Philosophy of Artificial Intelligence, Miscellaneous

Ikigai is a Japanese concept, which, in brief, refers to the “reason or purpose to live”. I-risks have been identified as a category of risks complementing x- risks, i.e., existential risks, and s-risks, i.e., suffering risks, which describes undesirable future scenarios in which humans are deprived of the pursuit of their individual ikigai. While some developments in AI increase i-risks, there are also AI-driven virtual opportunities, which reduce i-risks by increasing the space of potential ikigais, largely due to developments in (...) generative AI, virtual worlds as well as AI-driven hyper-personalization. The purpose of this paper is to present a first attempt to map the potential AI-driven virtual hyper-personalised ikigai universe. Moreover, challenges and further ideas are presented. (shrink)

Perception solves computationally demanding problems at lightning fast speed. It recovers sophisticated representations of the world from degraded inputs, often in a matter of milliseconds. Any theory of perception must be able to explain how this is possible; in other words, it must be able to explain perception's computational tractability. One of the few attempts to move toward such an explanation has been the information encapsulation hypothesis, which posits that perception can be fast because it keeps computational costs low by (...) forgoing access to information stored in cognition. I argue that we have no compelling reason to believe that encapsulation explains (or even contributes to an explanation of) perceptual tractability, and much reason to doubt it. This is because there exist much deeper computational challenges for perception than information access, and these threaten to make the costs of access irrelevant. If this is right, it undermines a core computational motivation for encapsulation and sends us back to the drawing board for explanations of perceptual tractability. (shrink)

La inteligencia artificial (IA) es la capacidad de una máquina o sistema informático para simular y realizar tareas que normalmente requerirían inteligencia humana, como el razonamiento lógico, el aprendizaje y la resolución de problemas. La inteligencia artificial se basa en el uso de algoritmos y tecnologías de aprendizaje automático para dar a las máquinas la capacidad de aplicar ciertas habilidades cognitivas y realizar tareas por sí mismas de manera autónoma o semiautónoma. La inteligencia artificial se distingue por su grado de (...) capacidad cognitiva o por su grado de autonomía. Por capacidad puede ser débil o limitada, general o superlativa. Por su autonomía, puede ser reactiva, deliberativa, cognitiva o totalmente autónoma. A medida que la inteligencia artificial mejora, muchos procesos son cada vez más eficientes y, tareas que hoy parecen complicadas se estarán realizando con mayor rapidez y precisión. (shrink)

One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance and linking the model to the phenomenon, (ii) (...) explanation, and (iii) attributions of understanding. At each of these stages, non-epistemic values can, in part, determine how much machine learning model opacity poses a problem. (shrink)

Chapter 3 of the ongoing publication "Artificial Aesthetics" Book information: Assume you're a designer, an architect, a photographer, a videographer, a curator, an art historian, a musician, a writer, an artist, or any other creative professional or student. Perhaps you're a digital content creator who works across multiple platforms. Alternatively, you could be an art historian, curator, or museum professional. -/- You may be wondering how AI will affect your professional area in general and your work and career. Our book (...) offers intellectual tools to help us better see the future of creative fields. These tools come from philosophy of art, experimental psychology, media theory, digital culture studies and data science. The book is the first to combine these different perspectives together. -/- We started the work on the book in summer 2019, exchanging numerous messages, commenting on each other ideas, and sharing drafts of sections. The final book is a result of this process. Although each chapter is written by one author, it reflects the discussions we had over 27 months. (shrink)

Artificial intelligence (AI) is a radically transformative technology (or system of technologies) that created new existential possibilities and new standards of well-being in human societies. In this article, I argue that to properly understand the increasingly important role AI plays in our society, we must consider its impacts on social justice. For this reason, I propose to conceptualize AI's transformative role and its socio-political implications through the lens of the theory of social justice known as the Capability Approach. According to (...) the approach, a just society must put its members in a position to acquire and exercise a series of basic capabilities and provide them with the necessary means for these capabilities to be actively realized. Because AI is re-shaping the very definition of some of these basic capabilities, I conclude that AI itself should be considered among the conditions of possession and realization of the capabilities it transforms. In other words, access to AI-in the many forms this access can take-is necessary for social justice. (shrink)

