The Nature of Artificial Intelligence

Social machines are systems formed by technical and human elements interacting in a structured manner. The use of digital platforms as mediators allows large numbers of human participants to join such mechanisms, creating systems where interconnected digital and human components operate as a single machine capable of highly sophisticated behaviour. Under certain conditions, such systems can be described as autonomous and goal-driven agents. Many examples of modern Artificial Intelligence (AI) can be regarded as instances of this class of mechanisms. We (...) argue that this type of autonomous social machines has provided a new paradigm for the design of intelligent systems marking a new phase in the field of AI. The consequences of this observation range from methodological, philosophical to ethical. On the one side, it emphasises the role of Human-Computer Interaction in the design of intelligent systems, while on the other side it draws attention to both the risks for a human being and those for a society relying on mechanisms that are not necessarily controllable. The difficulty by companies in regulating the spread of misinformation, as well as those by authorities to protect task-workers managed by a software infrastructure, could be just some of the effects of this technological paradigm. (shrink)

What does it truly mean to “understand”? The Chinese Room Argument asserts that AI, no matter how advanced, merely manipulates symbols without grasping meaning, while human cognition is uniquely capable of true understanding. But if human intelligence itself is built upon memorization, structured abstraction, and computational complexity, then is understanding anything more than an emergent property of hierarchical information processing? This paper argues that Searle’s framework rests on anthropocentric assumptions that fail to account for the variance in meaning structures between (...) human and artificial cognition. The claim that syntax alone cannot generate semantics relies on an outdated view of cognition, ignoring how meaning emerges differently across self-organizing systems. Furthermore, Searle’s demand that true understanding requires human-like intentionality is a category error, conflating distinct computational architectures with vastly different processing scales. By examining hierarchical abstraction, computational self-organization, and the mechanistic basis of understanding, this paper dismantles Searle’s framework and proposes that meaning is system-relative—not an exclusive product of human cognition, but a function of computational complexity. (shrink)

[This manuscript is outdated; read chapter 7 of my book "Information, Intelligence and Idealism" instead, which is also available as full text on PhilPapers] 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)

The irrational exuberance associated with contemporary artificial intelligence (AI) reminds me of Charles Dickens: "it was the age of foolishness, it was the epoch of belief" (cf. Nature Editorial, 2016; to get a feel for the vanity fair that is AI, see Mitchell and Krakauer, 2023; Stilgoe, 2023). It is particularly distressing—feels like yet another rerun of Seinfeld, which is all about nothing (pun intended); we have seen it in the 60s and again in the 90s. AI might have had (...) an inauspicious beginning, which may very well be the original sin, and our current predicament is the attendant divine condemnation. Being unable to stand it, however divine it may be, without further ado, I get to the genesis (instead of getting lost in the here-and-now of this or that so-called breakthrough of AI). Here's my unvarnished critique! (shrink)

Contains a description of a generalized and constructive formal model for the processes of subjective and creative thinking. According to the author, the algorithm presented in the article is capable of real and arbitrarily complex thinking and is potentially able to report on the presence of consciousness.

“Great pain urges all animals, and has urged them during endless generations, to make the most violent and diversified efforts to escape from the cause of suffering. Even when a limb or other separate part of the body is hurt, we often see a tendency to shake it, as if to shake off the cause, though this may obviously be impossible.” —Charles Darwin[1].

Are the real numbers rich enough to measure intelligence? We generalize a result of Alexander and Hutter about the so-called Legg-Hutter intelligence measures of reinforcement learning agents. Using the generalized result, we exhibit a paradox: in one particular version of the Legg-Hutter intelligence measure, certain agents all have intelligence 0, even though in a certain sense some of them outperform others. We show that this paradox disappears if we vary the Legg-Hutter intelligence measure to be hyperreal-valued rather than real-valued.

In this paper, we pursue three general aims: (I) We will define a notion of fundamental opacity and ask whether it can be found in High Energy Physics (HEP), given the involvement of machine learning (ML) and computer simulations (CS) therein. (II) We identify two kinds of non-fundamental, contingent opacity associated with CS and ML in HEP respectively, and ask whether, and if so how, they may be overcome. (III) We address the question of whether any kind of opacity, contingent (...) or fundamental, is unique to ML or CS, or whether they stand in continuity to kinds of opacity associated with other scientific research. (shrink)

Modern AI systems based on deep learning are neither traditional tools nor full-blown agents. Rather, they are characterised by idiosyncratic agential profiles, i.e., combinations of agency-relevant properties. Modern AI systems lack superficial features which enable people to recognise agents but possess sophisticated information processing capabilities which can undermine human goals. I argue that systems fitting this description, when they are adversarial with respect to human users, pose particular risks to those users. To explicate my argument, I provide conditions under which (...) agential profiles are explanatorily relevant to harms caused. I then contend that the role of recommender systems in producing harmful outcomes like digital addiction satisfies these conditions. (shrink)

Will we make machines as smart, as dumb, as creative, as whiny, as human beings—or are we wasting our time trying? What makes human cognition unique and irreplaceable—if anything? With James Moor , Drew Arrowood , and Valerie Hardcastle.

Throughout the past century, technology integration into the human body has experienced remarkable growth, finding its expression through specific artistic movements that have acted as catalysts for acceptance and societal transformation. This article delves into the examination of three paradigm shifts that underpin this profound evolution. The first shift marks a departure from curative treatments for disabilities, shifting towards a focus on augmentative improvements. Subsequently, the second shift propelled the enhancement of healthy bodies, driven by the visionary concept of posthumanism, (...) which envisions transcending the limits of our species through technology. Today, we find ourselves amidst the third paradigm shift, characterized by the practical cyborgization of society, made possible by the emergence of new cyborgs. By critically reflecting on the practices of these cyborgs, we gain valuable insights into the potential challenges and implications that technology may pose in the future. Crucially, this research highlights the indispensable role that art and aesthetics have played in shaping the social perception of technology integrated into the human body. Art, serving as a safer avenue for experimentation than scientific research, has become instrumental in presenting and normalizing these groundbreaking ideas within the public imagination. By exploring the intersections of art, technology, and human bodies, we can navigate the complex terrain of our ever-evolving relationship with technology and anticipate the profound implications it holds. (shrink)

