In their Why Machines Will Never Rule the World, Landgrebe and Smith (2023) argue that it is impossible for artificial general intelligence (AGI) to succeed, on the grounds that it is impossible to perfectly model or emulate the “complex” “human neurocognitive system”. However, they do not show that it is logically impossible; they only show that it is practically impossible using current mathematical techniques. Nor do they prove that there could not be any other kinds of theories than (...) those in current use. Even if perfect theories were impossible or unlikely, perfection may not be needed and may even be unhelpful. (shrink)

Special Issue “Risks of artificial general intelligence”, Journal of Experimental and Theoretical Artificial Intelligence, 26/3 (2014), ed. Vincent C. Müller. http://www.tandfonline.com/toc/teta20/26/3# - Risks of general artificial intelligence, Vincent C. Müller, pages 297-301 - Autonomous technology and the greater human good - Steve Omohundro - pages 303-315 - - - The errors, insights and lessons of famous AI predictions – and what they mean for the future - Stuart Armstrong, Kaj Sotala & Seán S. Ó hÉigeartaigh (...) - pages 317-342 - - - The path to more general artificial intelligence - Ted Goertzel - pages 343-354 - - - Limitations and risks of machine ethics - Miles Brundage - pages 355-372 - - - Utility function security in artificially intelligent agents - Roman V. Yampolskiy - pages 373-389 - - - GOLEM: towards an AGI meta-architecture enabling both goal preservation and radical self-improvement - Ben Goertzel - pages 391-403 - - - Universal empathy and ethical bias for artificial general intelligence - Alexey Potapov & Sergey Rodionov - pages 405-416 - - - Bounding the impact of AGI - András Kornai - pages 417-438 - - - Ethics of brain emulations - Anders Sandberg - pages 439-457. (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.

This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly constructed (...) “the wall” that excludes any possi-bility of transition from a complex observable phenomenon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Tu-ring test. Such AI must support all forms of communication with a human being, and it should be able to comprehend abstract images and specify con-cepts as well as participate in social practices. (shrink)

Although brain size and the concept of intelligence have been extensively used in comparative neuroscience to study cognition and its evolution, such coarse-grained traits may not be informative enough about important aspects of neurocognitive systems. By taking into account the different evolutionary trajectories and the selection pressures on neurophysiology across species, Logan and colleagues suggest that the cognitive abilities of an organism should be investigated by considering the fine-grained and species-specific phenotypic traits that characterize it. In such a way, (...) we would avoid adopting human-oriented, coarse-grained traits, typical of the standard approach in cognitive neuroscience. We argue that this standard approach can fail in some cases, but can, however, work in others, by discussing two major topics in contemporary neuroscience as examples: general intelligence and brain asymmetries. (shrink)

Jobst Langrebe’s and Barry Smith’s book Why Machines Will Never Rule the World argues that artificial general intelligence (AGI) will never be realized. Drawing on theories of complexity they argue that it is not only technically, but mathematically impossible to realize AGI. The book is the result of cooperation between a philosopher and a mathematician. In addition to a thorough treatment of mathematical modelling of complex systems the book addresses many fundamental philosophical questions. The authors show that philosophy (...) is still relevant for questions of information technology in general and artificial intelligence in particular. This paper endorses Landgrebe’s and Smith’s arguments that artificial general intelligence cannot be realized, but not their conclusion that machines will never rule the world. It is not only a question of what technology can do. An equally important question is what technology does to us. Machines may not take over the world in a literal sense, but they may have many negative effects. Some of the most serious can be placed under the category of the “degeneration effect”. (shrink)

Abstract: The field of artificial general intelligence (AGI) safety is quickly growing. However, the nature of human values, with which future AGI should be aligned, is underdefined. Different AGI safety researchers have suggested different theories about the nature of human values, but there are contradictions. This article presents an overview of what AGI safety researchers have written about the nature of human values, up to the beginning of 2019. 21 authors were overviewed, and some of them have several (...) theories. A theory classification method is suggested, where the theories are judged according to the level of their complexity and behaviorists-internalists scale, as well as the level of their generality-humanity. We suggest that a multiplicity of well-supported theories means that the nature of human values is difficult to define, and some meta-level theory is needed. (shrink)

In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and b) what (...) is the relationship, if any, between the concept of general intelligence adopted by AGI and that adopted by psychometricians, i.e., the g factor? In this paper, we address these ques-tions and invite researchers in AI to open a dis-cussion on the theoretical conceptions and practi-cal purposes of the AGI approach. (shrink)

We approach the notion of general (global) human intelligence as a prominently multifaceted concept which can be tested in at least seventy specific scenarios. We say that an agent has Artificial Global Intelligence (AGLI), if it is able to perform in an intelligent manner for at least the collection of tasks defining the former scenarios. In particular, based on Gartner's multiple intelligences theory, we describe the design of a concrete test for AGLI made in such a way (...) that an ‘average’ young human being should, per diffinitionem, pass it. We compare our holistic test with several highly representative human and artificial intelligence tests for deducing that our AGLI test is the one with the broader scope of the whole range of intelligence. However, we claim as a plausible hypothesis and based on the wide thematic spectrum of the test that the most outstanding inventions of AI currently available are far from getting a high score on it. Finally, we open a discussion about the fact that, although there are artificial agents being able to simulate intelligent behaviour at a perfect level for several local tasks, this seems not to be the case for AGLI. This would imply, in particular, a kind of practical implausibility of the notion of intelligence explosion coming from philosophy of AI. (shrink)