In recent years, a number of philosophers have turned their attention to developments in Artificial Intelligence, and in particular to deepfakes. A deepfake is a portmanteau of ‘deep learning' and ‘fake', and for the most part they are videos which depict people doing and saying things they never did. As a result, much of the emerging literature on deepfakes has turned on questions of trust, harms, and information-sharing. In this paper, I add to the emerging concerns around deepfakes by drawing (...) on resources from vice epistemology. As deepfakes become more sophisticated, I claim, they will develop to be a source of online epistemic corruption. More specifically, they will encourage consumers of digital online media to cultivate and manifest various epistemic vices. My immediate focus in this paper is on their propensity to encourage the development of what I call ‘intellectual cynicism'. After sketching a rough account of this epistemic vice, I go on to suggest that we can partially offset such cynicism – and fears around deceptive online media more generally – by encouraging the development what I term a trained ‘digital sensibility'. This, I contend, involves a calibrated sensitivity to the epistemic merits of online content. (shrink)

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, (...) and implications). We analyse this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasise or prioritise some over others depending on the proactive or reactive use of accountability and the missions of AI governance. (shrink)

The main problems related to the integration of diverse functional elements of advanced intelligent learning systems are considered. It is shown that the integration of the elements in the learning process is a complex multilayered process due to the great variety and complexity of the ongoing basic processes – cognitive, pedagogical, technological, social and interpersonal. It is emphasized that the need for the integration process to be solved as a multifactor optimization data-driven problem and the use of modern techniques in (...) the field of artificial intelligence – machine learning, pattern recognition, natural language processes, network management. (shrink)

This peer-reviewed book is a concise introduction to key philosophical questions in artificial intelligence that have long been debated by many of the great minds in computer science, cognitive science and philosophy, from Gottfried Leibniz to Alan Turing to Hubert Dreyfus. Topics include the limits of and problems in trying to create artificial general intelligence, if a computer can really think and have human-like sentience, how to identify intelligence in a computer, ethical and danger issues, and if human-like consciousness and (...) sentience are required to create artificial general intelligence. (shrink)

Do we suddenly become justified in treating robots like humans by positing new notions like “artificial moral agency” and “artificial moral responsibility”? I answer no. Or, to be more precise, I argue that such notions may become philosophically acceptable only after crucial metaphysical issues have been addressed. My main claim, in sum, is that “artificial moral responsibility” betokens moral responsibility to the same degree that a “fake orgasm” betokens an orgasm.

For the philosopher, the most critical and fundamental question in the project of Artificial Intelligence is the question of intelligence or cognition in general. From the beginning of the research in “thinking Machining”, or Artificial Intelligence as it later became known, the key question is: What makes a thing intelligent or what constitutes intelligence? Since, intelligence, is a fundamental activity of the mind, the question, has been: Whether the mind is a computer or is the computer a mind? Many philosophers (...) who have and are engaging and interrogating these problematics, do it from the perspective of modern and contemporary philosophy of mind, consciousness and language. The objective of this work is to interrogate the question of “intelligence” in Artificial Intelligence from the perspective of the Scholastics’ notion of Intellectus. (shrink)

