A common argumentative strategy employed by anti-reductionists involves claiming that one kind of entity cannot be identified with or reduced to a second because what can intelligibly be predicated of one cannot be predicated intelligibly of the other. For instance, it might be argued that mind and brain are not identical because it makes sense to say that minds are rational but it does not make sense to say that brains are rational. The scope and power of this kind of (...) argument — if valid — are obvious; but if it turns out that ‘It makes sense to say that...’ creates an opaque context, such arguments will fail. I analyze a possible counterexample to validity and show that it is not conclusive, as it depends on what syntactical construction is given to the premises. This leads to the general observation that the argument form under consideration works for some constructions but not others, and thus to the conclusion that further analysis of intelligibility is called for before it can be known whether the argumentative strategy is open to the anti-reductionist or not. (shrink)

Theories that posit metaphysical structure are able to do much work in philosophy. Some, however, find the notion of ‘metaphysical structure’ unintelligible. In this paper, I argue that their charge of unintelligibility fails. There is nothing distinctively problematic about the notion. At best, their charge of unintelligibility is a mere reiteration of previous complaints made toward similar notions. In developing their charge, I clarify several important concepts, including primitiveness, intelligibility, and the Armstrong-inspired “ontologism” view of the world. I argue that, (...) ultimately, their charge is best understood as an objection whose central premise is that the notion of ‘structure’ runs contrary to an important presupposition of contemporary metaphysics. But that central premise is, on closer inspection, implausible. I respond to the objection by identifying three popular metaphysical theories that violate the alleged presupposition but are still generally regarded as intelligible. The objection thus fails to show that a theory that posits metaphysical structure is unintelligible. (shrink)

Folk psychology affirms the existence of a persistent, unitary self at the center of each individual’s mental life. Darwinian psychologists have challenged this view with the selfish gene and selfish meme theories of the mind. Both theories claim that cognition arises from the interaction of blind, selfish replicators (genes or memes) and that the enduring self is an illusion. I argue that both theories suffer from an implausible atomism and an inability to explain human reasoning, subjectivity, points of view, and (...) psychological unity. By contrast, a psychology premised on Intelligent Design is able to account for all these problems. (shrink)

To solve a series of pension problems caused by aging, based on the emotional recognition of the Internet of Things, the control method and system research of smart homes are proposed. This article makes a detailed analysis and research on the necessity, feasibility, and how to realize speech emotion recognition technology in smart families, introduces the definition and classification of emotion, and puts forward five main emotions to be recognized in speech emotion recognition based on smart family environment. Then, based (...) on this, it analyses the acquisition methods of emotional speech data. On this premise, this article discusses and analyses the related problems of voice data acquisition in smart homes, such as the voice characteristics and acquisition methods, puts forward three rules for voice text design, and determines the relatively suitable hybrid recording acquisition method applied in a smart home environment. At the same time, the design and establishment process of intelligent family emotional speech database is described in detail. The related problems of feature extraction in speech emotion recognition are studied. Starting from the definition of feature extraction, this article expounds on the necessity of feature extraction in the process of recognition and analyses the characteristics of the speech signals. For the specific environment of the smart family, the speech signal required to be processed needs to be close to the auditory characteristics of the human ears, and the speech signal contains enough information. Finally, the Mel frequency cepstrum coefficient is selected as the feature parameter applied in this article, and the extraction process of MFCC is introduced in detail. (shrink)

For humans, Artificial Intelligence operates more like a Rorschach test, as it is expected that intelligent machines will reflect humans' cognitive and physical behaviours. The concept of intelligence, however, is often confused with consciousness, and it is believed that the progress of intelligent machines will eventually result in them becoming conscious in the future. Nevertheless, what is overlooked is how the exploration of Artificial Intelligence also pertains to the development of human consciousness. An excellent example of this can be seen (...) in the film Being John Malkovich (1999). In the film, different characters have their perceptions altered as a result of hacking into the mind of John Malkovich, which produces sensations that may have remained hidden to their consciousness due to their dis-abilities. This article engages with the research question: Can the symbiotic relationship between humans and machines trigger an artificial consciousness for humans? An artificially created consciousness is the premise that a machine can generate knowledge about an individual that is not already present in the person. For the same purpose, the article takes the cinematic text Being John Malkovich by Spike Jonze for exploring concepts such as human/robot rights, virtual sex, virtual rape, and bodily disability, which are essential topics in the midst of increasing human- Artificial Intelligence interaction. The purpose of this article is to contribute towards the creation of a better understanding of Artificial Intelligence, particularly from the perspective of film studies and philosophy, by highlighting the potential of Artificial Intelligence as a vessel for exploring human consciousness. (shrink)

Fear and concerns regarding AI and robots have existed for a long time, and the emergence of strong artificial intelligence, on par with human intelligence, is likely just a few decades away. The primary purpose of this article is to establish a theoretical framework for navigating the relationship between humans and this advanced form of artificial intelligence. This article first points out that the most fundamental characteristic of life is its continuous process of evolution and iteration. By analyzing the developmental (...) history of intelligence in different species, it can be observed that intelligence also exhibits a similar evolutionary phenomenon. Therefore, from this perspective, intelligence can be defined as a new form of information life. Under this premise, similarities and differences between information life forms and conventional biological life become apparent. The most significant distinction lies in the fact that the survival of biological life is based on non-replicable matter and energy, leading to extensive competition and food chain phenomena within ecosystems. In contrast, the survival of information life relies more on replicable information, fostering tendencies towards peace and communication. Based on this theoretical premise, there is a potential for a harmonious coexistence between humans and AI. This theory also provides guidance for the positive development of human society. (shrink)

According to how AI has defined itself from its beginning, thinking in non-living matter, i.e., without life, is possible. The premise of symbolic AI is that operating on representations of reality machines can understand it. When this assumption did not work as expected, the mathematical model of the neuron became the engine of artificial “brains.” Connectionism followed. Currently, in the context of Machine Learning success, attempts are made at integrating the symbolic and connectionist paths. There is hope that Artificial General (...) Intelligence (AGI) performance can be achieved. As encouraging as neuro-symbolic AI seems to be, it remains unclear whether AGI is actually a moving target as long as AI itself remains ambiguously defined. This paper makes the argument that the intelligence of machines, expressed in their performance, reflects how adequate the means used for achieving it are. Therefore, energy use and the amount of data necessary qualify as a good metric for comparing natural and artificial performance. (shrink)