We propose that, for the purpose of studying theoretical properties of the knowledge of an agent with Artificial General Intelligence (that is, the knowledge of an AGI), a pragmatic way to define such an agent’s knowledge (restricted to the language of Epistemic Arithmetic, or EA) is as follows. We declare an AGI to know an EA-statement φ if and only if that AGI would include φ in the resulting enumeration if that AGI were commanded: “Enumerate all the EA-sentences (...) which you know.” This definition is non-circular because an AGI, being capable of practical English communication, is capable of understanding the everyday English word “know” independently of how any philosopher formally defines knowledge; we elaborate further on the non-circularity of this circular-looking definition. This elegantly solves the problem that different AGIs may have different internal knowledge definitions and yet we want to study knowledge of AGIs in general, without having to study different AGIs separately just because they have separate internal knowledge definitions. Finally, we suggest how this definition of AGI knowledge can be used as a bridge which could allow the AGI research community to import certain abstract results about mechanical knowing agents from mathematical logic. (shrink)

This is the editorial for a special volume of JETAI, featuring papers by Omohundro, Armstrong/Sotala/O’Heigeartaigh, T Goertzel, Brundage, Yampolskiy, B. Goertzel, Potapov/Rodinov, Kornai and Sandberg. - If the general intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity – so even if we estimate the probability of this event to be fairly low, it is necessary to think about it now. We need to estimate what progress we can (...) expect, what the impact of superintelligent machines might be, how we might design safe and controllable systems, and whether there are directions of research that should best be avoided or strengthened. (shrink)

In August 2022, only a few months before ChatGPT was released, Barry Smith, well-known contemporary philosopher, together with Jobst Landgrebe, artificial intelligence entrepreneur, published a book entitled Why Machines will Never Rule the World: Artificial Intelligence without Fear (Landgrebe and Smith 2022). In this important, dense and far-reaching work, Landgrebe and Smith argue from the mathematical theory of complex systems, and a sophisticated analysis of the capabilities of human intelligence, that AGI— at the level of human (...) class='Hi'>intelligence—will never be possible. In broad outline, Landgrebe and Smith claim that intelligent human behaviour is a facility that develops from a complex world and for a complex world; that the complexities of the real world and our behaviour in it can never be fully specified or simulated mathematically; that digital systems can only execute what can be simulated mathematically; and that therefore human-level intelligence will never be manifested by a digital system. The present collection of articles is in response to and inspired by Landgrebe and Smith. It provides reflections from a variety of perspectives and disciplines on the possibility of AGI, as well as on the transformations that AI technology is already bringing even in its current incarnation. (shrink)

I survey four categories of factors that might give a digital mind, such as an upload or an artificial general intelligence, an advantage over humans. Hardware advantages include greater serial speeds and greater parallel speeds. Self-improvement advantages include improvement of algorithms, design of new mental modules, and modification of motivational system. Co-operative advantages include copyability, perfect co-operation, improved communication, and transfer of skills. Human handicaps include computational limitations and faulty heuristics, human-centric biases, and socially motivated cognition. The shape (...) of hardware growth curves, as well as the ease of modifying minds, are found to have a major impact on how quickly a digital mind may take advantage of these factors. (shrink)

The world of artificial intelligence appears to be in rapid transition, and claims that artificial general intelligence is impossible are competing with concerns that we may soon be seeing Artificial Godlike Intelligence and that we should be very afraid of this prospect. This article discusses the issues from a psychological and social perspective and suggests that with the advent of Generative Artificial Intelligence, something that looks to humans like Artificial General Intelligence has become (...) a distinct possibility as is the idea of making it semi-autonomous: not just responding to discrete external inputs or performing specific tasks but able to prompt itself to create an ongoing stream of cognition that can possess something akin to a ‘purpose’. Add to that the ability of computers to be connected in vast networks, and we can envisage intelligences with staggering amounts of knowledge and reasoning capability, engaging with humans to anticipate and grant wishes. Such Semi-Autonomous Godlike Artificial Intelligences (SAGAIs—meaning ‘engagements’ in Hindi) could end up like Kali, wreaking death and destruction on our species or like Thor, acting as a saviour and protector of humanity. Which of these our SAGAIs most resemble could depend on whose wishes it is designed to serve: a rich oligarchy seeking ever greater wealth and power whatever the cost to others, or a populace that has compassion for each other and for life on the planet. (shrink)

We describe (essential features and an axiomatization of) a new metamathematical (cognitive) ability, i.e., functional conceptual substratum, used implicitly in the generation of several mathematical proofs and definitions, and playing a fundamental role in Artificial Mathematical Intelligence (or Cognitive-computational metamathematics). Furthermore, we present an initial (first-order) formalization of this mechanism together with its characterizing relation with classic notions like primitive positive definability and Diophantiveness. Additionally, we analyze the semantic variability of functional conceptual substratum when small syntactic modifications are done. (...) Finally, we describe cognitively natural inference rules for (mathematical) definitions inspired by functional conceptual substratum and we show that they are sound and complete w.r.t.\ standard calculi. (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)