What is the essential ingredient of creativity that only humans – and not machines – possess? Can artificial intelligence help refine the notion of creativity by reference to that essential ingredient? How / do we need to redefine our conceptual and legal frameworks for rewarding creativity because of this new qualifying – actually creatively significant – factor? -/- Those are the questions tackled in this essay. The author’s conclusion is that consciousness, experiential states (such as a raw feel of what (...) is like to be creating) and propositional attitudes (such as intention to instigate change by creating) appear pivotal to qualifying an exploratory effort as creativity. Artificial intelligence systems would supposedly be capable of creativity if they could exhibit such states, which philosophers and computer scientists posit as conceptually admissible and practically possible. -/- The existing legal framework rewards creative endeavours by reference to the novelty or originality of the end result. But this bar is not insurmountable for artificial intelligence. Technically speaking, artificial intelligence systems can create works that are novel and/or original. Are we then prepared to grant to those systems the legal status of “creators” in their own right? Whom should the associated benefits and rewards be assigned to? How does the position change (or not) based on the qualifying factors set out above? Should – and if, how – the general public benefit from inventions / creative works of artificial intelligence systems if troves of personal data are the key component that fueled and informed creative choices? (shrink)

Argumentation schemes [1–3] are a relatively recent notion that continues an extremely ancient debate on one of the foundations of human reasoning, human comprehension, and obviously human argumentation, i.e., the topics. To understand the revolutionary nature of Walton’s work on this subject matter, it is necessary to place it in the debate that it continues and contributes to, namely a view of logic that is much broader than the formalistic perspective that has been adopted from the 20th century until nowadays. (...) With his book Argumentation schemes for presumptive reasoning, Walton attempted to start a dialogue between three different fields or views on human reasoning – one (argumentation theory) very recent, one (dialectics) very ancient and with a very long tradition, and one (formal logic) relatively recent, but dominating in philosophy. Argumentation schemes were proposed as dialectical instruments, in the sense that they represented arguments not only as formal relations, but also as pragmatic inferences in the sense that they depend on what the interlocutors share and accept in a given dialogical circumstance and affect their dialogical relation. In this introduction, the notion of argumentation scheme will be analyzed in detail, showing its different dimensions and its defining features which make them an extremely useful instrument in Artificial Intelligence. This theoretical background will be followed by a literature review on the uses of the schemes in computing, aimed at identifying the most important areas and trends, the most promising proposals, and the directions of future research. (shrink)

As we await the increasingly likely advent of genuinely intelligent artificial systems, a fair amount of consideration has been given to how we humans will interact with them. Less consideration has been given to how—indeed if—we humans will love them. What would human-AI romantic relationships look like? What do such relationships tell us about the nature of love? This chapter explores these questions via consideration of several works of science fiction, focusing especially on the Black Mirror episode “Be Right Back” (...) and the Spike Jonze's movie *Her*. As I suggest, there may well be cases where it is both possible and appropriate for a human to fall in love with a machine. (shrink)

Can the machines that play board games or recognize images only in the comfort of the virtual world be intelligent? To become reliable and convenient assistants to humans, machines need to learn how to act and communicate in the physical reality, just like people do. The authors propose two novel ways of designing and building Artificial General Intelligence (AGI). The first one seeks to unify all participants at any instance of the Turing test – the judge, the machine, the human (...) subject as well as the means of observation instead of building a separating wall. The second one aims to design AGI programs in such a way so that they can move in various environments. The authors of the article thoroughly discuss four areas of interaction for robots with AGI and introduce a new idea of techno-umwelt bridging artificial intelligence with biology in a new way. (shrink)

In this work, we survey skepticism regarding AI risk and show parallels with other types of scientific skepticism. We start by classifying different types of AI Risk skepticism and analyze their root causes. We conclude by suggesting some intervention approaches, which may be successful in reducing AI risk skepticism, at least amongst artificial intelligence researchers.