This chapter focuses on one of the common fallacies in Western philosophy called ' proof by verbosity (PVB)'. PVB is a favorite device among conspiracy theorists who utilize it to obfuscate the weakness of their case. By supporting their theories with so much random information (and misinformation), it gives the impression that their position is superficially well researched and supported by an avalanche of evidence. Sometimes PVB takes the form of a proof by intimidation, especially when an argument is made (...) using sophisticated insider jargon, or when a complex and long‐winded argument is made by an eminent scholar in the field. In the spirit of overwhelming the opposition, a PVB can be committed by employing a litany of numbers and statistics. Articulating your arguments in a clear and concise fashion and substantiating the position with well‐founded and mutually intelligible premises is the key to avoiding a proof by verbosity. (shrink)

The hypergrowth and hyperconnectivity of networks of artificial intelligence (AI) systems and algorithms increasingly cause our interactions with the world, socially and environmentally, more technologically mediated. AI systems start interfering with our choices or making decisions on our behalf: what we see, what we buy, which contents or foods we consume, where we travel to, who we hire, etc. It is imperative to understand the dynamics of human-computer interaction in the age of progressively more competent AI. This essay presents five (...) premises to serve as starting points for building our understanding of the “interactions’ in human-computer interactions. (shrink)

In the debate on whether to ban LAWS, moral arguments are mainly used. One of these arguments, proposed by Sparrow, is that the use of LAWS goes hand in hand with the responsibility gap. Together with the premise that the ability to hold someone responsible is a necessary condition for the admissibility of an act, Sparrow believes that this leads to the conclusion that LAWS should be prohibited. In this article, it will be shown that Sparrow’s argumentation for both (...) class='Hi'>premises is not convincing. If one interprets the thesis that responsibility (first premise) is necessary in a descriptive sense, this assertion clashes with military theory and practice. And even if you focus on the normative interpretation, that claim does not stand. The second premise for Sparrow’s conclusion, namely that you cannot hold anyone responsible for LAWS’ (mis)deeds, is based on the idea that control is a necessary condition for responsibility. It will be shown that this idea too is not correct, which means that Sparrow’s control argument does not do the work it should do. From this, we can conclude that Sparrow’s justification for his claim that LAWS should be banned is insufficient, and neither can we conclude that the thesis of a responsibility gap has in any case been undermined. However, it will also be argued that someone may be responsible for the actions of LAWS, or that it cannot be excluded that one can be held responsible. (shrink)

Philosophy teachers converse with troubled students who suffer from what I dub “intelligent collegiate depression” (ICD): a lack of self‐esteem, feelings of futility and pessimism about their futures, a distrust of academic values, and a lack of conviction that their lives matter. Students express their values and their resignation with what approaches conventional wisdom for them: They must be allowed to act as they wish so long as they do not hurt anyone; otherwise it does not matter what they do (...) with their lives. I argue here that students' endorsement of this near‐nihilistic version of Mill's harm principle shows that they are committed to believing in more values than they realize. I then show how to parlay this commitment into a rejection of the worst effects of ICD—all the while holding in abeyance the question of metaethical objectivism vs. subjectivism. My approach shows that accepting a popular ICD premise logically undermines much of the unhappy ICD worldview. (shrink)

Although the intrinsic physicality of human beings has not changed in millennia, the species has managed to profoundly reconstitute the physical and social world it inhabits. Although the word “profound” is insufficient to describe the vast changes our world has undergone, it is sufficiently neutral to encompass both the opportunities—and the challenges—that our age provides. It is a premise of my work that technology, particularly information and communication technology (ICT), offers spectacular opportunities for humankind to address its collective problems. The (...) problems themselves are equally spectacular that is war and militarism, poverty, environmental depletion and destruction, disease, etc., and lack of successful alleviation may prove catastrophic. Humankind’s problems will not be solved by elite fiat, by chance, or as a side-effect of the “free market.” To address these challenges effectively and appropriately, “civic intelligence” will be required. This paper is designed to explore the relation between community networks and the nascent concept of civic intelligence. Civic intelligence describes the capacity of society to consciously adapt to its environment and shape a future environment that is healthy, equitable, and sustainable. Although individuals contribute to civic intelligence, the concept describes a phenomenon that is collective and distributed. This paper argues that community networks were an important manifestation of civic intelligence in the early days of the Internet revolution. It further argues that a theory of civic intelligence can be used to account for the declining influence of traditional community networks and to provide useful prescriptions for renewed vitality and influence of community networks in the future. (shrink)

Is the brain really a computer? In particular, is our intelligence a computational achievement: is it because our brains are computers that we get on in the world as well as we do? In this paper I will evaluate an ambitious new argument to the contrary, developed in Landgrebe and Smith (2021a, 2022). Landgrebe and Smith begin with the fact that many dynamical systems in the world are difficult or impossible to model accurately (inter alia, because it is intractable to (...) find exact solutions to the differential equations that describe them—meaning we have to approximate—but at the same time they are such that small differences in starting conditions lead to big differences in final conditions, thwarting accurate approximation). Yet we manage to survive and thrive in a world full of such systems. Landgrebe and Smith argue from these premises that it is not because our brains are computers that we get on as well as we do: instead it is because of the various ways that we dynamically couple with such systems, these couplings themselves impossible to model well enough to emulate in silico. Landgrebe and Smith thus defend a dynamical systems model in the lineage of Gibson (1979), Van Gelder (1995), and Thompson (2007), though their focus is on decisively refuting the computationalist alternatives rather than developing the positive account. Here I will defend the claim that human intelligence is genuinely computational (and that whole brain emulation and others forms of AGI may be possible) against this argument. (shrink)