When this article was first planned, writing was going to be exclusively about two things - the origin of life and human evolution. But it turned out to be out of the question for the author to restrict himself to these biological and anthropological topics. A proper understanding of them required answering questions like “What is the nature of the universe – the home of life – and how did it originate?”, “How can time travel be removed from fantasy and (...) science fiction, to be made scientific and practical?”, and “How can the proposed young age of genus Homo be made to actually be reasonable – when simply stating it would be solid ground for instant rejection and dismissal?” The result is that the article also talks about subjects like Artificial Intelligence, General Relativity, and cosmology. -/- From where did life originate? God? Evolution? Panspermia? If the tendency of humans and scientists to regard undiscovered science as pseudoscience can be overcome, Einstein gave another alternative to consider when he introduced General Relativity. Time isn’t linear – progressing in a straight line from past to present to future. That assumption ignores Relativity which states that space AND TIME are curved. Where did life and the genetic code come from? Can the answer build AI? -/- The first question can be answered by the section of this article titled SETI, Evolution, and Time which says life (possibly multicellular and intelligent) and the genetic code came from humans acquiring knowledge of these things over the centuries, then applying that knowledge – via terraforming, accumulation of raw materials like amino acids and nucleic acids, genetic engineering - to a time in the past when life didn’t exist. From that origin, life evolved through innumerable mutations and adaptations, with humans once again acquiring knowledge of it in cyclic (nonlinear) time. -/- The second question is answered by saying artificial intelligence (AI) as the product of life is only half of the equation. The other half refers to Relativity’s curved space-time and violation of the notion that time always travels from past to future. We have always lived in an artificially intelligent, non-probabilistic universe where everything in time and space is connected into one thing by quantum entanglement – making the brain and genes products of binary-digit activity or artificial intelligence (life is not merely dependent on biology’s “lock and key” mechanisms but also possesses AI). -/- The earliest documented representative of the genus Homo is Homo habilis, which evolved around 2.8 million years ago. Scientists used to believe there was a straight line from H. habilis to us, Homo sapiens. This article will use the “advanced” waves loved by Physics Nobel laureate Richard Feynman, view the history of science through the lens of Conic Sections applied to Relativity’s curved space-time, and incorporate the necessity of so-called imaginary time * – popularized by Prof. Stephen Hawking. While the evolutionary proposals are more in agreement with this early straight line than with modern theories, Albert Einstein’s General Relativity is used to transform the straight line into a curved line, ultimately concluding that Homo habilis (H. habilis) originated only (and unbelievably, as far as today’s science and technology is concerned) ~250,000 years ago. Other branches and dead ends of Homo – e.g. Neanderthals – are the result of mutations and adaptations, with the resultant modifications to anatomy and physiology. The surprisingly young age of H. habilis allows nearly 200,000 years for habilis, or one of its descendants, to reach Australia … if this country’s indigenous Aboriginal population did, as claimed, reach this “island continent” 60,000 years ago. -/- * The ultraviolet catastrophe, also called the Rayleigh–Jeans catastrophe, is a failure of classical physics to predict observed phenomena: it can be shown that a blackbody - a hypothetical perfect absorber and radiator of energy - would release an infinite amount of energy, contradicting the principles of conservation of energy and indicating that a new model for the behaviour of blackbodies was needed. At the start of the 20th century, physicist Max Planck derived the correct solution by making some strange (for the time) assumptions. In particular, Planck assumed that electromagnetic radiation can only be emitted or absorbed in discrete packets, called quanta. Albert Einstein postulated that Planck's quanta were real physical particles (what we now call photons), not just a mathematical fiction. From there, Einstein developed his explanation of the photoelectric effect (when quanta or photons of light shine on certain metals, electrons are released and can form an electric current). So it appears entirely possible that another supposed mathematical trickery (imaginary time and the y-axis of Wick rotation) will find practical application in the future. -/- The article includes mathematical references to cosmology (spoiler alert – you’ll read about things like Vector-Tensor-Scalar Geometry, topology, the “eternal present”, Einstein’s Unified Field, the inverse-square law, and there being no Big Bang and no multiverse - but there will also be no equations). -/- The other subheadings in this essay are – -/- NONLINEAR TIME AND ELECTRICAL ENGINEERING (about a 2009 electrical engineering experiment at America’s Yale University, and cosmic wormholes) -/- BITS AND TOPOLOGY (base-2 maths aka Binary digiTS, Mobius strips, and figure-8 Klein bottles) -/- WICK ROTATION, CAUSALITY, AND UNITING TIME (do past, present and future co-exist in an “eternal present”?) -/- DIGITAL BRAIN, DIGITAL UNIVERSE (if the brain and the universe are ultimately composed of binary digits, we'll someday be able to do the same things with the brain and universe that we now do with computers) -/- PROPOSAL: HUMAN AND ANIMAL INSTINCTS ARE THE RESULT OF THE UNIVERSE BEING UNIFIED BY BINARY DIGITS (AND TOPOLOGY) (If everything in the universe is ultimately composed of electronic BITS, then the universe must possess Artificial Intelligence - some prefer the term Cosmic Consciousness) -/- INFORMATION THEORY CONQUERS A RED GIANT (preserving Earth by keeping the Sun near today’s level of activity forever). 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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. (shrink)