In this chapter I present the thesis of Proper Embodiment: the claim that (at least some of) the details of our physiology matter to cognition and consciousness in a fundamental way. This thesis is composed of two sub-claims: (1) if we are to design, build, or evolve artificial systems that are cognitive in the way that we are, these systems will have to be internally embodied, and (2) the exploitation of the particular internal embodiment that allows systems to evolve solutions (...) with greatly decreased computational complexity – and thus to be flexible and adaptive in the ways that are characteristic of cognitive systems – means that the orthodox distinction between algorithm and implementation is undermined. Evolved cognitive systems are therefore likely not to instantiate the distinction between phenomenology, algorithm, and implementation. The leaky levels evident in evolved cognitive systems motivate an extension of orthodox embodied cognitive science to the internal, affective, “gooey” realm that has so far only been embraced by those in the enactive tradition. This suggests that if we are to build artificial systems that will be genuinely cognitive they will have to incorporate parts of the gooey realm currently considered to be “mere implementation.”. (shrink)

This essay for the inaugural issue of Technology and Language discusses the problem of finding an optimal form of human-machine communication. In the ongoing search for an alien mind, humanity seems to find it not in the infinities of space, but in its own environment. Changes in the language of human-machine interaction made it understandable not only to trained specialists but to every household. In the course of time, home appliances and devices have developed their language abilities even more and (...) reached a very advanced level – by way of status indicators, displays, emergency sound and color signals. The transition to computer-assisted communication brought about a great diversity of human expression forms translated into the discrete digital language of technologies. According to some prognoses, the first human-robot marriage might be registered in the future, however, such a union is not the only possible human-machine alliance. (shrink)

The focus of this article is a question that has been neglected in debates about digitalization: Could machines replace human scientists? To provide an intelligible answer to it, we need to answer a further question: What is it that makes (or constitutes) a scientist? I offer an answer to this question by proposing a new demarcation criterion for science which I call “the discoverability criterion”. I proceed as follows: (1) I explain why the target question of this article is important, (...) and (2) show that it leads to a variant of the demarcation problem of science. (3) By arguing that it is probably an essential feature of science that we can make scientific discoveries, I suggest a novel way of dealing with this problem by proposing a new demarcation criterion. Before introducing it, (4) I analyze an exemplary case of a real scientific discovery, and (5) argue that scientific discovery processes have a general underlying structure. (6) I introduce my discoverability criterion for science and present my master argument that helps us understand which criteria have to be fulfilled in order to decide whether machines can replace human scientists or not. (7) I conclude by answering the article’s target question and bringing forward a take-home message. (shrink)

Set aside fanciful doomsday speculations about AI. Even lower-level AIs, while otherwise friendly and providing us a universal basic income, would be able to do all our jobs. Also, we would over-rely upon AI assistants even in our personal lives. Thus, John Danaher argues that a human crisis of moral passivity would result However, I argue firstly that if AIs are posited to lack the potential to become unfriendly, they may not be intelligent enough to replace us in all our (...) jobs. If instead they are intelligent enough to replace us, the risk they become unfriendly increases, given that they would not need us and humans would just compete for valuable resources. Their hostility will not promote our moral passivity. Secondly, the use of AI assistants in our personal lives will become a problem only if we rely on them for almost all our decision-making and motivation. But such a (maximally) pervasive level of dependence raises the question of whether humans would accept it, and consequently whether the crisis of passivity will arise. (shrink)

Export is an activity of selling goods to another country. Indonesia’s main export capital is natural wealth. From natural wealth owned, can be produced various kinds of export goods. Goods that can be exported are goods that are in demand and needed by overseas buyers. Indonesian export commodities consist of petroleum and gas (oil and gas) as well as non-oil and gas. This study aims to predict the value of Indonesia’s oil and gas exports with the Network of Artificial Backpropogation. (...) Data obtained from customs documents of the Directorate General of Customs and Excise (PEB and PIB) obtained from the Central Bureau of Statistics with url. The data used are the last 40 years (1975-2014) data which is divided into 4 decades (10 years/decade). From five architectural models 10-2-1, architecture 10-5-1, architecture 10-2-5-1, architecture 10-5-2-1 and architecture 10-10-1 obtained the best 10-5-1 model With epoch 28630, MSE training 0,0010005738, MSE testing 0,0034417506. With this architecture model obtained 90% prediction accuracy for the export value of Indonesia. (shrink)