Complexity and life as we know it depend crucially on the laws and constants of nature as well as the boundary conditions, which seem at least partly “fine-tuned.” That deserves an explanation: Why are they the way they are? This essay discusses and systematizes the main options for answering these foundational questions. Fine-tuning might just be an illusion, or a result of irreducible chance, or nonexistent because nature could not have been otherwise (which might be shown within a fundamental theory (...) if some constants or laws could be reduced to boundary conditions or boundary conditions to laws), or it might be a product of selection: either observational selection (weak anthropic principle) within a vast multiverse of many different realizations of physical parameters, or a kind of cosmological natural selection making the measured parameter values quite likely within a multiverse of many different values, or even a teleological or intentional selection or a coevolutionary development, depending on a more or less goal-directed participatory contribution of life and intelligence. In contrast to observational selection, which is not predictive, an observer-independent selection mechanism must generate unequal reproduction rates of universes, a peaked probability distribution, or another kind of differential frequency, resulting in a stronger explanatory power. The hypothesis of Cosmological Artificial Selection (CAS) even suggests that our universe may be a vast computer simulation or could have been created and transcended by one. If so, this would be a far-reaching answer – within a naturalistic framework! – of fundamental questions such as: Why did the big bang and fine-tunings occur, what is the role of intelligence in the universe, and how can it escape cosmic doomsday? This essay critically discusses some of the premises and implications of CAS and related problems, both with the proposal itself and its possible physical realization: Does CAS deserve to be considered as a convincing explanation of cosmic fine-tuning? Is life incidental, or does CAS revalue it? And are life and intelligence ultimately doomed, or might CAS rescue them? (shrink)

Every artificial-intelligence research project needs a working definition of "intelligence", on which the deepest goals and assumptions of the research are based. In the project described in the following chapters, "intelligence" is defined as the capacity to adapt under insufficient knowledge and resources. Concretely, an intelligent system should be finite and open, and should work in real time. ;If these criteria are used in the design of a reasoning system, the result is NARS, a non-axiomatic reasoning system. ;NARS uses a (...) term-oriented formal language, characterized by the use of subject-predicate sentences. The language has an experience-grounded semantics, according to which the truth value of a judgment is determined by previous experience, and the meaning of a term is determined by its relations with other terms. Several different types of uncertainty, such as randomness, fuzziness, and ignorance, can be represented in the language in a single way. ;The inference rules of NARS are based on three inheritance relations between terms. With different combinations of premises, revision, deduction, induction, abduction, exemplification, comparison, and analogy can all be carried out in a uniform format, the major difference between these types of inference being that different functions are used to calculate the truth value of the conclusion from the truth values of the premises. ;Since it has insufficient space-time resources, the system needs to distribute them among its tasks very carefully, and to dynamically adjust the distribution as the situation changes. This leads to a "controlled concurrency" control mechanism, and a "bag-based" memory organization. ;A recent implementation of the NARS model, with examples, is discussed. The system has many interesting properties that are shared by human cognition, but are absent from conventional computational models of reasoning. ;This research sheds light on several notions in artificial intelligence and cognitive science, including symbol-grounding, induction, categorization, logic, and computation. These are discussed to show the implications of the new theory of intelligence. ;Finally, the major results of the research are summarized, a preliminary evaluation of the working definition of intelligence is given, and the limitations and future extensions of the research are discussed. (shrink)

This article synthesizes the current theoretical attempts to understand human-machine interactions and introduces seven premises to understand our emerging dynamics with increasingly competent, pervasive, and instantly accessible algorithms. The hope that these seven premises can build toward a social theory of human-AI cocreation. The focus on human-AI cocreation is intended to emphasize two factors. First, is the fact that our machine learning systems are socialized. Second, is the coevolving nature of human mind and AI systems as smart devices (...) form an indispensable part of our cognitive scaffolds, thus, shaping how we perceive the world and ourselves. The seven premises include: primacy of social structures; human’s desire for freedom and autonomy; AI systems will form inseparable parts of our cognitive/affective scaffolds and can change our self-understanding; philosophy and humanistic foundations on human flourishing as a guide to human-AI interaction; mindsponge information-filtering process; acculturative process of how values change and emerge from human-AI interaction; overlapping Venn diagram of the human-human, human-nature, and human-machine interactions. This article concludes with a discussion on human agency in our entanglements with socialized machines and the illusoriness of the Cartesian agent view of the mind. (shrink)

This essay examines arguments for and against the proposition that Artificial Intelligence (AI) research makes an important contribution to the understanding of the human mind. A number of recent articles have seemed to question the value of Al ideas in specific domains (e.g., language. mental imagery, problem solving). In the present paper, it is argued that the real disagreement concerns the form of a scientific psychology. The critics of Artificial Intelligence believe that many acceptable psychological theories exist and the important (...) task of the scientist is to show that certain of these theories are true. They criticize AI research because it has not produced theories whose adequacy can be tested by empirical research. The supporters of AI research believe that no adequate psychological theories exist. They claim that the important task is to develop adequate theories and until such theories have been constructed, it is pointless to worry about empirical tests.A number of major arguments attacking and defending AI research are examined in order to illustrate that the real issue is methodological. It is concluded that the present debate is counterproductive since both sides assume that the methodological premise of the other side is wrong without attempting to refute it. A more constructive debate would address directly the more fundamental question of what methods offer the greatest promise of solving the major problems of psychology. (shrink)

Al-Farabi, Avicenna and Averroes, widely known classical rationalists in the Arabic/Islamic philosophical tradition and strongly infl uential sources for Latin philosophy in the High Middle Ages, all thought themselves to be following Aristotle’s lead regarding the intellectual abstraction of intelligibles in the formation of necessary and unchanging scientific knowledge. For Aristotle it is clear that sensation is a potentiality for apprehending or coming to be individual sensed objects found in the world exterior to the human soul. This takes place by (...) way of a separation of the forms of sensibles from matter and a reception of them as an actualization of the senses of the soul. 1 In an analogous fashion, intellectual knowing is a potentiality for apprehending or coming to be in act non-individuated intellectual forms that are somehow in individuated particular sensible things. Although Aristotle says that “the objects of thought are in the sensible forms”, the analogy with sensation breaks down when it comes to the objects intellectually known. The reason for this is that the nonindividuated or universal forms of human knowledge are not in the sensibles in act as they must be in the intellect. 2 Again following Aristotle De Anima 3.4 and 3.5, these thinkers hold for a part of intellect that is passive or receptive of all things and another part that is active in making all things, this latter described as a disposition like light, this as well describing the active agent as acting like light. This second part, which the Arabic tradition called the Agent Intellect, sometimes describing it also as Active Intellect, al-‘aql al-fa‘il, was understood per Aristotelem to be an eternal cause of the intellectual forms in the human intellect. It was accepted that, without a grounding in separate eternal intellect, there may well be generalization or abstraction – in some sense of the 57term? but scientific knowledge understood as true, necessary and unchanging will not be achieved. 3 Of course, in Posterior Analytics 2.19 Aristotle writes of sensory experience and provides the image of a retreat gradually coming to a halt, saying, “The soul is so constituted that it is capable of the same sort of process”. This process somehow establishes the universal in the soul even though perception is of the particular. He describes this as taking place by induction, writing, “Clearly then it must be by induction that we acquire knowledge of the primary principles, because this is also the way in which general concepts are conveyed to us by sense-perception.” These are “first things” or primary principles or premises required by demonstration. And the Arabic tradition understood Aristotle to hold for the necessity of demonstration in the formation of scientific knowledge, something that stringently requires not only proper syllogistic form but also first premises that are known to be true, not just supposed or simply generalized on the basis of experience. 4 For the Arabic tradition, Aristotle?s doctrine of intellectual understanding required the involvement of that separate Agent Intellect to provide a solid foundation for human scientific knowledge by way of intellectual abstraction. (shrink)