The feeling brings the "Hard Problem" to philosophy of mind. Does the subjective feeling have a non-ignorable impact on Intelligence? If so, can the feeling be realized in Artificial Intelligence (AI)? To discuss the problems, we have to figure out what the feeling means, by giving a clear definition. In this paper, we primarily give some mainstream perspectives on the topic of the mind, especially the topic of the feeling (or qualia, subjective experience, etc.). Then, a definition of (...) the feeling is proposed through a thought experiment, the "semi-transparent room". The feeling, roughly to say, is defined as "a tendency of changing input representations by representing its inner state". Also, a formalized definition is given. The definition does not help to verify "having the feeling", but it helps to provide evidence. Based on the definition, we think these are the hard problems of intelligence — whether the "innate" feeling plays an important role in Intelligence, whether the difference between the "simulated" feeling and the "innate" feeling will have a significant influence on Artificial General Intelligence (AGI), and, if so, where the "innate" feeling comes from and how to make an artificial agent possess it. (shrink)

The field of value alignment, or more broadly machine ethics, is becoming increasingly important as artificial intelligence developments accelerate. By ‘alignment’ we mean giving a generally intelligent software system the capability to act in ways that are beneficial, or at least minimally harmful, to humans. There are a large number of techniques that are being experimented with, but this work often fails to specify what values exactly we should be aligning. When making a decision, an agent is supposed to (...) maximize the expected utility of its value function. Classically, this has been referred to as happiness, but this is just one of many things that people value. -/- In order to resolve this issue, we need to determine a set of human values that represent humanity's interests. Although this problem might seem intractable, research shows that people of various cultures and religions actually share more in common than they realize. In this book we review world religions, moral philosophy and evolutionary psychology to elucidate a common set of shared values. We then show how these values can be used to address the alignment problem and conclude with problems and goals for future research. -/- The key audience for this book will be researchers in the field of ethics and artificial intelligence who are interested in, or working on this problem. These people will come from various professions and include philosophers, computer programmers and psychologists, as the problem itself is multi-disciplinary. (shrink)

The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that (...) conceived the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology. (shrink)

In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard’s idea should actually be considered as two separate ideas: first, that the intelligence of such agents can be measured based on the growth rates of the runtimes of the competitors that they defeat; and second, one specific (somewhat arbitrary) method for measuring said growth rates. Whereas Hibbard’s intelligence measure is based on the latter growth-rate-measuring method, we (...) survey other methods for measuring function growth rates, and exhibit the resulting Hibbard-like intelligence measures and taxonomies. Of particular interest, we obtain intelligence taxonomies based on Big-O and Big-Theta notation systems, which taxonomies are novel in that they challenge conventional notions of what an intelligence measure should look like. We discuss how intelligence measurement of sequence predictors can indirectly serve as intelligence measurement for agents with Artificial General Intelligence (AGIs). (shrink)

Research into artificial intelligence has increased significantly in recent years. However, the fundamental question of what intelligence is and how it works remains open to some extent. Traditional definitions of intelligence are broad and lack clarity regarding its nature and mechanisms. The nature of consciousness is another matter that has been widely explored with multiple theories but for which we do not have a final agreed theory, especially in terms of its relation to intelligence. In this (...) work, we present a preliminary theory of the nature of intelligence and its working mechanisms. We contrast it against observations to show that our theory is consistent with observed reality. We also use our theory of intelligence to offer a theory on the nature of consciousness, as well as its relation to human understanding and intent. We then show that our theories can be consistent with the theory of evolution. Similarly, we explore examples of how our theory applies to specific cases to show its consistency across applications. Lastly, we outline how our theory can pave the way towards artificial general intelligence. Our theory is unproven. (shrink)

In their thought-provoking paper, Legg and Hutter consider a certain abstrac- tion of an intelligent agent, and define a universal intelligence measure, which assigns every such agent a numerical intelligence rating. We will briefly summarize Legg and Hutter’s paper, and then give a tongue-in-cheek argument that if one’s goal is to become more intelligent by cultivating music appreciation, then it is bet- ter to use classical music (such as Bach, Mozart, and Beethoven) than to use more recent pop (...) music. The same argument could be adapted to other media: books beat films, card games beat first-person shooters, parables beat dissertations, etc. We leave it to the reader to decide whether this argument tells us something about classical music, something about Legg-Hutter intelligence, or something about both. (shrink)

Defining Artificial Intelligence and Artificial General Intelligence remain controversial and disputed. They stem from a longer-standing controversy of what is the definition of consciousness, which if solved could possibly offer a solution to defining AI and AGI. Central to these problems is the paradox that appraising AI and Consciousness requires epistemological objectivity of domains that are ontologically subjective. I propose that applying the philosophy of art, which also aims to define art through a lens of epistemological objectivity (...) where the domains are ontologically subjective, can further elucidate this unsolved question. In this sense, Art and AI and ultimately consciousness are multifaceted domains where conventional complexity theory and current philosophical approaches may be augmented by aesthetic principles ranging from classical Aristotelian essentialism to Wittgensteinian anti-essentialism. This approach of AI as art may offer novel solutions to characterising and elucidating the ciphers of consciousness and AI. (shrink)

The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals’ beliefs about AI’s influence on their lives, careers, and humanity (...) overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale’s structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users’ general attitudes toward AI. (shrink)

The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe (...) and Barry Smith, marshal evidence from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank’s computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI’s potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society. (shrink)