The 21st century has witnessed accelerating growth in the utilization of digitized financial services. There is a consistent effort to simulate, augment, enhance, and replicate human intelligence using artificial technologies, to form intelligent machines. Artificial intelligence, which has its foundations in computer science, linguistics, psychology, mathematics, and philosophy has proved to be a powerful tool in financial services. Firms are now utilizing analytical tools such as machine learning and ANN to analyse data collected over time. AI enhances adaptive pattern recognition (...) using modern statistical methods and large volumes of data to provide the 'best guess' answer to any specific and narrowly-defined problem set. AI has a high potential for positive impact when deployed with care, prudence, and diligence. This paper seeks to expound on the application of AI tools in finance and the implications of implementation to financial stability. Important considerations for AI use include benefits to clients and firms, data privacy and protection, elimination of human capital, and sustainability of financial services. (shrink)

Research in artificial intelligence (AI) has led to revise the challenges of the AI initial programme as well as to keep us alert to peculiarities and limitations of human cognition. Both are linked, as a careful further reading of the Turing’s test makes it clear from Searle’s Chinese room apologue and from Dreyfus’ suggestions, and in both cases, ideal had to be turned into operating mode. In order to rise these more pragmatic challenges AI does not hesitate to link together (...) operations of various levels and functionalities, more specific or more general. The challenges are not met by an operating formal system which should have from the outset all the learning skills, but -for instance in simulation- by the dynamics of a succession of solutions open to adjustments as well as to reflexive repeats. (shrink)

We argue that legal argumentation, as the subject matter as well as a special subfield of Argumentation Studies (AS), has to be examined by making skilled use of the full panoply of tools such as argumentation and story schemes which are at the forefront of current work in AS. In reviewing the literature, we make explicit our own methodological choices (particularly regarding the place of normative deliberation in practical reasoning) and then illustrate the implications of such an approach through the (...) analysis of a case study in the English law of evidence. We argue that a clear distinction must be drawn between practical argumentation and stories. Because of the institutional separation between legal judgment and fact-finding in common-law jury trials, we argue for the combination of argument and story-based analysis. (shrink)

Último sermão da Igreja do naturalismo fundamentalista pelo pastor Hofstadter. Como o seu muito mais famoso (ou infame por seus erros filosóficos implacáveis) Godel, Escher, Bach, ele tem uma plausibilidade superficial, mas se se compreende que este é um scientismo desenfreado que mistura questões científicas reais com os filosóficos (ou seja, o somente as edições reais são que jogos da língua nós devemos jogar) então quase todo seu interesse desaparece. Eu forneci um quadro para análise baseada na psicologia evolutiva e (...) no trabalho de Wittgenstein (desde que atualizado em meus escritos mais recentes). Aqueles que desejam um quadro até à data detalhado para o comportamento humano da opinião moderna dos dois sistemas consultar meu livros Falando Macacos 3ª Ed (2019), A Estrutura Lógica da Filosofia, Psicologia, Mente e Linguagem em Ludwig Wittgenstein e John Searle 2a Ed (2019), Suicídio Pela Democracia,4aEd(2019), Entendendo as Conexões entre Ciência, Filosofia, Psicologia, Religião, Política e Economia Artigos e Análises 2006-2019 (2019), Ilusões Utópicas Suicidas no 21St século. (shrink)

This article offers a critical interpretation of Artificial Intelligence (AI) as a philosophical notion which exemplifies a secular conception of thinking. One way in which AI notably differs from the conventional understanding of “thinking” is that, according to AI, “intelligence” or “thinking” does not necessarily require “life” as a precondition: that it is possible to have “thinking without life.” Building on Charles Taylor’s critical account of secularity as well as Hubert Dreyfus’ influential critique of AI, this article offers a theological (...) analysis of AI’s “lifeless” picture of thinking in relation to the Augustinian conception of God as “Life itself.” Following this critical theological analysis, this article argues that AI’s notion of thinking promotes a societal privilege of certain rationalistic or calculative ways of thought over more existential or spiritual ways of thinking, and thereby fosters a secularization or de-spiritualization of thinking as an ethical human practice. (shrink)