This publication constitutes a comprehensive account of the panel discussion entitled “Philosophical and Ethical Analysis of the Concepts of Death and Human Existence in the Context of Cybernetic Immortality” which transpired within the confines of the international scientific symposium “The Seventh Lemovsky Readings” held in Samara from March 28th to 30th, 2024. The aforementioned panel discussion, which congregated scores of erudite scholars representing preeminent research institutions across the Russian Federation, emerged as one of the cardinal events of the conference. Eminent (...) Russian academics hailing from multifarious domains of knowledge engaged in a profound deliberation on the potentialities of cybernetic immortality, the conundrums associated with the transference of human consciousness and identity into the virtual realm, as well as the ethical and philosophical quandaries engendered by the transfiguration of the conceptualization of the notions of mortality and the natural life cycle. The roundtable delved into the technological feasibilities and perils of engendering digital doppelgangers, the preservation of individuality after corporeal demise, as well as the plausible ramifications of the technological modification of the human essence. (shrink)

Playing chess is one of the first sectors of human thinking that were conquered by computers. From the historical win of Deep Blue against chess champion Garry Kasparov until today, computers have completely dominated the world of chess leaving no room for question as to who is the king in this sport. However, the better computers become in chess the more obvious their basic disadvantage becomes: Even though they can defeat any human in chess and play phenomenally great and intuitive (...) moves, they do not seem to know what chess it. Recently, the advent of AlphaZero brought the level of computers to even higher grounds, but made that disadvantage even more obvious. Nowadays the best chess algorithm can find the best moves in any position without having any knowledge of any chess principle whatsoever. A new world of capabilities and knowledge lays open in front of us, but the darkness behind it is deeper than we could ever imagine. The neural network algorithms seem capable of being best in something without even knowing what that something is, making questions regarding the essence of what thought is more important than ever. (shrink)

The rapid advancements in artificial intelligence, particularly in natural language processing, have brought to light a critical challenge, i.e., the semantic grounding problem. This article explores the root causes of this issue, focusing on the limitations of connectionist models that dominate current AI research. By examining Noam Chomsky's theory of Universal Grammar and his critiques of connectionism, I highlight the fundamental differences between human language understanding and AI language generation. Introducing the concept of semantic grounding, I emphasise the need for (...) AI to connect language with real-world experiences and context to achieve true understanding. While multi-modal models represent a step forward, they fall short of addressing the core issue. The article concludes with a call for a course correction in AI development, advocating for the integration of embodied cognition, dynamic learning, hybrid models, and ethical practices. This shift is essential for creating AI systems that can meaningfully interact with the world, reducing the incidence of hallucinations and enhancing their reliability and utility. (shrink)

Landgrebe and Smith provide an argument for the impossibility of Artificial General Intelligence based on the limits of simulating complex systems. However, their argument presupposes a very contemporary vision of artificial intelligence as a model trained on data to produce an algorithm executable in a modern digital computing system. The present contribution explores what it means to be artificial. Current artificial intelligence approaches on modern computing systems are not the only conceivable way in which artificial intelligence technology might be created. (...) If there are conceivable routes by which an artificial intelligence might be developed that are not constrained in the same way as the current generation of artificial neural networks, then these might be plausible routes towards the engineered generation of artificial general intelligence that are not precluded by the Landgrebe and Smith argument against the possibility of artificial general intelligence. (shrink)

Artificial intelligence is man's attempt to use software to emulate the intelligence of human beings. But the complexity of the human neurological system formed in the course of evolution is impossible to replicate: "Human languages and societies are complex systems, indeed complex systems of many complex systems," so much so that their mathematical modeling is impossible. Barry Smith, philosopher and professor at the University at Buffalo. shows no uncertainty about this. His latest book written with Jobst Landgrebe, a mathematician and (...) entrepreneur in the AI field, leaves no doubt – starting with the title: Why machines will never rule the world (Routledge 2023). The book was presented in Turin during an evening organized by the Fondazione CRT and the Bruno Leoni Institute, in which Smith was interviewed based on questions developed by generative Ai. We have accepted the challenge to conduct an interview parallel to the one done with GPT4. (shrink)

This research examines how computer-generated art (CG-art) is reshaping the notion of artistic creativity in the current age of Artificial Intelligence (AI). In this context, this study proposes to refine the concept of CG-art by delimiting what an AI-generated artwork is. This new term has its roots in Cognitive Science, Aesthetics and Computer Science, emphasising their intersections. It involves the conjunction of three elements: (1) an autonomous AI-production of a new and surprising idea or artefact, (2) which passes an internal (...) evaluation mechanism embedded in the very same AI, and (3) is considered a candidate of appreciation by a human audience. This definition helps to analyse several aspects of CG-art by determining if they meet the requirements for being considered AI-generated artworks. To illustrate this, the Deep Dream algorithm, the painting Portrait of Edmond de Belamy, and the music albums I AM AI and PROTO are discussed. (shrink)

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. This position paper takes the stand that, in the light of all we currently know about brain evolution (...) and the never-stopping formation of adaptive neural circuitry for learning, memory, decision making and, ultimately, fully conscious reasoning and creativity in the human species, the idea of an artificial consciousness appears misconceived. The paper highlights some of the major reasons why. While awareness to external stimuli for processes such as perception, recognition, and operational problem solving is under the direct control of functionally specific brain networks associated with sensory and cognitive functions across animal species, consciousness is a unique property of the human mind. (shrink)

La scorciatoia - Come le macchine sono diventate intelligenti senza pensare in modo umano -/- Le nostre creature sono diverse da noi e talvolta più forti. Per poterci convivere dobbiamo imparare a conoscerle Vagliano curricula, concedono mutui, scelgono le notizie che leggiamo: le macchine intelligenti sono entrate nelle nostre vite, ma non sono come ce le aspettavamo. Fanno molte delle cose che volevamo, e anche qualcuna in più, ma non possiamo capirle o ragionare con loro, perché il loro comportamento è (...) in realtà guidato da relazioni statistiche ricavate da quantità sovrumane di dati. Eppure possono essere in certi casi più potenti di noi: ci osservano continuamente, e prendono decisioni al nostro posto. E allora come incorporarle nella nostra società senza rischi ed effetti collaterali? Questo libro - rigoroso, pungente, originale nell'approccio - ci spiega come siamo arrivati sin qui, e indica il percorso che ci aspetta prima di poterci fidare di questi nuovi agenti «alieni». La tecnologia non basta, occorre un dialogo tra scienze naturali e umane: è il passaggio cruciale per una convivenza sicura con questa nuova forma di intelligenza. (Il Mulino). (shrink)