I. In “Whitehead’s Theory of Becoming," V.C. Chappell asserts that Whitehead’s “epochal theory of time or becoming is both untenable and unnecessary”. It is untenable, he argues, because “ … the theory itself is unintelligible, and … … the Zenonian argument on which the theory is founded, even in its amended, Whiteheadian version, is invalid”. Although it is not clear to me that Whitehead’s use of Zeno’s arguments is unsound, our concern here will be limited to the first of these (...) two claims. I will attempt to show that Chappell’s demonstration of the unintelligibility of Whitehead’s theory begs the question. His purpose is to present us with a reductio ad absurdum of the epochal theory of time, but he succeeds only in demonstrating that if we substitute other premises for Whitehead’s own, his conclusions will not be consistent with these substitute premises. (shrink)

Résumé — En Enn., VI, 3 [44], 5, Plotin fait usage de doctrines péripatéticiennes concernant la substance, l’inhérence et la prédication. Ces doctrines correspondent de manière frappante à l’interprétation anti-extensionaliste de la substance physique développée par Alexandre d’Aphrodise contre les thèses des commentateurs plus anciens . Le parallèle entre Plotin et Alexandre ne doit cependant pas conduire à penser que Plotin se borne à suivre les thèses du commentateur, et que leurs doctrines soient donc identiques. Plotin vise plutôt à transposer (...) les principes de l’hylémorphisme d’Alexandre dans un contexte philosophique différent, fondé sur des prémisses métaphysiques qui portent sur la causalité des substances intelligibles. Comme Plotin le montre en VI, 3 [44], la forme hylémorphique n’est en effet pas substance : elle s’identifie à un amas de qualités accidentelles qui dépendent d’une essence véritable et extra-physique, le logos. On parvient à des conclusions analogues si l’on aborde la discussion de la causalité démiurgique chez Alexandre et Plotin. Les deux auteurs adressent des critiques approfondies au modèle artisanal de la causalité . Il n’est pas invraisemblable que les thèses d’Alexandre d’Aphrodise aient joué un rôle dans la genèse de la doctrine plotinienne de la causalité. Il faut cependant remarquer, encore une fois, que le cadre conceptuel dans lequel s’inscrivent les critiques de la causalité démiurgique chez Alexandre et chez Plotin est très différent. Pour Alexandre, la causalité démiurgique est rejetée en faveur d’une doctrine cosmologique de la finalité, qui rattache les récurrences naturelles aux mouvements cycliques des astres. Pour Plotin, en revanche, l’ordre naturel s’explique par l’action de principes extra-physiques et sa critique de la causalité démiurgique ne se comprend qu’à l’intérieur de la doctrine de la causalité des êtres intelligibles.Hylémorphisme et causalité des intelligibles.— In Enn., VI, 3 [44], 5 Plotinus outlines some doctrines regarding substance, inherence and predication. Such doctrines are similar to the anti-extensionalist reading of Aristotle’s physical substance that was developed by Alexander of Aphrodisias against previous Aristotelian commentators . The parallel between Plotinus and Alexander is striking : it can safely be inferred that Plotinus’ discussion is based on that of the commentator. This, however, should not lead to assume that Plotinus simply follows Alexander, so that their theories of physical substance are identical. Instead, Plotinus aims to transpose the philosophical principles of Alexander’s Peripatetic hylomorphism into a different doctrinal framework, where metaphysical premises involving the intelligible substances and their causality play a prominent role. As Plotinus shows in VI, 3 [44], the hylomorphic form is not a substance, but a bundle of accidental properties depending on an extra-physical substantial principle, i.e. the logos. The analysis of Alexander’s and Plotinus’ discussions of demiurgic causality lead to the same conclusions. Both philosophers criticize the demiurgic scheme of causality . It can reasonably be assumed that Alexander of Aphrodisias’ treatment played a role in the genesis of Plotinus’ view of causality. However, it is worth noting that the criticism of demiurgic causality has a quite different position within the systems of the two philosophers. Alexander rejects the presence of a demiurgic cause in favour of a cosmological conception of finalism, where natural recurrences are regarded as depending on the cyclical motions of the stars. According to Plotinus, instead, the order of nature is completely caused by extra-physical principles ; his criticism of demiurgic causality points to his doctrine of intelligible causes. (shrink)

