusses the problem of what AI is and how we can understand the “anthropocentrism” of AI from a philosophical point of view. Three interpretations of the relationship between humans and technology are presented: 1) AI-aided human intelligence, based on the extension theory of technology (Stiegler, 1998); 2) human-aided AI intelligence, based on ideas related to political economy (Crawford, 2021); and 3) the relativity of the anthropocentric frame of reference (AFR), based on the taxonomy of species. The purpose (...) of the article is to provide considerations of the three different interpretations of the relationship between humans and technology and how they lead to different opportunities to answer the question of how we can understand AI’s creativity. (shrink)

The paper identifies and assesses the implications of two approaches to the field of artificial intelligence and legal reasoning. The first — pragmatism — concentrates on the development of working systems to the exclusion of theoretical problems. The second — purism — focuses on the nature of the law and of intelligence with no regard for the delivery of commercially viable systems. Past work in AI and law is classified in terms of this division. By reference to The Latent (...) Damage System, an operational system, the paper articulates and responds to conceivable purist (jurisprudential and AI) objections to such a program. The methods of the pragmatist are also called into question and refined. The author concludes that pragmatism within a purist framework is the only sound approach to developing reliable AI systems in law. (shrink)

For a variety of reasons, consciousness and selfhood are beginning once again to be intensively studied in a scientific frame of reference. The notions of each which are emerging are extremely varied: in the case of selfhood, the lack of an adequate vocabulary to capture various aspects of subjectivity has led to deep confusion. The task of the first part of this article is to clear up this terminological confusion, while salvaging whatever is valuable from the contemporary discussion. (...) The more important task of the second part is to discuss the moral issues inevitably involved in any treatment, scientific or otherwise, of the modern identity. (shrink)

Each of the chapters in this volume devotes considerable attention to defining and elaborating the notion of the frame problem-one of the hard problems of artificial intelligence. Not only do the chapters clarify the problems at hand, they shed light on the different approaches taken by those in artificial intelligence and by certain philosophers who have been concerned with related problems in their field. The book should therefore not be read merely as a discussion of the (...) class='Hi'>frame problem narrowly conceived, but also as a general analysis of what could be a major challenge to the design of computer systems exhibiting general intelligence. (shrink)

Consciousness is extremely familiar yet it is at the limits—beyond the limits, some would say—of what one can sensibly talk about or explain. Perhaps this is the reason its study has drawn contributions from many fields including psychology, neuroscience, philosophy, anthropology, cultural and literary theory, artificial intelligence, physics, and others. The focus of this entry is on: the varieties of consciousness, different problems that have been raised about these varieties, and prospects for progress on these problems.