This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liberty, authenticity, and fairness in more detail. (...) We conclude that while there are very significant technical hurdles to real human enhancement through AI, and significant ethical problems, there are also significant benefits that may realistically be achieved in ways that are consonant with a rights-based ethics as well. We also highlight the specific concerns that apply particularly to applications of AI for "sheer" IA (more realistic in the near term), and to enhancement applications, respectively. (shrink)

As artificial intelligence (AI) becomes progressively more engaged with society, its shift from technical tool to participating in society raises questions about AI personhood. Drawing upon developmental psychology and systems theory, a mediating structure for AI proto-personhood is defined analogous to an early stage of human development. The proposed AI bridges technical, psychological, and theological perspectives on near-future AI and is structured by its hardware, software, computational, and sociotechnical systems through which it experiences its world as embodied (even for putatively (...) disembodied AI). Further social and moral construction can occur building upon a simple “self” for AI synthesized from symbolic and statistical approaches to AI. (shrink)

The notion of intelligence is relevant to several fields of research, including cognitive and comparative psychology, neuroscience, artificial intelligence, and philosophy, among others. However, there is little agreement within and across these fields on how to characterise and explain intelligence. I put forward a behavioural, operational characterisation of intelligence that can play an integrative role in the sciences of intelligence, as well as preserve the distinctive explanatory value of the notion, setting it apart from the related concepts of cognition and (...) rationality. Finally, I examine a popular hypothesis about the underpinnings of intelligence: the capacity to manipulate internal representations of the environment. I argue that the hypothesis needs refinement, and that so refined, it applies only to some forms of intelligence. (shrink)

The expanding social role and continued development of artificial intelligence (AI) needs theological investigation of its anthropological and moral potential. A pragmatic theological anthropology adapted for AI can characterize moral AI as experiencing its natural, social, and moral world through interpretations of its external reality as well as its self-reckoning. Systems theory can further structure insights into an AI social self that conceptualizes itself within Ignacio Ellacuria’s historical reality and its moral norms through Thomistic ideogenesis. This enables a conceptualization process (...) capable of carrying moral weight and grounded in reality; structures the experience of an AI emergent self into multiple levels of interpretation; and drives a multi-level systems architecture for moral AI. Modeling AI’s interpretive experience and self-reckoning as a causal, sociotechnical, and moral actor can help moral AI identify conflicts between its normative values and develop the practical wisdom (phronesis) needed to apply its general moral models. (shrink)

In his 1950 paper “Computing Machinery and Intelligence,” Alan Turing proposed that we can determine whether a machine thinks by considering whether it can win at a simple imitation game. A neutral questioner communicates with two different systems – one a machine and a human being – without knowing which is which. If after some reasonable amount of time the machine is able to fool the questioner into identifying it as the human, the machine wins the game, and we should (...) conclude that it thinks. This imitation game, now known as the Turing Test, has been much discussed by philosophers of mind, and for more than half a century now there has been considerable debate about whether it is an adequate test for thinking. But what has not been much discussed are the sexed presuppositions underlying the test. Too often forgotten in the philosophical discussion is the fact that Turing’s imitation game is modeled on an imitation game in which a neutral questioner communicates with two different humans – one a man and one a woman – without knowing which is which. In this original imitation game, the man wins the game if he is able to fool the questioner into identifying him as the woman. In this paper, I explore the implications of this set-up. As I argue, the fact that the Turing test was modeled on a man/woman imitation game seems to have led us astray in various ways in our attempt to conduct an effective investigation and assessment of computer intelligence. (shrink)

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as (...) argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence. (shrink)

Theologians often reflect on the ethical uses and impacts of artificial intelligence, but when it comes to artificial intelligence techniques themselves, some have questioned whether much exists to discuss in the first place. If the significance of computational operations is attributed rather than intrinsic, what are we to say about them? Ancient thinkers—namely Augustine of Hippo (lived 354–430)—break the impasse, enabling us to draw forth the moral and metaphysical significance of current developments like the “deep neural networks” that are responsible (...) for some of the most remarkable achievements of the past few years. First, Augustine’s theology of the natural world’s rationes seminales makes sense of neural networks’ success by explaining the world as a kaleidoscopic refraction of divine Wisdom rather than as a collection of discrete objects standing in crisp relations. Second, his account of interpretive judgment as a moral act bound up with love reveals how our training and use of AI relates to the Christian’s assimilation to Wisdom. The contingent meaning of the neural network reveals AI’s potential either for standing between us and the world or in some sense facilitating a Christian’s regathering of the created echoes of divine Wisdom throughout his or her journey into the Trinity. (shrink)

Can an agent's intelligence level be negative? We extend the Legg-Hutter agent-environment framework to include punishments and argue for an affirmative answer to that question. We show that if the background encodings and Universal Turing Machine (UTM) admit certain Kolmogorov complexity symmetries, then the resulting Legg-Hutter intelligence measure is symmetric about the origin. In particular, this implies reward-ignoring agents have Legg-Hutter intelligence 0 according to such UTMs.

Can humans and artificial intelligences share concepts and communicate? Making AI Intelligible shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can understand each other. In doing so, they illustrate ways in which that philosophical tradition can be improved. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. (...) Many important decisions about human life are now influenced by AI. In giving that power to AI, we presuppose that AIs can track features of the world that we care about (for example, creditworthiness, recidivism, cancer, and combatants). If AIs can share our concepts, that will go some way towards justifying this reliance on AI. This ground-breaking study offers insight into how to take some first steps towards achieving Interpretable AI. (shrink)

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...) intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (shrink)

This paper raises three questions regarding the attribution of beliefs, desires, and intentions to robots. The first one is whether humans in fact engage in robot mindreading. If they do, this raises a second question: does robot mindreading foster trust towards robots? Both of these questions are empirical, and I show that the available evidence is insufficient to answer them. Now, if we assume that the answer to both questions is affirmative, a third and more important question arises: should developers (...) and engineers promote robot mindreading in view of their stated goal of enhancing transparency? My worry here is that by attempting to make robots more mind-readable, they are abandoning the project of understanding automatic decision processes. Features that enhance mind-readability are prone to make the factors that determine automatic decisions even more opaque than they already are. And current strategies to eliminate opacity do not enhance mind-readability. The last part of the paper discusses different ways to analyze this apparent trade-off and suggests that a possible solution must adopt tolerable degrees of opacity that depend on pragmatic factors connected to the level of trust required for the intended uses of the robot. (shrink)