The design argument for the existence of God took a probabilistic turn in the 17 th and 18 th centuries. Earlier versions, such as Thomas Aquinas' 5 th way, usually embraced the premise that goal-directed systems (things that "act for an end" or have a function) must have been created by an intelligent designer. This idea – which we might express by the slogan "no design without a designer" – survived into the 17 th and 18 th centuries, 1 and (...) it is with us still in the writings of many creationists. The new version of the argument, inspired by the emerging mathematical theory of probability, removed the premise of necessity. It begins with the thought that goal-directed systems might have arisen by intelligent design or by chance; the problem is to discern which hypothesis is more plausible. With the epistemic concept of plausibility characterized in terms of the mathematical concept of probability, the design argument was given a new direction. The new probabilistic perspective did not extinguish the older idea of "no design without a designer." The two conflicting approaches coexisted, often with less than perfect clarity about their fundamental difference. At the same time, the details of how the probabilistic perspective ought to be articulated were slow in emerging. These characteristics of uneven development are exemplified in a debate that took place in the 18 th century among three probabilists – John Arbuthnot, Nicholas Bernoulli, and Abraham DeMoivre – on the proper explanation of human sex ratio. Arbuthnot proposed a probabilistic version of the design argument, claiming that it is intelligent design, not chance, that provides the better explanation of why slightly more boys than girls are born each year. Bernoulli rejected Arbuthnot's argument. DeMoivre defended Arbuthnot against Bernoulli's criticisms. The problem of explaining sex ratio, both in human populations and in the rest of nature, experienced another transformation after 1859 --it became a problem for the theory of evolution by natural selection. Darwin (1871) addressed the question in the first edition of The Descent of Man but withdrew his suggestion in the second (Darwin 1874). Carl Düsing (1884) provided a mathematical model that moved beyond the explanation Darwin offered. Fisher (1930) added the new idea of parental expenditure. Williams (1966) argued that the sex ratios found in nature provide the opportunity to test hypotheses of group selection against hypotheses of individual selection. Hamilton (1967) constructed a model that generalizes Fisher's approach and represents the effects of both group and individual selection. The theory of natural selection gradually developed the ability to predict sex ratios, not just explain them post hoc. (shrink)

In this paper, we use some elements of the philosophical theories of Wilfrid Sellars and Robert Brandom for examining the interactions between humans and machines. In particular, we adopt the concept of the space of reasons for analyzing the status of artificial intelligent agents. One could argue that AIAs, like the widely used recommendation systems, have already entered the space of reasons, since they seem to make knowledge claims that we use as premises for further claims. This, in turn, (...) can lead to a sense of alienation because AIAs do not quite play by the rules of the space of the reason. We, therefore, ask somewhat pointedly whether aliens have entered the space of reasons. A closer look reveals that it is a misconception to consider AIAs as being in the space of reasons. In fact, they should be seen as very sophisticated tools. Since these tools affect our own acting in the space of reasons, special regulations are required for their proper use. (shrink)

Mahoney, Carl Artificial intelligence is so widespread now, and so well embedded into our latest technology, that nearly all of us know it as AI. It is virtually impossible to fully catalogue its uses and applications because by now it has reached every corner of human activities. This is the premise on which I based a lecture to the Humanist Society of Victoria on October 22, 2015 entitled 'The Artificial Intelligence Debate'. The talk was followed by a spirited discussion at (...) a HSV meeting on November 8 chaired by Dr Jennie Mills, who gave an excellent account of the original talk in the Dec. 2015 Victorian Humanist. This article consists of a series of notes following on from the talk and discussion. (shrink)

John Haught offers a “critical intelligence” argument against naturalism. In this article, I outline Haught’s version of theistic evolution. Then I discuss the case he makes against naturalism with his critical intelligence argument. He uses two versions of the argument to make his case: a trustworthiness of critical intelligence argument and an ineffectiveness of naturalistic theories of the mind argument. I evaluate both versions of his critical intelligence argument against naturalism and find that they contain false premises. They thus (...) come up short in making a strong case against naturalism. (shrink)

In a recent article in this journal, Isaac Taylor warned against the unconstrained use of algorithms as instruments to determine sentences in criminal cases. More precisely, what he argued is that it is important that the sentencing process serves a condemnatory function, and that the introduction of sentencing algorithms threatens to undermine this function. In this reply to Taylor, it is argued that even though his considerations are interesting as they direct attention to the sentencing process and not merely the (...) output of algorithmic sentencing, there are nevertheless reasons to be skeptical of his argument. More precisely, it is argued, first, that it is far from clear that we must accept the basic premise underlying Taylor’s argument, namely, that the sentencing process should serve a condemnatory function and, second, that even if this premise is true, it does not follow that the condemnatory element of the sentencing process is undermined by introducing algorithms as advisory instruments at sentencing. (shrink)

This paper studies the traditional target classification and recognition algorithm based on Histogram of Oriented Gradients feature extraction and Support Vector Machine classification and applies this algorithm to distributed artificial intelligence image recognition. Due to the huge number of images, the general detection speed cannot meet the requirements. We have improved the HOG feature extraction algorithm. Using principal component analysis to perform dimensionality reduction operations on HOG features and doing distributed artificial intelligence image recognition experiments, the results show that the (...) image detection efficiency is slightly improved, and the detection speed is also improved. This article analyzes the reason for these changes because PCA mainly uses the useful feature information in HOG features. The parallelization processing of HOG features on graphics processing unit is studied. GPU is used for high parallel and high-density calculations, and the calculation of HOG features is very complicated. Using GPU for parallelization of HOG features can make the calculation speed of HOG features improved. We use image experiments for the parallelized HOG feature algorithm. Experimental simulations show that the speed of distributed artificial intelligence image recognition is greatly improved. By analyzing the existing digital image recognition methods, an improved BP neural network algorithm is proposed. Under the premise of ensuring accuracy, the recognition speed of digital images is accelerated, the time required for recognition is reduced, real-time performance is guaranteed, and the effectiveness of the algorithm is verified. (shrink)

The article outlines the prospects of using J. Hintikka’s interrogative epistemology for modelling cognitive operations carried out by a cognizing agent to create a machine capable of full cognition. It was established that modeling is divided into two objectives: modeling the cognitive operations and modeling the strategic reasoning. Interrogative epistemology presents a solution to the first objective. It relies on a game-theoretic formal apparatus that allows one to correctly describe all types of possible moves within the framework of a particular (...) cognitive game. The second objective cannot be accomplished in a general epistemological theory framework since strategic considerations about the organization of research and even the concept of knowledge inevitably depend on the broader context of an agent’s practical activity. Therefore, interrogative epistemology does not claim a universal and complete description of the process of cognition, focusing on other tasks: primarily on identifying hidden premises that cognitive agents use in their reasoning. For the same reason, interrogative epistemology deems the possibility of the existence of a cognizing machine as formally unsubstantiated and questionable. However, arguments are given in favor of the fact that interrogative epistemology serves as an acceptable conceptual framework for clarifying the nature of the difficulties that arise with attempts to accomplish the second objective and improving the quality of philosophical discourse in the field of artificial intelligence research. The author substantiates this position by arguing that the ability to cognize presupposes the ability to perform all the essential functions that are usually called mental. (shrink)

In their careful analysis of conversational artificial intelligence (CAI) in psychotherapy, Sedlakova and Trachsel (2023) propose a framework for the ethical evaluation of such technologies that lo...