Abstract: In the future, it will be possible to create advance simulations of ancestor in computers. Superintelligent AI could make these simulations very similar to the real past by creating a simulation of all of humanity. Such a simulation would use all available data about the past, including internet archives, DNA samples, advanced nanotech-based archeology, human memories, as well as text, photos and videos. This means that currently living people will be recreated in such a simulation, and in some sense, (...) “resurrected”. Such “resurrectional simulation” could be deliberately created just for this goal: to return to life all people who have ever lived. The main technical problem of such simulation will be uncertainty about the past, which increases exponentially for more remote times. Such problem could be partly addressed by “acausal trade” between different branches of the multiverse, which will create slightly different versions of the simulation using a quantum randomness generator. Such trade will result in resurrection of all possible people (including those who existed in other branches). Ethical problems of such a resurrectional simulation include: a) possible resurrection of some people against their will; b) such simulation may create additional suffering; с) such simulation could be used by hostile AI to return people to life and then torture them. In this work, I explore preliminary ideas about how to address these problems. (shrink)

Thinking Machines and the Philosophy of Computer Science: Concepts and Principles presents a conversation between established experts and new researchers in the field of philosophy and computer science about human and non-human relationships with the environment. This resource contains five sections including topics on philosophical analysis, the posterior ethical debate, the nature of computer simulations, and the crossroads between robotics, AI, cognitive theories and philosophy.

This paper proposes that existing computational modeling research programs may be combined into platforms for the information of public policy. The main idea is that computational models at select levels of organization may be integrated in natural terms describing biological cognition, thereby normalizing a platform for predictive simulations able to account for both human and environmental costs associated with different action plans and institutional arrangements over short and long time spans while minimizing computational requirements. Building from established research programs, the (...) proposal aims to take advantage of current momentum in the direction of the integration of the cognitive with social and natural sciences, reduce start-up costs and increase speed of development. These are all important upshots given rising unease over the potential for AI and related technologies to shape the world going forward. (shrink)

The purpose of this paper is to offer some critical remarks on the so-called pragmatist approach to the regulation of robotics. To this end, the article mainly reviews the work of Jack Balkin and Joanna Bryson, who have taken up such ap- proach with interestingly similar outcomes. Moreover, special attention will be paid to the discussion concerning the legal fiction of ‘electronic personality’. This will help shed light on the opposition between essentialist and pragmatist methodologies. After a brief introduction (1.), (...) in 2. I introduce the main points of the methodological debate which opposes pragmatism and essentialism in the regulation of robotics and I examine how legal fictions are framed from a pragmatist, functional perspective. Since this approach entails a neat separation of ontological analysis and legal rea- soning, in 3. I discuss whether considerations on robots’ essence are actually put into brackets when the pragmatist approach is endorsed. Finally, in 4. I address the problem of the social valence of legal fictions in order to suggest a possible limit of the pragmatist approach. My conclusion (5.) is that in the specific case of regulating robotics it may be very difficult to separate ontological considerations from legal reasoning—and vice versa—both on an epistemological and social level. This calls for great caution in the recourse to anthropomorphic legal fictions. (shrink)

A brief history of affective responses to AI.