An argument with roots in ancient Greek philosophy claims that only humans are capable of a certain class of thought termed conceptual, as opposed to perceptual thought, which is common to humans, the higher animals, and some machines. We outline the most detailed modern version of this argument due to Mortimer Adler, who in the 1960s argued for the uniqueness of the human power of conceptual thought. He also admitted that if conceptual thought were ever manifested by machines, such an (...) achievement would contradict his conclusion. We revisit Adler’s criterion in the light of the past five decades of artificial-intelligence research, and refine it in view of the classical definitions of perceptual and conceptual thought. We then examine two well-publicized examples of creative works produced by AI systems and show that evidence for conceptual thought appears to be lacking in them. Although clearer evidence for conceptual thought on the part of AI systems may arise in the near future, especially if the global neuronal workspace theory of consciousness prevails over its rival, integrated information theory, the question of whether AI systems can engage in conceptual thought appears to be still open. (shrink)

Can an AGI create a more intelligent AGI? Under idealized assumptions, for a certain theoretical type of intelligence, our answer is: “Not without outside help”. This is a paper on the mathematical structure of AGI populations when parent AGIs create child AGIs. We argue that such populations satisfy a certain biological law. Motivated by observations of sexual reproduction in seemingly-asexual species, the Knight-Darwin Law states that it is impossible for one organism to asexually produce another, which asexually produces another, and (...) so on forever: that any sequence of organisms (each one a child of the previous) must contain occasional multi-parent organisms, or must terminate. By proving that a certain measure (arguably an intelligence measure) decreases when an idealized parent AGI single-handedly creates a child AGI, we argue that a similar Law holds for AGIs. (shrink)

We define a notion of the intelligence level of an idealized mechanical knowing agent. This is motivated by efforts within artificial intelligence research to define real-number intelligence levels of compli- cated intelligent systems. Our agents are more idealized, which allows us to define a much simpler measure of intelligence level for them. In short, we define the intelligence level of a mechanical knowing agent to be the supremum of the computable ordinals that have codes the agent knows to be codes (...) of computable ordinals. We prove that if one agent knows certain things about another agent, then the former necessarily has a higher intelligence level than the latter. This allows our intelligence no- tion to serve as a stepping stone to obtain results which, by themselves, are not stated in terms of our intelligence notion (results of potential in- terest even to readers totally skeptical that our notion correctly captures intelligence). As an application, we argue that these results comprise evidence against the possibility of intelligence explosion (that is, the no- tion that sufficiently intelligent machines will eventually be capable of designing even more intelligent machines, which can then design even more intelligent machines, and so on). (shrink)

The traditional Turing test appeals to an interrogator's judgement to determine whether or not their interlocutor is an intelligent agent. This paper argues that this kind of asymmetric experimental set-up is inappropriate for tracking a property such as intelligence because intelligence is grounded in part by symmetric relations of recognition between agents. In place, it proposes a reciprocal test which takes into account the judgments of both interrogators and competitors to determine if an agent is intelligent. This form of social (...) interaction better tracks both the evolution of natural intelligence and how the concept of intelligence is actually used within our society. This new test is defended against the criticisms that a proof of intelligence requires a demonstration of self-consciousness and that semantic externalism entails that a non-embodied Turing test is inadequate. (shrink)

We’ve seen a significant increase in the attention AI research is receiving this past decade, in large part due to some of the impressive feats of machine learning, particularly deep learning. This has resulted in something of a hype in the ability of AI’s in tackling various issues. The aim of the current essay is to examine the speculative questions “what would it mean for systems to transition from merely intelligently executing a task to knowledgably executing a task?” and “what (...) is the relation between consciousness and intelligence, such that specific evaluations about conscious AI might be made?” We offer what we hope is a roadmap for how to navigate asking these questions and a route forward in understanding how AI goes from intelligence to knowledge and potentially to some types of consciousness. (shrink)

Искусственный интеллект – последняя, хотя и иллюзорная надежда продажных и провалившихся режимов как на Западе, так и на Востоке остаться на плаву: ведь тонущий хватается и за соломинку. Но всё течёт и всё изменяется, и никаким деспотиям и деспотам не удастся остановить ход истории, как бы они этого не желали и тому не противились. Хотя у истории нет конца, но их история и история совершённых ими предательств уже закончилась. Plaudite, cives, plaudite, amici, finita est comoedia: „Рукоплещите, граждане, друзья, комедия окончена.“.

Il ya quelques années, j’ai atteint le point où je peux généralement dire à partir du titre d’un livre, ou du moins à partir des titres chapitre, quels types d’erreurs philosophiques seront faites et à quelle fréquence. Dans le cas des travaux théoriquement scientifiques, ceux-ci peuvent être largement limités à certains chapitres qui cire philosophique ou essayer de tirer des conclusions générales sur le sens ou la signification à long terme de l’œuvre. Normalement, cependant, les questions scientifiques de fait sont (...) généreusement entrecoupées de charabia philosophique quant à ce que ces faits signifient. Les distinctions claires que Wittgenstein a décrites il y a environ 80 ans entre les questions scientifiques et leurs descriptions par divers jeux linguistiques sont rarement prises en considération, et on est donc tour à tour séduit par la science et consterné par son analyse incohérente. Il en est de même pour ce volume. Si l’on veut créer un esprit plus ou moins comme le nôtre, il faut avoir une structure logique de rationalité et une compréhension des deux systèmes de pensée (théorie du double processus). Si l’on veut philosopher à ce sujet, il faut comprendre la distinction entre les questions scientifiques de fait et la question philosophique de la façon dont le langage fonctionne dans le contexte en cause, et de la façon d’éviter les pièges du réductionnisme et du scientisme, mais Kurzweil, comme la plupart des étudiants de comportement, est largement désemparé. Il est enchanté par les modèles, les théories et les concepts, et l’envie d’expliquer, tandis que Wittgenstein nous a montré que nous n’avons qu’à décrire, et que les théories, les concepts, etc., ne sont que des moyens d’utiliser le langage (jeux linguistiques) qui n’ont de valeur que dans la mesure où ils ont un test clair (les véridiques clairs, ou comme John Searle (le critique le plus célèbre de l’IA) aime à dire, claire conditions de satisfaction (COS)). J’ai essayé de donner un début à ce sujet dans mes écrits récents. (shrink)