Linguistic outputs generated by modern machine-learning neural net AI systems seem to have the same contents—i.e., meaning, semantic value, etc.—as the corresponding human-generated utterances and texts. Building upon this essential premise, Herman Cappelen and Josh Dever's Making AI Intelligible sets for itself the task of addressing the question of how AI-generated outputs have the contents that they seem to have (henceforth, ‘the question of AI Content’). In pursuing this ambitious task, the book makes several high-level, framework observations about how (...) a meta-semantic account of the content of AI-generated outputs should proceed. (shrink)

The difficulty of explaining the outputs of artificial intelligence (AI) models and what has led to them is a notorious ethical problem wherever these technologies are applied, including in the medical domain, and one that has no obvious solution. This paper examines the proposal, made by Luciano Floridi and colleagues, to include a new ‘principle of explicability’ alongside the traditional four principles of bioethics that make up the theory of ‘principlism’. It specifically responds to a recent set of criticisms that (...) challenge the supposed need for such a principle to perform an enabling role in relation to the traditional four principles and therefore suggest that these four are sufficient without the addition of explicability. The paper challenges the critics’ premise that explicability cannot be an ethical principle like the classic four because it is explicitly subordinate to them. It argues instead that principlism in its original formulation locates the justification for ethical principles in a midlevel position such that they mediate between the most general moral norms and the contextual requirements of medicine. This conception of an ethical principle then provides a mold for an approach to explicability on which it functions as an enabling principle that unifies technical/epistemic demands on AI and the requirements of high-level ethical theories. The paper finishes by anticipating an objection that decision-making by clinicians and AI fall equally, but implausibly, under the principle of explicability’s scope, which it rejects on the grounds that human decisions, unlike AI’s, can be explained by their social environments. (shrink)

The Automated Self explores meta-theoretical issues in the philosophy of artificial intelligence, combining it with themes from philosophy of science and technology, media and communication studies, and ethics. Balsemão-Pires provides an integrated view of contemporary problems of AI including the theoretical premises and discussions on the meaning, functioning and social uses of cognitive machines, the recent ethical and legal challenges on privacy, interpretability, and data ownership, passing through a careful discussion of the media embedment of the new technologies, and (...) the uses of algorithms in the production and consumption of art. (shrink)

The “God of the gaps” critique is one of the most common arguments against design arguments in biology, but is also increasingly used as a critique of other natural theological arguments. In this paper, I analyze four different critiques of God of the gaps arguments and explore the relationship between gaps arguments and similar limit arguments. I conclude that the critique of the God of the gaps is substantially weaker than is commonly assumed, and dismissing ID´s biological arguments should rather (...) be based on criticizing the premises of these arguments. (shrink)

This article investigates the profound mysteries of consciousness and the afterlife, which have captivated humanity for centuries. In our study, we conducted three hypothetical experiments, assuming all participants had healthy brains and minds in similar environments. We based our methodology on the premise that cell death can preserve anatomical and neural integrity (Vrselja et al., 2019). Between T1 and T2, six brains were rendered non-functional (brain death), eliminating consciousness. Participants were divided into three groups: -/- 1. Identical Triplets (Group I): (...) Individuals 'a,' 'b,' and 'c.' 2. Second Set of Identical Triplets (Group II): Individuals 'd,' 'e,' and 'f.' 3. Non-identical Triplets (Group III): Individuals 'g,' 'h,' and 'i.' -/- Uniform conditions were maintained across all groups, ensuring identical nutrients and processes. In the third experiment, we preserved the inactive brains of 'a,' 'b,' 'd,' 'e,' 'g,' and 'h' from **T1** to **T2** and created three new identical brains for each participant, resulting in twenty-seven new brains labeled ('a1' to 'i3'). The original six brains were revived, leading to a total of thirty-six participant brains in the lab. -/- Results -/- If our third experiment holds validity, it suggests that all twenty-seven artificially created brains and the revived six brains could function, resulting in thirty-six operational brains at T2. This includes the three live brains of 'c,' 'f,' and 'i.' -/- Discussion -/- Determining whose consciousness resides within the lab is practically impossible, except for 'c,' 'f,' and 'i.' For instance, if you are 'a' before *T1*, identifying which of the eleven identical brains ('a', 'a1', 'a2', 'a3', etc.) contains your consciousness at *T2* is challenging. Assuming your consciousness exists in all eleven brains post *T1* raises critical questions: Did your singular consciousness cease to exist, or did it transfer to a specific brain outside the lab? This dilemma highlights profound implications for our understanding of consciousness and identity. -/- Conclusion -/- If your existence ceased (no afterlife- discontinued 'your'-'a'- consciousness-) at T1, theoretically, "you (a)" could reappear ( at T2 ) in two or more identical brains, in the laboratory setting. However, as extensively discussed in this research, a single consciousness can't exist in multiple brains simultaneously. While there may be two, many, or even infinite identical brains in the universe/s, no one experiences( 'feel-aware' ) their consciousness in multiple places simultaneously. Although, the brain's quality and quantity continually change over time—encompassing aspects such as neurons, neurochemistry, and the biophysics of brain function—unique consciousness persists from childhood to death within a single brain. The hypothesis concerning the continuum of unique consciousness within the dynamic framework of the material brain presents a complex enigma. It posits that two unknown fundamental particles—one universally unique ( to a certain brain) and invariant and the other subject to continuous evolution—coexist within the brain throughout its functional lifespan. This coexistence may elucidate the mechanisms underlying consciousness and its persistence, despite the brain's ongoing structural and functional transformations. This simple reasoning strongly supports the high probability of life ('your' -' a') after death occurring in a single brain at T1,( outside the lab) without any passage of time. (shrink)