This paper focuses on the research field of machine ethics and how it relates to a technological singularity—a hypothesized, futuristic event where artificial machines will have greater-than-human-level intelligence. One problem related to the singularity centers on the issue of whether human values and norms would survive such an event. To somehow ensure this, a number of artificial intelligence researchers have opted to focus on the development of artificial moral agents, which refers to machines capable of moral reasoning, judgment, and decision-making. (...) To date, different frameworks on how to arrive at these agents have been put forward. However, there seems to be no hard consensus as to which framework would likely yield a positive result. With the body of work that they have contributed in the study of moral agency, philosophers may contribute to the growing literature on artificial moral agency. While doing so, they could also think about how the said concept could affect other important philosophical concepts. (shrink)

Many global catastrophic risks are threatening human civilization, and a number of ideas have been suggested for preventing or surviving them. However, if these interventions fail, society could preserve information about the human race and human DNA samples in the hopes that the next civilization on Earth will be able to reconstruct Homo sapiens and our culture. This requires information preservation of an order of magnitude of 100 million years, a little-explored topic thus far. It is important that a potential (...) future civilization will discover this information as early as possible, thus a beacon should accompany the message in order to increase visibility. The message should ideally contain information about how humanity was destroyed, perhaps including a continuous recording until the end. This could help the potential future civilization to survive. The best place for long-term data storage is under the surface of the Moon, with the beacon constructed as a complex geometric figure drawn by small craters or trenches around a central point. There are several cost-effective options for sending the message as opportunistic payloads on different planned landers. (shrink)

This paper presents a theoretical study of the binary oppositions underlying the mechanisms of natural computation understood as dynamical processes on natural information morphologies. Of special interest are the oppositions of discrete vs. continuous, structure vs. process, and differentiation vs. integration. The framework used is that of computing nature, where all natural processes at different levels of organisation are computations over informational structures. The interactions at different levels of granularity/organisation in nature, and the character of the phenomena that unfold through (...) those interactions, are modeled from the perspective of an observing agent. This brings us to the movement from binary oppositions to dynamic networks built upon mutually related binary oppositions, where each node has several properties. (shrink)

Begriffsanalyse und empirische Forschung auf dem Gebiet der künstlichen Intelligenz zeigen, daß momentan derart kontroverse Ansätze entwickelt werden, daß gemeinschaftliche Momente in Perspektiven und angewandten Methodologien nur schwer auszumachen sind.

Die Debatten zu den Themen Physikalismus, Willensfreiheit und Künstliche Intelligenz stehen seit einigen Jahren im Mittelpunkt der Philosophie des Geistes. In den Debatten um den Physikalismus geht es dabei u.a. um folgende Fragen: Lässt sich alles, was es gibt, physikalisch erklären - auch der menschliche Geist? Lässt sich alles auf das Physische reduzieren? Ist der Bereich des Physischen kausal geschlossen? Realisiert das Physische das Mentale? Wie lässt sich mentale Verursachung erklären? In den Debatten um Willensfreiheit fragt man sich: Sind wir (...) physikalisch determiniert? Können wir freie Entscheidungen treffen? Sind wir für unsere Handlungen verantwortlich? Wie verhalten sich Freiheit und Determinismus zueinander? Welchen Beitrag kann die empirische Forschung leisten? In den Debatten um Künstliche Intelligenz werden diese Fragen verhandelt: Können Maschinen denken? Unterscheiden sich Menschen von Maschinen? Sind uns Computer geistig überlegen? Stellt maschinelle Intelligenz ein Modell für menschliche Kognition dar? Welchen wissenschaftlichen Nutzen kann Künstliche Intelligenz haben? Zu jedem der drei Themen Physikalismus, Willensfreiheit und Künstliche Intelligenz bietet das Buch drei Originalbeiträge von renommierten Wissenschaftlern verschiedener Disziplinen sowie jeweils einen kritischen Kommentar. Auf diese Weise soll deutlich werden, dass die drei Themen nicht nur eng miteinander zusammenhängen, sondern auch interdisziplinär von großem Interesse sind. (shrink)