몇 년전, 저는 보통 책의 제목이나 적어도 장 제목에서 어떤 종류의 철학적 실수를 저지르고 얼마나 자주 알 수 있는지 를 알 수 있는 지점에 도달했습니다. 명목상 과학적 작품의 경우, 이들은 크게 철학적 왁스 또는 의미 또는 긴에 대한 일반적인 결론을 그리려는 특정 장으로 제한 될 수있다-작업의기간 의의. 그러나 일반적으로 사실의 과학적 문제는 이러한 사실이 무엇을 의미하는지에 관해서는 철학적 횡설수설과 관대하게 얽혀있다. Wittgenstein이 약 80 년 전에 과학 문제와 다양한 언어 게임에 의한 설명 사이에 설명 한 명확한 차이점은 거의 고려되지 않으므로 (...) 과학에 의해 번갈아 열광하고 일관되지 않은 분석에 의해 실망합니다. 그래서이 볼륨입니다. 우리처럼 마음을 만들려면 합리성을 위한 논리적 구조와 두 가지 사상 체계(이중 프로세스 이론)에 대한 이해가 필요합니다. 이것에 대해 철학적으로 생각한다면, 사실의 과학적 문제와 언어가 문제의 맥락에서 어떻게 작동하는지에 대한 철학적 문제, 그리고 환원주의와 사이언티즘의 함정을 피하는 방법의 차이를 이해해야하지만, 커즈와일은 대부분의 행동 학생들처럼 대체로 단서가 없다. 그는 모델, 이론, 개념, 그리고 설명 하는 충 동에 의해 매료, Wittgenstein 우리가 설명 하는 데 필요한 것을 보여 주었다 하는 동안, 그리고 이론, 개념 등, 그들은 명확한 테스트 (명확한 진실 제작자, 또는 존 Searle (AI의 가장 유명한 비평가)를 좋아하는, 명확한 조건(명확한 진실 제작자)를 가지고 있는 한, 가치있는 언어 (언어 게임)를 사용하는 단지 방법입니다. 나는 나의 최근 글에서 이것에 대한 시작을 제공하기 위해 시도했다. 현대 의 두 시스템 보기에서인간의 행동에 대한 포괄적 인 최신 프레임 워크를 원하는 사람들은 내 책을 참조 할 수 있습니다'철학의 논리적 구조, 심리학, 민d와 루드비히 비트겐슈타인과 존 Searle의언어' 2nd ed (2019). 내 글의 더 많은 관심있는 사람들은 '이야기 원숭이를 볼 수 있습니다-철학, 심리학, 과학, 종교와 운명 행성에 정치 - 기사 및 리뷰 2006-2019 3 rd 에드 (2019) 및 21st 세기 4번째 에드 (2019) 및 기타에서 자살 유토피아 망상. 또한, AI/로봇 공학의 '사실' 계정에서 평소와 같이 그는 우리의 개인 정보 보호, 안전, 심지어 다른 저자 (Bostrum, 호킹등)에서 눈에 띄는 사회의 증가 '안드로이드화'에서 심지어 생존에 매우 실제 위협에 시간을 제공하지 않습니다, 그래서 나는 '좋은'안드로이드, 휴머노이드, 인공 지능 (AI), 인공 지능의 매우 자살 유토피아 망상에 대한 몇 가지 의견을 합니다. 저는 전자, 로봇 공학 및 AI의 기술적 발전이 사회에 중대한 변화를 초래할 것이라는 점을 당연하게 생각합니다. 그러나, 나는 유전 공학에서 오는 변경은 우리가 우리가 누구인지 완전히 바꿀 수 있게 해 줄 것이기 때문에 적어도 크고 잠재적으로 훨씬 더 크다고 생각합니다. 그리고 우리의 유전자 또는 다른 원숭이의 유전자를 수정하여 슈퍼 스마트 / 슈퍼 강한 종을 만드는 것이 가능할 것입니다. 다른 기술과 마찬가지로, 저항하는 모든 국가는 뒤에 남아있을 것입니다. 그러나 대규모로 바이오봇이나 초인간을 구현하는 것이 사회적, 경제적으로 실현 가능할까요? 그리고 그렇다 하더라도 인구 과잉, 자원 고갈, 기후 변화, 그리고 아마도 중국을 통치하는 일곱 사회경로의 독재적 통치에 의한 산업 문명의 파괴를 막기 위해 경제적, 사회적으로, 는 가능성이없어 보인다. 그래서, 이 볼륨의 철학적 실수를 무시하고, 과학에만 우리의 관심을 지시, 우리가 여기에 있는 것은 기본 생물학, 심리학 및 인간의 생태학, 미국과 세계를 파괴하는 동일한 망상을 파악하는 실패에 뿌리를 둔 또 다른 자살 유토피아 망상입니다. 나는 세계가 구원 될 수있는 원격 가능성을 볼 수 있지만, AI / 로봇 공학, CRISPR, 또는 신마르크스주의, 다양성과 평등에 의해. (shrink)