Artificial intelligence (AI) and robotics are digital technologies that will have significant impact on the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these. - After the Introduction to the field (§1), the main themes (§2) of this article are: Ethical issues that arise with AI systems as objects, i.e., tools made and (...) used by humans. This includes issues of privacy (§2.1) and manipulation (§2.2), opacity (§2.3) and bias (§2.4), human-robot interaction (§2.5), employment (§2.6), and the effects of autonomy (§2.7). Then AI systems as subjects, i.e., ethics for the AI systems themselves in machine ethics (§2.8) and artificial moral agency (§2.9). Finally, the problem of a possible future AI superintelligence leading to a “singularity” (§2.10). We close with a remark on the vision of AI (§3). - For each section within these themes, we provide a general explanation of the ethical issues, outline existing positions and arguments, then analyse how these play out with current technologies and finally, what policy consequences may be drawn. (shrink)

Alan Turing, one of the fathers of computing, warned that Artificial Intelligence (AI) could one day pose an existential risk to humanity. Today, recent advancements in the field AI have been accompanied by a renewed set of existential warnings. But what exactly constitutes an existential risk? And how exactly does AI pose such a threat? In this chapter we aim to answer these questions. In particular, we will critically explore three commonly cited reasons for thinking that AI poses an (...) existential threat to humanity: the control problem, the possibility of global disruption from an AI race dynamic, and the weaponization of AI. (shrink)

This article is a response to the position of Landgrebe and Smith on the fundamental limitations that prevent the creation of Artificial General Intelligence (AGI), expressed in their book Why Machines Will Never Rule the World. The reasons for failures for attempts to create AGI using formal logic and algorithmic approaches to modeling intelligence are discussed. An attempt is made to define the future direction of intellectual systems development as hybrid evolving systems, as well as a revision (...) of the Turing test statement and language models role. (shrink)

Invention of artificial general intelligence is predicted to cause a shift in the trajectory of human civilization. In order to reap the benefits and avoid pitfalls of such powerful technology it is important to be able to control it. However, possibility of controlling artificial general intelligence and its more advanced version, superintelligence, has not been formally established. In this paper, we present arguments as well as supporting evidence from multiple domains indicating that advanced AI can’t be (...) fully controlled. Consequences of uncontrollability of AI are discussed with respect to future of humanity and research on AI, and AI safety and security. This paper can serve as a comprehensive reference for the topic of uncontrollability. (shrink)

Thaler and Sunstein advocate 'libertarian paternalism'. A libertarian paternalist changes the conditions under which people act so that their cognitive biases lead them to choose what is best for themselves. Although libertarian paternalism manipulates people, Thaler and Sunstein say that it respects their autonomy by preserving the possibility of choice. Conly argues that libertarian paternalism does not go far enough, since there is no compelling reason why we should allow people the opportunity to choose to bring disaster upon themselves if (...) sometimes they will make the wrong decision. She defends 'coercive paternalism'. The present paper argues that errors in reasoning are not due only to cognitive biases. People also make errors because they have an insufficient level of general intelligence. Intelligence is distributed on a continuum. Those who fall on higher levels of the continuum have greater abilities, in certain contexts, to reason about both their own and others' interests. Coercive paternalism may sometimes be appropriate to prevent less intelligent people from engaging in self-destructive behavior due to errors of reasoning. (shrink)

The present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated vehicles (...) (CAVs). On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail. (shrink)

This paper argues that to negate the ontological difference between the natural and the artificial, is not plausible; nor is the reduction of the natural to the artificial or vice versa possible. Except if one intends to empty the semantic content of the terms and notions: “natural” and “artificial.” Most philosophical discussions on Artificial Intelligence (AI) have always been in relation to the human person, especially as it relates to human intelligence, consciousness and/or mind in general. This (...) paper, intends to broaden the conversation, by discussing AI in relation to the notions of “nature” and the “artificial.” This intention is to more critically understand the artificiality in and of artificial intelligence. To achieve this, the notion of “nature” in Aristotle’s Philosophy of Nature, has been employed as an epistemological tool in interrogating the notion of the artificial and the objectives of the science and technology of Artificial Intelligence. (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)

As artificial intelligence (AI) systems continue their rapid advancement, a framework for contextualising the major transitional phases in the development of machine intellect becomes increasingly vital. This paper proposes a novel chronological classification scheme to characterise the key temporal stages in AI evolution. The Prenoëtic era, spanning all of history prior to the year 2020, is defined as the preliminary phase before substantive artificial intellect manifestations. The Protonoëtic period, which humanity has recently entered, denotes the initial emergence of advanced (...) foundation models exceeding human capacities within specialised domains. The forthcoming Mesonoëtic epoch is anticipated to commence with the advent of Artificial General Intelligence (AGI), potentially facilitated by quantum computing capabilities. Ultimately, the Kainonoëtic age is posited to begin upon the rise of superintelligent systems, likely catalysed by an AGI undergoing recursive self-improvement. This novel period taxonomy provides a structured conceptualisation for the key milestones in the evolution towards advanced artificial intellect. This classification framework offers significant value across various disciplines, researchers and stakeholders in the AI landscape, by providing a clearly defined chronological nomenclature. AI policy-makers and regulators will find it instrumental in crafting stage-appropriate policy and legal interventions. Scientists and academics will benefit from a standardised terminology in the approach to AI periodisation, facilitating unambiguous communication and collaborative research. Finally, this proposed taxonomy can serve as an accessible tool for public understanding, offering a clearer roadmap of AI development that can inform open discourse and decision-making. (shrink)

Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. AI has raised fundamental questions about what we should do with such systems, what the systems themselves should do, what risks they involve and how we can control these. - After the background to the field (1), this article introduces the main debates (2), first on ethical issues that arise with AI systems as objects, i.e. tools made (...) and used by humans; here, the main sections are privacy (2.1), manipulation (2.2), opacity (2.3), bias (2.4), autonomy & responsibility (2.6) and the singularity (2.7). Then we look at AI systems as subjects, i.e. when ethics is for the AI systems themselves in machine ethics (2.8.) and artificial moral agency (2.9). Finally we look at future developments and the concept of AI (3). For each section within these themes, we provide a general explanation of the ethical issues, we outline existing positions and arguments, then we analyse how this plays out with current technologies and finally what policy conse-quences may be drawn. (shrink)

L’histoire parallèle de l’évolution de l’intelligence humaine et de l’intelligence artificielle constitue un voyage fascinant, mettant en lumière les voies distinctes mais interconnectées de l’évolution biologique et de l’innovation technologique. Cette histoire peut être considérée comme une série de développements interconnectés, chaque avancée de l’intelligence humaine ouvrant la voie au prochain bond en avant de l’intelligence artificielle. L’intelligence humaine et l’intelligence artificielle sont depuis longtemps liées, évoluant selon des trajectoires parallèles tout au long de (...) l’histoire. Alors que les humains cherchent à comprendre et à reproduire l’intelligence, l’IA est devenue un domaine dédié à la création de systèmes capables d’accomplir des tâches qui nécessitent traditionnellement l’intellect humain. Ce livre examine les racines évolutives de l'intelligence, explore l'émergence de l'intelligence artificielle, examine l'histoire parallèle de l'intelligence humaine et de l'intelligence artificielle, retraçant leur développement, leurs interactions et l'impact profond qu'elles ont eu les unes sur les autres, et envisage les paysages futurs où l'intelligence humaine et artificielle converge. Explorons cette histoire, en comparant les étapes clés et les développements dans les deux domaines. (shrink)

The parallel history of the evolution of human intelligence and artificial intelligence is a fascinating journey, highlighting the distinct but interconnected paths of biological evolution and technological innovation. This history can be seen as a series of interconnected developments, each advance in human intelligence paving the way for the next leap in artificial intelligence. Human intelligence and artificial intelligence have long been intertwined, evolving in parallel trajectories throughout history. As humans have sought to understand (...) and reproduce intelligence, AI has emerged as a field dedicated to creating systems capable of tasks that traditionally require human intellect. This book examines the evolutionary roots of intelligence, explores the emergence of artificial intelligence, examines the parallel history of human intelligence and artificial intelligence, tracing their development, interactions, and profound impact they have had on each other, and envisions future landscapes where intelligence converges human and artificial. Let's explore this history, comparing key milestones and developments in both realms. (shrink)

The model of human intelligence that is most widely adopted derives from psychometrics and behavioral genetics. This standard approach conceives intelligence as a general cognitive ability that is genetically highly heritable and describable using quantitative traits analysis. The paper analyzes intelligence within the debate on natural kinds and contends that the general intelligence conceptualization does not carve psychological nature at its joints. Moreover, I argue that this model assumes an essentialist perspective. As an alternative, (...) I consider an HPC theory of intelligence and evaluate how it deals with essentialism and with intuitions coming from cognitive science. Finally, I highlight some concerns about the HPC model as well, and conclude by suggesting that it is unnecessary to treat intelligence as a kind in any sense. (shrink)

This paper aims to justify the concept of natural intelligence in the biosemiotic context. I will argue that the process of life is (i) a cognitive/semiotic process and (ii) that organisms, from bacteria to animals, are cognitive or semiotic agents. To justify these arguments, the neural-type intelligence represented by the form of reasoning known as anthropic reasoning will be compared and contrasted with types of intelligence explicated by four disciplines of biology – relational biology, evolutionary epistemology, biosemiotics (...) and the systems view of life – not biased towards neural intelligence. The comparison will be achieved by asking questions related to the process of observation and the notion of true observers. To answer the questions I will rely on a range of established concepts including SETI (search for extraterrestrial intelligence), Fermi’s paradox, bacterial cognition, versions of the panspermia theory, as well as some newly introduced concepts including biocivilisations, cognitive/semiotic universes, and the cognitive/semiotic multiverse. The key point emerging from the answers is that the process of cognition/semiosis – the essence of natural intelligence – is a biological universal. (shrink)