In lieu of an abstract, here is a brief excerpt of the content:On the Conceivability of Artificially Created EnlightenmentPaul Andrew PowellPointsman can only possess the zero and the one. He cannot, like [Roger] Mexico, survive anyplace in between.... [H]e imagines the cortex of the brain as a mosaic of tiny on/off elements.... [E]ach point is allowed only the two states:... [o]ne or zero.... [B]rain mechanics assumes the presence of these bi-stable points....If ever the Anti-pointsman existed, Roger Mexico is the man.... (...) How can Mexico play, so at his ease, with these symbols of randomness and fright? Innocent as a child, perhaps unaware—perhaps—that in his play he wrecks the elegant rooms of history.—Thomas Pynchon, Gravity's RainbowWhen I first read about the Dalai Lama's remark that "if all the external conditions and the karmic action were there, a stream of consciousness might actually enter into a computer,"1 I couldn't help but wonder if a computer could then attain enlightenment. The answer, at first, seemed simple and obvious (though perhaps distasteful for some): if the machine was self-aware, sentient, and conscious, then it would be only reasonable to assume that it could also attain enlightenment. Yes, simple and obvious, of course, but this takes for granted some very large and complex issues. After all, what makes for self-awareness, sentience, and consciousness to begin with, and how would we manufacture them in a machine?If we take a moment to think about the creation of artificial enlightenment the paradox becomes clear. In order to manufacture enlightenment, we must first manufacture samsara, because for a Buddhist, enlightenment is the "event" of deconstructing the problematic natures of "self," sentience (suchness), and consciousness, all of which are samsaric illusions. "Self," sentience, and consciousness represent the prerequisites of samsara/Nirvana, realized or unrealized, artificial or organic. They represent the prerequisites of desire. They represent the prerequisites for the illusion of death and birth. And thus they represent the truly salient issues that must be addressed before we can assemble our complicated hard (and soft) ware. Interestingly, because computational information consists of a conceptualized, dualistic [End Page 123] binary system of ones and zeroes, and because the nature of Nirvana/samsara also consists of a conceptualized, dualistic system of "self"/"other," technology, in theory anyway, may probably be capable of manufacturing a facsimile of samsara, if only because computational information is, by its very nature, samsaric to begin with. Of course, all this demands that consciousness, self-awareness, and sentience be a function of information processing. Personally, I believe they are, and that, in fact, the words information and samsara are synonymous with one another. Thus it follows that information technology will most certainly be capable of manufacturing the prerequisites, at least, of enlightenment. If that blunt statement strikes you as utterly preposterous, technically impossible, or even offensive, you might want to first read any number of excellent texts describing the future of artificial intelligence (AI). Two of the best popular books on the subject are Robot by Hans Moravec and The Age of Spiritual Machines by Ray Kurzweil. These will assuage your doubts concerning the soft problem, or, as it is sometimes called, the nuts-and-bolts problem.With these considerations in mind, let's see what we can come up with.In order to arrive at what you are not You must go through the way in which you are not.—T.S. Eliot, "East Coker" (The Four Quartets)For most Buddhists, enlightenment can be defined as seeing through the illusion of the self and experiencing unadulterated suchness. In the words of Master Wolfgang Kopp, "The seer, the seen, and the process of seeing are one. The thinker, the thought, and the process of thinking fall together into one and multiplicity melts away. There is neither inside, nor outside in this state. There is only 'suchness,' tathata."2 So, enlightenment is suchness, or, things as they are, revealed as all that there is. As engineers of enlightenment, it would appear then that are at least two components we need to build into our machine: an illusionary "self" and suchness, or things as they are. As we shall see, the... (shrink)

The chapters in this book have evolved from talks originally presented at The First International Workshop on Human and Machine Cognition.

Background: Artificial intelligence (AI) is the simulation of human intelligence processes by machines, such as learning, reasoning, and problem-solving. AI has the potential to transform the field of public health, which is concerned with promoting and protecting the health of populations and preventing diseases. AI can help public health organizations perform their essential functions more efficiently, effectively, and equitably; AI can transform the public health field, but it also poses some challenges and risks that must be addressed carefully and (...) responsibly. Objectives: This paper reviews the current and potential applications of AI in public health, discusses the opportunities and challenges of AI for public health, and provides recommendations for the ethical and responsible use of AI in public health. Results: AI can improve the speed and accuracy of diagnosis, screening, and treatment of various diseases and support disease surveillance, outbreak response, and health systems management. However, AI poses significant challenges and risks, such as ethical, legal, and social implications, data quality and security, algorithmic bias and fairness, and environmental impact. Conclusion: AI has the potential to revolutionize public health, but it comes with risks that must be addressed. Promoting digital literacy, establishing modern data governance frameworks, and investing in advanced data infrastructure and procedures are essential. Public health organizations must also train their workforce to collaborate with AI. By doing so, they can improve health outcomes, reduce health disparities, and advance public health science and practice. (shrink)

The development of the mind follows the path of biological evolution towards the accumulation and transmission of information with increasing efficiency. In addition to the cognitive constants of speech (Solntsev, 1974), which greatly improved the transmission of information, people have created computing devices, from the abacus to the quantum computer. The capabilities of computers classified as artificial intelligence are developing at a rapid pace. However, at the present stage, artificial intelligence (AI) lacks an emotion module, and this makes (...) AI fundamentally different from human intelligence, since the life of the mind in humans cannot be separated from their feelings (Damasio, 2010; Panksepp, 1997). Consciousness itself is formed through the sensory and motor systems, that is, it is embodied (Foglia & Wilson, 2013), which means that our mental life is inseparable from our sensory motor experience (Wellsby & Pexman, 2014). Evolutionarily, our minds rely on ancient survival mechanisms that influence our decisions and choices. Hence, for example, the question whether the choice of Artificial Intelligence will always be favorable for humanity. (shrink)