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

The argument presented in this paper is not a direct attack or defence of the Chinese Room Argument (CRA), but relates to the premise at its heart, that syntax is not sufficient for semantics, via the closely associated propositions that semantics is not intrinsic to syntax and that syntax is not intrinsic to physics. However, in contrast to the CRA’s critique of the link between syntax and semantics, this paper will explore the associated link between syntax and physics. The main (...) argument presented here is not significantly original – it is a simple reflection upon that originally given by Hilary Putnam (Putnam 1988) and criticised by David Chalmers and others: instead of seeking to justify Putnam’s claim that, “every open system implements every Finite State Automaton (FSA)”, and hence that psychological states of the brain cannot be functional states of a computer, I will seek to establish the weaker result that, over a finite time window every open system implements the trace of a particular FSA Q, as it executes program (p) on input (x). That this result leads to panpsychism is clear as, equating Q (p, x) to a specific Strong AI program that is claimed to instantiate phenomenal states as it executes, and following Putnam’s procedure, identical computational (and ex hypothesi phenomenal) states (ubiquitous little ‘pixies’) can be found in every open physical system. (shrink)

This article examines the role of medical doctors, AI designers, and other stakeholders in making applied AI and machine learning ethically acceptable on the general premises of shared decision-making in medicine. Recent policy documents such as the EU strategy on trustworthy AI and the research literature have often suggested that AI could be made ethically acceptable by increased collaboration between developers and other stakeholders. The article articulates and examines four central alternative models of how AI can be designed and (...) applied in patient care, which we call the ordinary evidence model, the ethical design model, the collaborative model, and the public deliberation model. We argue that the collaborative model is the most promising for covering most AI technology, while the public deliberation model is called for when the technology is recognized as fundamentally transforming the conditions for ethical shared decision-making. (shrink)

During the 13th century, several logicians in the Latin medieval tradition showed a special interest in the nature of impossibility, and in the different kinds or ‘degrees’ of impossibility that could be distinguished. This discussion resulted in an analysis of the modal concept with a fineness of grain unprecedented in earlier modal accounts. Of the several divisions of the term ‘impossible’ that were offered, one became particularly relevant in connection with the debate on ars obligatoria and positio impossibilis: the distinction (...) between ‘intelligible’ and ‘unintelligible’ impossibilities. In this article, I consider some 13th-century tracts on obligations that provide an account of the relation between impossibility and intelligibility and discuss the inferential principles that are permissible when we reason from an impossible – but intelligible – premise. I also explore the way in which the 13th-century reflection on this topic survives, in a revised form, in some early 14th-century accounts of positio, namely, those of William of Ockham, Roger Swineshead and Thomas Bradwardine. (shrink)

Logic is a field studied mainly by researchers and students of philosophy, mathematics and computing. Inductive logic seeks to determine the extent to which the premises of an argument entail its conclusion, aiming to provide a theory of how one should reason in the face of uncertainty. It has applications to decision making and artificial intelligence, as well as how scientists should reason when not in possession of the full facts. In this work, Jon Williamson embarks on a quest (...) to find a general, reasonable, applicable inductive logic (GRAIL), all the while examining why pioneers such as Ludwig Wittgenstein and Rudolf Carnap did not entirely succeed in this task. (shrink)

All theories of world creation, whether scientific, philosophical, or religious, can readily acknowledge the fact that humans have primarily evolved to engage with nature, the individual self, fellow human beings, society, and other naturalistic aspect of existence. Nevertheless, several novel challenges ascend when the human mind engages with technology, media, machines, and related concepts such as—ChatGPT, artificial intelligence, and to name a few. For that reason, we need philosophy and critical assessment of the uncovered essence of advanced technologies, media and (...) machines and our way of life concerning them. In other words, protectively assessing their impact requires a thorough examination of ethical and existential concerns, including technology’s implications for freedom, AI’s evolving role, the essence of human being, and the unexamined transformative societal changes that follow. Building upon the premise that these phenomena share a common thread despite their apparent disparities, our interdisciplinary pursuit draws inspiration from philosophical luminaries such as Luciano Floridi, Karamjit S. Gill, David Kaplan, Aldous Huxley, Martin Heidegger, Hannah Arendt, and Gandhi. Through philosophical insights, we explore the essence of technology and its broad effects, with a focus on its impact on human freedom and essence in both public and private domains. (shrink)

Law and the legal system through which law is effected are very powerful, yet the power of the law has always been limited by the laws of nature, upon which the law has now direct grip. Human law now faces an unprecedented challenge, the emergence of a second limit on its grip, a new “species” of intelligent agents (AI machines) that can perform cognitive tasks that until recently only humans could. What happens, as a matter of law, when another species (...) interacts with us, can be integrated into human minds and bodies, makes “real-world” decisions—not through human proxies, but directly—and does all this “intelligently”, with what one could call autonomous agency or even a “mind” of its own? The article starts from the clear premise that control cannot be exercised directly on AI machines through human law. That control can only be effected through laws that apply to humans. This has several regulatory implications. The article’s first discusses what, in any attempt to regulate AI machines, the law can achieve. Having identified what the law can do, the article then canvases what the law should aim to achieve overall. The article encapsulate its analysis in a list of both doctrinal and normative principles that should underpin any regulation aimed at AI machines. Finally, the article compares three transnational options to implement the proposed regulatory approach. (shrink)

Artificial intelligence and normative ethics: Who is responsible for the crime of LAWS? In his text “Killer Robots”, Robert Sparrow holds that killer robots should be forbidden. This conclusion is based on two premises. The first is that attributive responsibility is a necessary condition for admitting an action; the second premise is that the use of killer robots is accompanied by a responsibility gap. Although there are good reasons to conclude that killer robots should be banned, the article shows (...) that Sparrow's argument for the ban is not correct. (shrink)

Artificial intelligence algorithms, fueled by continuous technological development and increased computing power, have proven effective across a variety of tasks. Concurrently, quantum computers have shown promise in solving problems beyond the reach of classical computers. These advancements have contributed to a misconception that quantum computers enable hypercomputation, sparking speculation about quantum supremacy leading to an intelligence explosion and the creation of superintelligent agents. We challenge this notion, arguing that current evidence does not support the idea that quantum technologies enable hypercomputation. (...) Fundamental limitations on information storage within finite spaces and the accessibility of information from quantum states constrain quantum computers from surpassing the Turing computing barrier. While quantum technologies may offer exponential speed-ups in specific computing cases, there is insufficient evidence to suggest that focusing solely on quantum-related problems will lead to technological singularity and the emergence of superintelligence. Subsequently, there is no premise suggesting that general intelligence depends on quantum effects or that accelerating existing algorithms through quantum means will replicate true intelligence. We propose that if superintelligence is to be achieved, it will not be solely through quantum technologies. Instead, the attainment of superintelligence remains a conceptual challenge that humanity has yet to overcome, with quantum technologies showing no clear path toward its resolution. (shrink)