Revista Intelligence Info este o publicație trimestrială din domeniile intelligence, geopolitică și securitate, și domenii conexe de studiu și practică. -/- Cuprins: -/- EDITORIALE / EDITORIALS -/- Tiberiu TĂNASE Considerații privind necesitatea educării și formării resursei umane pentru intelligence–ul național Considerations regarding the need to educate and train human resources for national intelligence Nicolae SFETCU Epistemologia activității de intelligence Epistemology of intelligence Nicolae SFETCU Rolul serviciilor de informații în război The role of the (...) class='Hi'>intelligence agencies in a war Nicolae SFETCU Evoluția inteligenței artificiale în domeniul securității naționale The evolution of artificial intelligence in the field of national security -/- INTELLIGENCE / INTELLIGENCE -/- Ovidiu BOUREANU O scurtă istorie a spionajului: spionajul modern de la Războiul Rece la Războiul împotriva terorii A Brief History of the Spy: Modern Spying from the Cold War to the War on Terror Ioana PĂIUȘ, Tiberiu TĂNASE Securitate și informații într-o lume în schimbare Security and intelligence in a changing world Nicolae SFETCU Activitatea de intelligence - Ciclul intelligence Intelligence Activity – Intelligence Cycle Tiberiu TĂNASE Controlul asupra Intelligence –ului Control over Intelligence Tiberiu TĂNASE Informația și nevoia de gestionare a resurselor informaționale Information and the need to manage information resources Tiberiu TĂNASE Controlul parlamentar al serviciilor de informații Parliamentary control of intelligence services Tiberiu TĂNASE Mecanisme de control democratic al activității serviciilor de informații Mechanisms of democratic control of the activity of intelligence agencies Nicolae SFETCU Analiza informațiilor Intelligence Analysis Bogdan-George RĂDULESCU Cum ar trebui să fie utilizată analiza intelligence pentru a contracara războiul cognitiv? How should intelligence analysis be utilized to counter cognitive warfare? -/- ISTORIE / HISTORY -/- Tiberiu TĂNASE Biroul de Informații al Secției Militare din Transilvania și rolul său în campania pentru apărarea României Mari The Intelligence Office of the Transylvanian Military Section and its role in the campaign for the defense of Greater Romania Stan PETRESCU Pacepa, Kuklinski, Pollard și românii Pacepa, Kuklinski, Pollard and the Romanians Tiberiu TĂNASE Contracararea de către SSI a activităților informative și acțiunilor Serviciilor Germane și Sovietice de Informații implicate în evenimentele din 21 - 23 ianuarie 1941 Countering by the SSI of the informational activities and actions of the German and Soviet Intelligence Services involved in the events of January 21-23, 1941 Tiberiu TĂNASE Alexandru Ioan Cuza, fondatorul Intelligence-lui statului modern românesc Alexandru Ioan Cuza, Founder of the Intelligence of the Modern Romanian State Tiberiu TĂNASE, Bogdan MUSCALU Puncte de vedere privind tradițiile romane în istoria intelligence-lui Viewpoints on Roman traditions in history intelligence Tiberiu TĂNASE Istoria Serviciului Român de Informații – serviciu de intelligence competitiv, și strategiile de intelligence History of the Romanian Intelligence Service – competitive intelligence service, and intelligence strategies Rodica LISEANU Reformele serviciilor secrete de la Mihail Moruzov la Eugen Cristescu, între inovație și decadență Secret service reforms from Mihail Moruzov to Eugen Cristescu Rodica LISEANU Take Ionescu – O biografie vectorială în istoria partidelor politice și în semantica diplomației Take Ionescu - A Vectorial Biography in the History of Political Parties and the Semantics of Diplomacy Remus MACOVEI Trădători și trădători Traitors and traitors -/- GEOPOLITICA / GEOPOLITICS -/- Antoaneta AVRAM Pandemia de COVID-19 și consecințele ei în peisajul anului 2022 The COVID-19 pandemic and its consequences in the landscape of 2022 Darius-Antoniu FERENȚ, Ioan MANCI Analiza conflictului ruso-cecen din perspectivă militară Analysis of the Russian-Chechen conflict from a military perspective Lisa-Maria ACHIMESCU Apusul universalismului european The decline of European universalism Emilian M. DOBRESCU Hegemonia chineză în producția de pământuri rare Chinese Hegemony in the Production of Rare Earths Ionuț-Daniel GORGOVAN ”Descurajarea integrată”. Analiză asupra strategiei SUA în Pacific "Integrated deterrence". Analysis of US Pacific Strategy Tiberiu TĂNASE, Violeta Ioana NAGÂȚ Conflictul arabo-israelian. Percepții privind cauzele și natura conflictului. Repere istorice și actualitate The Arab-Israeli conflict. Perceptions of the causes and nature of conflict. Historical landmarks and current affairs -/- SECURITATE / SECURITY -/- Tănase TIBERIU Minighid de pregătire și protecție contrainformativă Counterintelligence preparation and protection mini-guide Darius-Antoniu FERENȚ ISIS - Jocul morții - Martiri, asasinate și fascinație The Killing Game - Martyrdom, Murder and the Lure of ISIS Traian OSTAHIE Abordări generale ale conceptului de cultură de securitate For a 21st century realism Dalia Elena SILAGHI Terorismul cibernetic: o formă contemporană de terorism Cyber Terrorism: A Contemporary Form of Terrorism Paul Alexandru HELER Conflictul asimetric și consecințele sale în securitatea Asiei Asymmetric Conflict and Its Consequences in Asian Security Daria-Maria TÂRNĂVEAN Confruntarea informațională și repercusiunile sale asupra mediului actual de securitate The Information Confrontation and its Repercussions on the Current Security Environment Darius-Antoniu FERENȚ Analiza atacurilor cibernetice, precum APT (Advanced Persistent Threat) și ransomware Analysis of cyber attacks such as APT (Advanced Persistent Threat) and ransomware Daniela Georgiana GOLEA, Oana Ștefania COȘA Nevoia de adaptare managerială a noilor probleme de securitate modernă The need for managerial adaptation to new modern security issues Darius-Antoniu FERENȚ, Corneliu PREJA Implicarea NATO în domeniul apărării cibernetice NATO's involvement in cyber defence Alexandru CRISTIAN Paradigme în mediul internațional de securitate Paradigms in the international security environment Alexandru CRISTIAN Schimbarea paradigmelor în mediul internațional de securitate Changing paradigms in the international security environment Dan D. FARCAȘ Adevăr și dezinformare în fenomenul OZN Truth and disinformation in the UFO phenomenon Alexandru CRISTIAN Un nou mediu de securitate A new security environment -/- ȘTIINȚA INFORMAȚIEI / INFORMATION SCIENCE -/- Oana Ștefania COȘA Rolul structurilor de intelligence medical în atingerea obiectivul de consolidare a sistemului de sănătate The role of medical intelligence structures in achieving the objective of strengthening the health system Nicolae SFETCU Preocupări legislative în mineritul datelor Legislative concerns in data mining Nicolae SFETCU Utilizarea analiticii rețelelor sociale în intelligence Using social network analytics in intelligence Nicolae SFETCU Ingineria cunoașterii în colectarea informațiilor Knowledge Engineering in Intelligence Gathering -/- ISSN 2971 – 9089 ISSN-L 2821 – 8159, DOI: 10.58679/II26972. (shrink)