In 1969, John McCarthy and Pat Hayes uncovered a problem that has haunted the field of artificial intelligence ever since--the frame problem. The problem arises when logic is used to describe the effects of actions and events. Put simply, it is the problem of representing what remains unchanged as a result of an action or event. Many researchers in artificial intelligence believe that its solution is vital to the realization of the field's goals. Solving the Frame (...) Problem presents the various approaches to the frame problem that have been proposed over the years. The author presents the material chronologically--as an unfolding story rather than as a body of theory to be learned by rote. There are lessons to be learned even from the dead ends researchers have pursued, for they deepen our understanding of the issues surrounding the frame problem. In the book's concluding chapters, the author offers his own work on event calculus, which he claims comes very close to a complete solution to the frame problem. Artificial Intelligence series. (shrink)

The development of the systems of artificial intelligence (AI) and digital transformation in general lead to the formation of multitude of autonomous agents of artificial and mixed genealogy, as well as to complex structures in the information and regulatory environment with many opportunities and pathologies and a growing level of uncertainty in making managerial decisions. The situation is complicated by the continuing plurality of understanding of the essence of AI systems. The modern expanded understanding of AI goes back (...) to ideas formulated more than 100 years ago. In official national policy documents on the development of AI, working definitions of AI are preferred. The current stage of AI systems life cycle can be assessed as the completion of the initial period in the development of systems associated with weak AI. The ability of artificial systems to realize themselves as a separate person becomes one of the serious scientific and practical challenges. Attention to the issues of the ethics of AIS indicates the expansion of the diversity of its forms and the beginning of the work in the field of goal-setting. New moral and ethical problems also arise in connection with the possibility of the creation of genuine conscious subjects in the foreseeable future. There is an increasing phenomenon of degradation of natural intelligence. It is required to take into account the issue of the heterogeneity of data generated by humans, electronic sensors and network devices in the dynamic complex environments of the digital economy, the issue of the complexity of the process of co-evolution of AI systems, collective and individual natural consciousness. A special area of opportunities and risks is the development of neurotechnologies. The object of control is digital twins, through which there can be manipulation of real attitudes, preferences, and behavior of individuals. As a result, there are the development of technological capabilities that provoke destructive phenomena as well as the formation of a new class of mass addictions. (shrink)

Over the coming decades, Artificial Intelligence will profoundly impact the way we live, work, wage war, play, seek a mate, educate our young, and care for our elderly. It is likely to greatly increase our aggregate wealth, but it will also upend our labor markets, reshuffle our social order, and strain our private and public institutions. Eventually it may alter how we see our place in the universe, as machines pursue goals independent of their creators and outperform us in (...) domains previously believed to be the sole dominion of humans. Whether we regard them as conscious or unwitting, revere them as a new form of life or dismiss them as mere clever appliances, is beside the point. They are likely to play an increasingly critical and intimate role in many aspects of our lives. The emergence of systems capable of independent reasoning and action raises serious questions about just whose interests they are permitted to serve, and what limits our society should place on their creation and use. Deep ethical questions that have bedeviled philosophers for ages will suddenly arrive on the steps of our courthouses. Can a machine be held accountable for its actions? Should intelligent systems enjoy independent rights and responsibilities, or are they simple property? Who should be held responsible when a self-driving car kills a pedestrian? Can your personal robot hold your place in line, or be compelled to testify against you? If it turns out to be possible to upload your mind into a machine, is that still you? The answers may surprise you. (shrink)