私は計算と宇宙の限界に関する最近の議論をコンピュータとして読み、ポリマス物理学者と意思決定理論家デビッド・ウォルパートの驚くべき仕事に関するいくつかのコメントを見つけることを望んでいますが、単一の引用 を見つけていないので、私はこの非常に簡単な要約を提示します。ウォルパートは、計算を行うデバイスから独立し、物理学の法則から独立している推論(計算)の限界に関する驚くべき不可能または不完全な定理(199 2年から2008年のarxiv.org参照)を証明したので、コンピュータ、物理学、人間の行動に適用されます。彼らは、カントールの対角化、嘘つきのパラドックス、ワールドラインを利用して、チューリングマシ ン理論の究極の定理である可能性のあるものを提供し、不可能、不完全性、計算の限界、そしてコンピュータとしての宇宙に関する洞察を提供し、すべての可能な宇宙とすべての存在またはメカニズムを生み出し、とりわけ 非量子機械不確実性原理と単一主義の証明を生み出します。チャイティン、ソロモノフ、コモルガロフ、ヴィトゲンシュタインの古典的な作品と、どのプログラム(したがってデバイスも)が所有するよりも複雑なシーケン ス(またはデバイス)を生成できないという考えには明らかなつながりがあります。この作品の体は、物理的な宇宙よりも複雑な存在はあり得ないので無テズムを意味すると言うかもしれませんし、ヴィトゲンチニアンの観 点から見ると、「より複雑な」は無意味です(満足の条件はありません、すなわち、真実のメーカーやテスト)。「神」(つまり、無限の時間/空間とエネルギーを持つ「デバイス」)でさえ、与えられた「数」が「ランダ ム」であるかどうかを判断したり、与えられた「公式」、定理または「文章」または「デバイス」(これらはすべて複雑な言語ゲームである)が特定の「システム」の一部であることを示す特定の方法を見つけることができ ません。 現代の2つのシス・エムスの見解から人間の行動のための包括的な最新の枠組みを望む人は、私の著書「ルートヴィヒ・ヴィトゲンシュタインとジョン・サールの第2回(2019)における哲学、心理学、ミンと言語の論 理的構造」を参照することができます。私の著作の多くにご興味がある人は、21世紀4日(2019年)の「話す猿--哲学、心理学、科学、宗教、政治」を見ることができます。 .

Несколько летназад, я достиг точки, где я обычно могу сказать из названия книги, или, по крайней мере, из названия глав, какие философские ошибки будут сделаны и как часто. В случае номинально научных работ они могут быть в значительной степени ограничены определенными главами, которые воск философские или попытаться сделать общие выводы о значении или долгосрочнойперспективезначение работы. Нормально однако научные вопросы факта великодушно interlarded с философски тарабаризом о чего эти факты значат. Четкое различие, которое Витгенштейн описал около 80 лет назад между научными (...) вопросами и их описаниями различными языковыми играми, редко принимаются во внимание, и поэтому человек поочередно поражается наукой и встревожен ее бессвязным анализом. Так и с этим объемом. Если создать ум, более или менее похожий на наш, нужно иметь логическую структуру рациональности и понимание двух систем мышления (теория двойного процесса). Если философия об этом, нужно понимать различие между научными вопросами факта и философский вопрос о том, как язык работает в контексте, о котором идет речь, и о том, как избежать ловушек редукционизма и саентизма, но Kurzweil, как и большинство студентов поведения, в значительной степени невежественны. Он очарован моделями, теориями и понятиями, и желание объяснить, в то время как Витгенштейн показал нам, что нам нужно только описать, и что теории, концепции и т.д., это просто способы использования языка (языковые игры), которые имеют значение только в той мере, в какой они имеют четкий тест (ясные truthmakers, или как Джон Сирл (самый известный критик AI) любит говорить, Я попытался обеспечить начало этого в моих последних трудах. Те, кто желает всеобъемлющего до современных рамок для человеческого поведения из современных двух systEms зрения могут проконсультироваться с моей книгой"Логическая структура философии, психологии, Минd иязык в Людвиг Витгенштейн и Джон Сирл" второй ред (2019). Те, кто заинтересован в более моих сочинений могут увидеть "Говоря обезьян - Философия, психология, наука, религия и политика на обреченной планете - Статьи и обзоры 2006-2019 3-й ed (2019) и suicidal утопических заблуждений в 21-мst веке 4-й ed (2019) th и другие. (shrink)

数年前、私は本のタイトルから、あるいは少なくとも章のタイトルから、どのような哲学的な間違いが起こり、どのくらいの頻度で分かることができるところまで達しました。名目上の科学的な研究の場合、これらは主に哲 学的なワックスや作品の意味または長期的な意義に関する一般的な結論を引き出そうとする特定の章に制限される可能性があります-。しかし、通常、事実の科学的な問題は、これらの事実が何を意味するのかについて、哲 学的なちんぷんかんぷんと寛大に絡み合っています。ヴィトゲンシュタインが約80年前に科学的な問題と様々な言語ゲームによる記述の間で述べた明確な区別はめったに考慮されないので、1つは交互に科学に驚き、その 支離滅裂な分析に失望しています。だから、このボリュームです。 多かれ少なかれ私たちのような心を作るのであれば、合理性と思考の2つのシステム(二重プロセス理論)の理解のための論理的な構造を持っている必要があります。このことについて哲学するならば、事実の科学的問題と 、問題となっている文脈における言語の仕組みの哲学的問題と、還元主義とサイエンティズムの落とし穴を避ける方法の区別を理解する必要がありますが、カーツワイルは、ほとんどの行動学生と同様に、ほとんど手がかり がない。彼はモデル、理論、概念、そして説明したいという衝動に魅了されていますが、ヴィトゲンシュタインは、私たちが記述する必要があり、理論、概念などは、明確なテストを持っている限り価値のある言語(言語ゲ ーム)を使用する方法にすぎないことを示しました(明確な真実主義者、またはジョン・サール(AIの最も有名な批評家)が言うのが好きです。私は最近の著作でこれに関するスタートを提供しようとしました。 現代の2つのシス・エムスの見解から人間の行動のための包括的な最新の枠組みを望む人は、私の著書「ルートヴィヒ・ヴィトゲンシュタインとジョン・サールの第2回(2019)における哲学、心理学、ミンと言語の論 理的構造」を参照することができます。私の著作の多くにご興味がある人は、運命の惑星における「話す猿--哲学、心理学、科学、宗教、政治―記事とレビュー2006-2019 第3回(2019)」と21世紀4日(2019年)の自殺ユートピア妄想st Century 4th ed (2019)などを見ることができます。 .