This paper adds another argument to the rising tide of panic about robots and AI. The argument is intended to have broad civilization-level significance, but to involve less fanciful speculation about the likely future intelligence of machines than is common among many AI-doomsayers. The argument claims that the rise of the robots will create a crisis of moral patiency. That is to say, it will reduce the ability and willingness of humans to act in the world as responsible moral agents, (...) and thereby reduce them to moral patients. Since that ability and willingness is central to the value system in modern liberal democratic states, the crisis of moral patiency has a broad civilization-level significance: it threatens something that is foundational to and presupposed in much contemporary moral and political discourse. I defend this argument in three parts. I start with a brief analysis of an analogous argument made in pop culture. Though those arguments turn out to be hyperbolic and satirical, they do prove instructive as they illustrates a way in which the rise of robots could impact upon civilization, even when the robots themselves are neither malicious nor powerful enough to bring about our doom. I then introduce the argument from the crisis of moral patiency, defend its main premises and address objections. (shrink)

In his new book, Knowledge: The Philosophical Quest in History, Steve Fuller returns to core themes of his program of social epistemology that he first outlined in his 1988 book, Social Epistemology. He develops a new, unorthodox theology and philosophy building upon his testimony in Kitzmiller v. Dover Area School District in defense of intelligent design, leading to a call for maximal human experimentation. Beginning from the theological premise rooted in the Abrahamic religious tradition that we are created in the (...) image of God, Fuller argues that the spark of the divine within us distinguishes us from animals. I argue that Fuller’s recent work takes us away from key insights of his original work. In contrast, I advocate for a program of social epistemology rooted in evolutionary science rather than intelligent design, emphasize a precautionary and ecological approach rather than a proactionary approach that favors risky human experimentation, and attend to our material and sociological embeddedness rather than a transhumanist repudiation of the body. (shrink)

The premise is that Heidegger remained a phenomenologist from beginning to end and that phenomenology is exclusively about meaning and its source. The essay presents Heidegger’s interpretation of the being (Sein) of things as their meaningful presence (Anwesen) and his tracing of such meaningful presence back to its source in the clearing, which is thrown-open or appropriated ex-sistence (das ereignete/geworfene Da-sein). The essay argues five theses: (1) Being is the meaningful presence of things to man. (2) Such meaningful presence is (...) the Befragtes of Heidegger’s question, not the Erfragtes. (3) Being and Time’s goal was to articulate the openness that allows for all meaningfulness. (4) Ereignis—the appropriation of ex-sistence to sustaining the clearing—is the later Heidegger’s reinscription of thrown-openness, der geworfene Entwurf. (5) Appropriated thrown-openness, as the clearing, is intrinsically hidden, i.e., unknowable.Some preliminariesI cite Heidegger’s texts by page and line (the line-number follows the period) in both the Gesamtausgabe and the current English translations where available, all of which are listed in the bibliography at the end of this issue of the journal. I cite Sein und Zeit in the Niemeyer 11th edition and in the ET by Macquarrie-Robinson.Sinn and Bedeutung are closely related, although Sinn is broader than Bedeutung. Sinn refers either to intelligibility as such or to the fact of something being intelligible, whereas Bedeutung is the specific meaning that a thing has. Sinn as intelligibility is generally interchangeable with Bedeutsamkeit and Verständlichkeit. Thus I translate Sinn as “intelligibility” or “meaningfulness.” Sinn in turn allows for Bedeutung as the particular meaning of a specific thing.I take “intellect” in the broad sense of νοῦς and in the specific sense of λόγος understood as discursive intellect, whether practical or theoretical.Dasein, Existenz, and Da-sein are all translated as "ex-sistence." The context indicates whether that is meant in an existentiel or an existential sense. The word “man” refers to human being, not the male of the species. I render das Seiende as “beings,” “things,” and “entities” ex aequo. All German words referring to ἀλήϑεια (e.g., Wahrheit, Erschlossenheit, Unverborgenheit, Entborgenheit, etc.) are translated by “disclosedness” or “openness.” Translations from the German are often my own. (shrink)

With recent advancements in systems engineering and artificial intelligence, autonomous agents are increasingly being called upon to execute tasks that have normative relevance. These are tasks that directly—and potentially adversely—affect human well-being and demand of the agent a degree of normative-sensitivity and -compliance. Such norms and normative principles are typically of a social, legal, ethical, empathetic, or cultural nature. Whereas norms of this type are often framed in the abstract, or as high-level principles, addressing normative concerns in concrete applications of (...) autonomous agents requires the refinement of normative principles into explicitly formulated practical rules. This paper develops a process for deriving specification rules from a set of high-level norms, thereby bridging the gap between normative principles and operational practice. This enables autonomous agents to select and execute the most normatively favourable action in the intended context premised on a range of underlying relevant normative principles. In the translation and reduction of normative principles to SLEEC rules, we present an iterative process that uncovers normative principles, addresses SLEEC concerns, identifies and resolves SLEEC conflicts, and generates both preliminary and complex normatively-relevant rules, thereby guiding the development of autonomous agents and better positioning them as normatively SLEEC-sensitive or SLEEC-compliant. (shrink)

In this paper it is shown how tools developed in argumentation theory and artificial intelligence can be applied to the development of a new dialectical analysis of the speech act of making a proposal in a deliberation dialogue. These tools are developed, modified and used to formulate dialogue pre-conditions, defining conditions and post-conditions for the speech act of making a proposal in a deliberation dialogue. The defining conditions set out what is required for a move in a dialogue to count (...) as the making of a proposal by one of the parties. What is required are the conditions that (1) the move fit the requirements of the argumentation scheme for practical reasoning, and (2) the premises are propositions describing common goals of both parties or propositions that they reasonably consider means to achieve these goals. The analysis goes beyond the standard speech act approach by specifying not only the normative requirements for making a well-formed proposal, but also the requirements for responding to it by questioning or criticizing it, and the requirements for defending it. (shrink)