Reviews the book, Artificial intelligence and human reason: A teleological critique by J. F. Rychlak. There is a frequent lack of communication between artificial intelligence practitioners and critics. Joseph Rychlak's latest book is an attempt to contribute productively to this exchange. He introduces his work by stating: "I think it is relatively easy to write a superficial book "picking" on the computer as somehow inadequate to the description of human beings. Overall, Rychlak provides a sufficiently detailed criticism of computational approaches (...) to intelligence to allow access to the implications of his work by the artificial intelligence practitioner and I recommend his book to this group. 2012 APA, all rights reserved). (shrink)

We are not today close to producing a computer that could convince us that it is intelligent. Some philosophers have argued that we are not even appreciably closer to this goal than we were ten years ago. But why should artificial intelligence even be considered possible? In this paper I shall argue that the temptation to believe in the possibility of AI stems from a misunderstanding about the nature of ideas; further, this misunderstanding can be traced back at least to (...) Plato’s presentation of the metaphor of the cave. A phenomenological view of the nature of ideas removes the temptation to believe in the possibility of AI as presently considered. (shrink)

Improvements in computational hardware enabled by nanotechnology promise a dual revolution in coming decades: machines which are both more intelligent and more numerous than human beings. This possibility raises substantial concern over the moral nature of such intelligent machines. An analysis of the prospects involves at least two key philosophical issues. The first, intentionality in formal systems, turns on whether a “mere machine” can be a mind whose thoughts have true meaning and understanding. Second, what is the moral nature of (...) a machine vis-a-vis a human: can a machine be a true moral agent, capable of real responsibility, possessed of rights and duties? If so, might a machine be a better moral agent than a human? (shrink)

In this chapter, I examine how Dewey circumnavigated debates between empiricists and a priorists by showing that active bodies can perform integrative operations traditionally attributed to “inner” mechanisms, and how he thereby realized developments at which the artificial intelligence, robotics and cognitive science communities only later arrived. Some of his ideas about experience being constituted through skills actively deployed in cultural settings were inspired by ancient Greek sources. Thus in some of his more radical moments, Dewey refined rather than invented (...) the wheel, and I suggest that prominent embodiment figures have done the same, Dewey having anticipated them, particularly Noë and his version of enactivism. I urge that cognitive science may progress into relatively unexplored territory by traveling Dewey’s historically sensitive path. (shrink)

This article reports on a newly discovered bug in the original simulation argument. Two different ways of patching the argument are proposed, each of which preserves the original conclusion.

In this paper I put forward a reconstruction of the evolution of certain explanatory hypotheses on the neural basis of association and learning that are the premises of connectionism in the cybernetic age and of present-day connectionism. The main point of my reconstruction is based on two little-known case studies. The first is the project, published in 1913, of a hydraulic machine through which its author believed it was possible to simulate certain essential elements of the plasticity of nervous connections. (...) The author, S. Bent Russell, was an engineer deeply influenced by the neurological hypotheses on nervous conduction of Herbert Spencer, Max Meyer and Edward L. Thorndike. The second is the project, published in 1929, of an electromechanical machine in which the author, the psychologist J.M. Stephens, believed it was possible to embody Thorndike's law of effect. Thus both Bent Russell and Stephens referred to the principles of learning that Thorndike defined as connectionist . Their attempt was that of simulating by machines at least certain simple aspects of inhibition, association and habit formation that are typical of living organisms. I propose to situate their projects within the frame of thediscovery of a simulative (modelling) methodology which I believe might be considered an important topic of the Culture of the Artificial . Certain more recent steps toward such a methodology made by both connectionism of the 1950s and present-day connectionism are briefly pointed out in the paper. (shrink)

So-called ‘reified temporal logics’ were introduced by researchers in Artificial Intelligence (AI) in the early 1980s, and gave rise to a long-running series of debates concerning the proper way to represent states, events, causation, action, and other notions identified as crucial to the knowledge representation needs of AI. These debates never resulted in a definitive resolution of the issues under discussion, and indeed continue to produce aftershocks to the present day; none the less, we are now sufficiently far removed in (...) time from their heyday for it to be a worthwhile exercise to stand back and review them as a connected piece of history. (shrink)