Alcuni anni fa, ho raggiunto il punto in cui di solito posso dire dal titolo di un libro, o almeno dai titoli dei capitoli, quali tipi di errori filosofici saranno fatti e con quale frequenza. Nel caso di opere nominalmente scientifiche queste possono essere in gran parte limitate a determinati capitoli che sono filosofici o cercanodi trarre conclusioni generali sul significato o sul significato a lungoterminedell'opera. Normalmente però le questioni scientifiche di fatto sono generosamente intrecciate con incomprodellami filosofici su ciò (...) che questi fatti significano. Le chiare distinzioni che Wittgenstein ha descritto circa 80 anni fa tra le questioni scientifiche e le loro descrizioni da parte di vari giochi linguistici sono raramente prese in considerazione, e quindi si è alternativamente stupiti dalla scienza e costerghi per la sua analisi incoerente. Così è con questo volume. Se si vuole creare una mente più o meno come la nostra, è necessario avere una struttura logica per la razionalità e la comprensione dei due sistemi di pensiero (teoria del doppio processo). Se si vuole filosofare su questo, bisogna capire la distinzione tra le questioni scientifiche di fatto e la questione filosofica di come funziona il linguaggio nel contesto in questione, e di come evitare le insidie del riduzionismo e dello scientismo, ma Kurzweil, come la maggior parte degli studenti di comportamento, è in gran parte all'oscuro. Egli è incantato da modelli, teorie e concetti, e dalla voglia di spiegare, mentre Wittgenstein ci ha mostrato che dobbiamo solo descrivere, e che le teorie, i concetti, ecc., sono solo modi di usare il linguaggio (giochi linguistici) che hanno valore solo nella misura in cui hanno un test chiaro (clear truthmakers, o come John Searle (il critico più famoso di AI) ama dire, chiare condizioni di soddisfazione (COS)). Ho cercato di dare un inizio su questo nei miei scritti recenti. Coloro che desiderano un quadro aggiornato completo per il comportamento umano dalla moderna vista a due systems possono consultare il mio libro 'La struttura logica della filosofia, psicologia, mente e il linguaggio in Ludwig Wittgenstein e John Searle' 2nd ed (2019). Coloro che sono interessati a più dei miei scritti possono vedere 'Talking Monkeys--Filosofia, Psicologia, Scienza, Religione e Politica su un Pianeta Condannato--Articoli e Recensioni 2006-2019 3rd ed (2019) e Suicidal Utopian Delusions in the 21st Century 5th ed (2019) . (shrink)

Alcuni anni fa, ho raggiunto il punto in cui di solito posso dire dal titolo di un libro, o almeno dai titoli dei capitoli, quali tipi di errori filosofici saranno fatti e con quale frequenza. Nel caso di opere nominalmente scientifiche queste possono essere in gran parte limitate a determinati capitoli che sono filosofici o cercanodi trarre conclusioni generali sul significato o sul significato a lungoterminedell'opera. Normalmente però le questioni scientifiche di fatto sono generosamente intrecciate con incomprodellami filosofici su ciò (...) che questi fatti significano. Le chiare distinzioni che Wittgenstein ha descritto circa 80 anni fa tra le questioni scientifiche e le loro descrizioni da parte di vari giochi linguistici sono raramente prese in considerazione, e quindi si è alternativamente stupiti dalla scienza e costerghi per la sua analisi incoerente. Così è con questo volume. Se si vuole creare una mente più o meno come la nostra, è necessario avere una struttura logica per la razionalità e la comprensione dei due sistemi di pensiero (teoria del doppio processo). Se si vuole filosofare su questo, bisogna capire la distinzione tra le questioni scientifiche di fatto e la questione filosofica di come funziona il linguaggio nel contesto in questione, e di come evitare le insidie del riduzionismo e dello scientismo, ma Kurzweil, come la maggior parte degli studenti di comportamento, è in gran parte all'oscuro. Egli è incantato da modelli, teorie e concetti, e dalla voglia di spiegare, mentre Wittgenstein ci ha mostrato che dobbiamo solo descrivere, e che le teorie, i concetti, ecc., sono solo modi di usare il linguaggio (giochi linguistici) che hanno valore solo nella misura in cui hanno un test chiaro (clear truthmakers, o come John Searle (il critico più famoso di AI) ama dire, chiare condizioni di soddisfazione (COS)). Ho cercato di dare un inizio su questo nei miei scritti recenti. Coloro che desiderano un quadro aggiornato completo per il comportamento umano dalla moderna vista a due systems possono consultare il mio libro 'La struttura logica della filosofia, psicologia, mente e il linguaggio in Ludwig Wittgenstein e John Searle' 2nd ed (2019). Coloro che sono interessati a più dei miei scritti possono vedere 'Talking Monkeys--Filosofia, Psicologia, Scienza, Religione e Politica su un Pianeta Condannato--Articoli e Recensioni 2006-2019 3rd ed (2019) e Suicidal Utopian Delusions in the 21st Century 5th ed (2019) . (shrink)

कुछ साल पहले, मैं उस बिंदु पर पहुँच गया जहां मैं आमतौर पर एक किताब के शीर्षक से बता सकते हैं, या कम से कम अध्याय शीर्षक से, दार्शनिक गलतियों के किस प्रकार किया जाएगा और कितनी बार. नाममात्र के वैज्ञानिक कार्यों के मामले में ये काफी हद तक कुछ अध्यायों तक सीमित हो सकते हैं जो दार्शनिक मोमकरते हैं या कार्य के अर्थ या दीर्घकालिक महत्व के बारे में सामान्य निष्कर्ष निकालने का प्रयास करतेहैं। आम तौर पर हालांकि तथ्य (...) के वैज्ञानिक मामलों उदारता से इन तथ्यों का क्या मतलब है के रूप में दार्शनिक अस्पष्ट के साथ interlarded रहे हैं. स्पष्ट भेद जो Wittgenstein वैज्ञानिक मामलों और विभिन्न भाषा के खेल द्वारा उनके विवरण के बीच कुछ 80 साल पहले वर्णित शायद ही कभी ध्यान में रखा जाता है, और इसलिए एक बारी-बारी से विज्ञान द्वारा wowed है और इसके असंबद्ध से निराश विश्लेषण. तो यह इस मात्रा के साथ है. यदि एक हमारे जैसे अधिक या कम एक मन बनाने के लिए है, एक तर्कसंगतता और विचार के दो प्रणालियों की समझ के लिए एक तार्किक संरचना की जरूरत है (दोहरी प्रक्रिया सिद्धांत). यदि एक इस बारे में दर्शन है, एक तथ्य के वैज्ञानिक मुद्दों और कैसे भाषा के मुद्दे पर संदर्भ में काम करता है के दार्शनिक मुद्दे के बीच अंतर को समझने की जरूरत है, और कैसे रिडक्शनिज्म और scientism के नुकसान से बचने के लिए, लेकिन Kurzweil, जैसे सबसे अधिकव्यवहार के छात्रों, काफी हद तक अनजान है. वह मॉडल, सिद्धांतों, और अवधारणाओं से मुग्ध है, और समझाने के लिए आग्रह करता हूं, जबकि Wittgenstein हमें पता चला है कि हम केवल वर्णन करने की जरूरत है, और है कि सिद्धांतों, अवधारणाओं आदि, भाषा का उपयोग करने के सिर्फ तरीके हैं (भाषा का खेल) जो मूल्य केवल insofar के रूप में वे एक स्पष्ट है परीक्षण (स्पष्ट truthmakers, या जॉन Searle (एआई के सबसे प्रसिद्ध आलोचक) के रूप में कहना पसंद करती है, संतोष की स्पष्ट शर्तें (COS)). मैं अपने हाल के लेखन में इस पर एक शुरुआत प्रदान करने का प्रयास किया है. आधुनिक दो systems दृश्यसे मानव व्यवहार के लिए एक व्यापक अप करने के लिए तारीख रूपरेखा इच्छुक लोगों को मेरी पुस्तक 'दर्शन, मनोविज्ञान, मिनडी और लुडविगमें भाषा की तार्किक संरचना से परामर्श कर सकते हैं Wittgenstein और जॉन Searle '2 एड (2019). मेरे लेखन के अधिक में रुचि रखने वालों को देख सकते हैं 'बात कर रहेबंदर- दर्शन, मनोविज्ञान, विज्ञान, धर्म और राजनीति पर एक बर्बाद ग्रह --लेख और समीक्षा 2006-2019 3 एड (2019) और आत्मघाती यूटोपियान भ्रम 21st मेंसदी 4वें एड (2019) . (shrink)