Humans have invented intelligent machinery to enhance their rational decision-making procedure, which is why it has been named ‘augmented intelligence’. The usage of artificial intelligence (AI) technology is increasing enormously with every passing year, and it is becoming a part of our daily life. We are using this technology not only as a tool to enhance our rationality but also heightening them as the autonomous ethical agent for our future society. Norbert Wiener envisaged ‘Cybernetics’ with a view of a (...) brain-machine interface to augment human beings' biological rationality. Being an autonomous ethical agent presupposes an ‘agency’ in moral decision-making procedure. According to agency's contemporary theories, AI robots might be entitled to some minimal rational agency. However, that minimal agency might not be adequate for a fully autonomous ethical agent's performance in the future. If we plan to implement them as an ethical agent for the future society, it will be difficult for us to judge their actual stand as a moral agent. It is well known that any kind of moral agency presupposes consciousness and mental representations, which cannot be articulated synthetically until today. We can only anticipate that this milestone will be achieved by AI scientists shortly, which will further help them triumph over ‘the problem of ethical agency in AI’. Philosophers are currently trying a probe of the pre-existing ethical theories to build a guidance framework for the AI robots and construct a tangible overview of artificial moral agency. Although, no unanimous solution is available yet. It will land up in another conflicting situation between biological, moral agency and autonomous ethical agency, which will leave us in a baffled state. Creating rational and ethical AI machines will be a fundamental future research problem for the AI field. This paper aims to investigate ‘the problem of moral agency in AI’ from a philosophical outset and hold a survey of the relevant philosophical discussions to find a resolution for the same. (shrink)

This book aims to lay bare the logical foundations of tractable reasoning. It draws on Marvin Minsky's seminal work on frames, which has been highly influential in computer science and, to a lesser extent, in cognitive science. Only very few people have explored ideas about frames in logic, which is why the investigation in this book breaks new ground. The apparent intractability of dynamic, inferential reasoning is an unsolved problem in both cognitive science and logic-oriented artificial intelligence. By means (...) of a logical investigation of frames and frame concepts, Andreas devises a novel logic of tractable reasoning, called frame logic. Moreover, he devises a novel belief revision scheme, which is tractable for frame logic. These tractability results shed new light on our logical and cognitive means to carry out dynamic, inferential reasoning. Modularity remains central for tractability, and so the author sets forth a logical variant of the massive modularity hypothesis in cognitive science. (shrink)

Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first‐order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and (...) actions. It may be possible to implement a temporal‐inference machine based on this logic, which keeps track of several “maps” of a time line, one per possible history. (shrink)

The economic calculation of a central planner has traditionally been argued to result in irrational and inefficient allocation of resources, but this can be reasonably questioned given advances in computing technology, especially artificial intelligence. We conclude that central planning coupled with AI is still unable to allocate resources with the same efficiency as price signals and market forces through examinations of the technical structure of current AI approaches. AI-driven central planning is not viable in part due to incentives, computing (...) power, knowledge/data acquisition, and speed of collection. There are deep incentive problems for planners, programmers, and ordinary participants to complicate efforts at planning and bias data. Most importantly, AI cannot easily or quickly duplicate the signals of relative scarcity that are generated in markets. Some challenges we highlight are pertinent to planning generally, but many others arise from the introduction of an AI-planner. (shrink)

In this article, the potential for Artificial Intelligence (AI) to be appraised as an object of beauty is critically examined through the lens of philosophical thought. Tracing beauty’s evolution from Platonic ideals to contemporary interpretations, the analysis contends that AI’s emergence offers a unique illustration of beauty in the modern age. Confronting the challenge of assigning beauty to entities devoid of consciousness or emotional depth, the argument unfolds to suggest that the intricate design of AI’s algorithms and its (...) technological advancements constitute an emergent form of beauty, one inherently tied to human intellectual endeavor. The article proposes that with time, as with historical art forms, the aesthetic qualities of AI may gain broader acknowledgment, implying that technological advancements necessitate an ongoing revision of established beauty norms. The historical evolution of beauty has been traced in details, and an extended literature review has been provided, serving as the foundation for a comprehensive exploration into the potential for AI to be recognized as an object of beauty. The harmony, elegance, and problem-solving capabilities of AI algorithms have been meticulously evaluated, with a comparison made to classical elements of beauty in an effort to contextually situate AI within longstanding aesthetic traditions. Furthermore, the analysis has been extended to consider the emergence of artificial and digital beauty, alongside an investigation into the parallels drawn between divine and artificial intelligence. As part of a deeper inquiry, the possibility of human emotions, such as awe and love, being authentically directed towards AI has been scrutinized. In conclusion, a reflection has been presented on the transformative role of AI in reimagining the conceptual landscape of beauty, prompting readers to thoughtfully engage with an extended review and the philosophical and cultural implications emerging from AI’s evolving influence on the field of aesthetics. (shrink)