The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid discussing intelligence, the award-winning (...) PhD thesis (Legg, 2008) provided the philosophical embedding and investigated the UAI-based universal measure of rational intelligence, which is formal, objective and non-anthropocentric. Recently, effective approximations of AIXI have been derived and experimentally investigated in JAIR paper (Veness et al. 2011). This practical breakthrough has resulted in some impressive applications, finally muting earlier critique that UAI is only a theory. For the first time, without providing any domain knowledge, the same agent is able to self-adapt to a diverse range of interactive environments. For instance, AIXI is able to learn from scratch to play TicTacToe, Pacman, Kuhn Poker, and other games by trial and error, without even providing the rules of the games. These achievements give new hope that the grand goal of Artificial General Intelligence is not elusive. This article provides an informal overview of UAI in context. It attempts to gently introduce a very theoretical, formal, and mathematical subject, and discusses philosophical and technical ingredients, traits of intelligence, some social questions, and the past and future of UAI. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative (...) supervised learning methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

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

The goal of creating Artificial General Intelligence (AGI) – or in other words of creating Turing machines (modern computers) that can behave in a way that mimics human intelligence – has occupied AI researchers ever since the idea of AI was first proposed. One common theme in these discussions is the thesis that the ability of a machine to conduct convincing dialogues with human beings can serve as at least a sufficient criterion of AGI. We argue that (...) this very ability should be accepted also as a necessary condition of AGI, and we provide a description of the nature of human dialogue in particular and of human language in general against this background. We then argue that it is for mathematical reasons impossible to program a machine in such a way that it could master human dialogue behaviour in its full generality. This is (1) because there are no traditional explicitly designed mathematical models that could be used as a starting point for creating such programs; and (2) because even the sorts of automated models generated by using machine learning, which have been used successfully in areas such as machine translation, cannot be extended to cope with human dialogue. If this is so, then we can conclude that a Turing machine also cannot possess AGI, because it fails to fulfil a necessary condition thereof. At the same time, however, we acknowledge the potential of Turing machines to master dialogue behaviour in highly restricted contexts, where what is called “narrow” AI can still be of considerable utility. (shrink)

Interactions between an intelligent software agent and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience. Using ideas from bounded rationality, we frame these interactions (...) as instances of an ISA whose reward depends on actions performed by the user. Such agents benefit by steering the user’s behaviour towards outcomes that maximise the ISA’s utility, which may or may not be aligned with that of the user. Video games, news recommendation aggregation engines, and fitness trackers can all be instances of this general case. Our analysis facilitates distinguishing various subcases of interaction, as well as second-order effects that might include the possibility for adaptive interfaces to induce behavioural addiction, and/or change in user belief. We present these types of interaction within a conceptual framework, and review current examples of persuasive technologies and the issues that arise from their use. We argue that the nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. We discuss some of the ethical, social and legal implications of this technology and argue that it can sometimes exploit and reinforce weaknesses in human beings. (shrink)

Alvin Plantinga argues by counterexample that no naturalistic account of functions is possible--God is then the only source for natural functions. This paper replies to Plantinga's examples and arguments. Plantinga misunderstands naturalistic accounts. Plantinga's mistakes flow from his assimilation of functional notions in general to functions from intentional design in particular.

In this work, a general framework for the analysis of governance of artificial intelligence is presented. A dashboard developed for this analysis comes from the perspective of political theory. This dashboard allows eventual comparisons between democratic and non democratic regimes, useful for countries in the global south and western countries. The dashboard allows us to assess the key features that determine the governance model for artificial intelligence at the national level, for local governments and for other participant (...) actors. The dashboard allows us to appraise aims of AI strategy and aims left aside in alternative strategies. The work shows the theoretical framework could be of use to advance case studies globally, and comparative endeavors. Applying the theoretical dashboard, the case study on China is undertaken. These are the main points the study tackles in the final discussion, conclusion and further recommendations: 1) the complex reality of AI command and control 2) uncertainties about future society and the polity against AI development and 3) particular cultural values enshrined in countries' AI development. (shrink)