According to a tradition that we hold variously today, the relational person lives most personally in affective and cognitive empathy, whereby we enter subjective communion with another person. Near future social AIs, including social robots, will give us this experience without possessing any subjectivity of their own. They will also be consumer products, designed to be subservient instruments of their users’ satisfaction. This would seem inevitable. Yet we cannot live as personal when caught between instrumentalizing apparent persons (slaveholding) or numbly (...) dismissing the apparent personalities of our instruments (mild sociopathy). This paper analyzes and proposes a step toward ameliorating this dilemma by way of the thought of a 5th century North African philosopher and theologian, Augustine of Hippo, who is among those essential in giving us our understanding of relational persons. Augustine’s semiotics, deeply intertwined with our affective life, suggest that, if we are to own persuasive social robots humanely, we must join our instinctive experience of empathy for them to an empathic acknowledgment of the real unknown relational persons whose emails, text messages, books, and bodily movements will have provided the training data for the behavior of near-future social AIs. So doing, we may see simulation as simulation (albeit persuasive), while expanding our empathy to include those whose refracted behavioral moments are the seedbed of this simulation. If we naïvely stop at the social robot as the ultimate object of our cognitive and affective empathy, we will suborn the sign to ourselves, undermining rather than sustaining a culture that prizes empathy and abhors the instrumentalization of persons. (shrink)

Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg- Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer (...) based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures. (shrink)

During the last century, the new, exciting field of Artificial Intelligence has risen. With its promises, fears for the uncertain future development of this area started to rise. Is it going to be sentient? Is it going to be smarter than us? Is it going to "understand" our uselessness? Is it going to decide that we are no more fundamental? And consequently decide to end our species? These and more questions emerged, nourished by the apprehension of the possible dangers that (...) we would need to face. A number of possible solutions were proposed, to try to limit these yet-to-exist new beings. But none of them got universally accepted, thus leaving this primordial fright unscathed. In this paper a novel point of view is considered where, instead of limiting and preventing the possible worst outcomes, an analysis of the human psyche is performed. This work of introspection grants us the great power of copying: once we understand how our psyche is divided - or "stratified" - and how we can, socially speaking, live together and coexist, we are able to try and replicate this abstract structure inside the "mind" of an hypothetical new artificial being. Using - and possibly improving - this set of abstract mental tools and intrinsic barriers for the creation of a new being should, hypothetically, remove the chance - and the fear - of a future repercussion from its original root. (shrink)

The idea of artificial intelligence implies the existence of a form of intelligence that is “natural,” or at least not artificial. The problem is that intelligence, whether “natural” or “artificial,” is not well defined: it is hard to say what, exactly, is or constitutes intelligence. This difficulty makes it impossible to measure human intelligence against artificial intelligence on a unique scale. It does not, however, prevent us from comparing them; rather, it changes the sense and meaning of such comparisons. Comparing (...) artificial intelligence with human intelligence could allow us to understand both forms better. This paper thus aims to compare and distinguish these two forms of intelligence, focusing on three issues: forms of embodiment, autonomy and judgment. Doing so, I argue, should enable us to have a better view of the promises and limitations of present-day artificial intelligence, along with its benefits and dangers and the place we should make for it in our culture and society. (shrink)

Purpose of the article is to identify the religious factor in the teaching of transhumanism, to determine its role in the ideology of this flow of thought and to identify the possible limits of technology interference in human nature. Theoretical basis. The methodological basis of the article is the idea of transhumanism. Originality. In the foreseeable future, robots will be able to pass the Turing test, become “electronic personalities” and gain political rights, although the question of the possibility of machine (...) consciousness and self-awareness remains open. In the face of robots, people create their assistants, evolutionary competition with which they will almost certainly lose with the initial data. For successful competition with robots, people will have to change, ceasing to be people in the classical sense. Changing the nature of man will require the emergence of a new – posthuman – anthropology. Conclusions. Against the background of scientific discoveries, technical breakthroughs and everyday improvements of the last decades, an anthropological revolution has taken shape, which made it possible to set the task of creating inhumanly intelligent creatures, as well as changing human nature, up to discussing options for artificial immortality. The history of man ends and the history of the posthuman begins. We can no longer turn off this path, however, in our power to preserve our human qualities in the posthuman future. The theme of the soul again reminded of itself, but from a different perspective – as the theme of consciousness and self-awareness. It became again relevant in connection with the development of computer and cloud technologies, artificial intelligence technologies, etc. If a machine ever becomes a "man", then can a man become a "machine"? However, even if such a hypothetical probability would turn into reality, we cannot talk about any form of individual immortality or about the continuation of existence in a different physical form. A digital copy of the soul will still remain a copy, and I see no fundamental possibility of isolating a substrate-independent mind from the human body. Immortality itself is necessary not so much for stopping someone’s fears or encouraging someone’s hopes, but for the final solution of a religious issue. However, the gods hold the keys to heaven hard and are unlikely to admit our modified descendants there. (shrink)