The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

It is proved that the variety of relevant disjunction lattices has the finite embeddability property. It follows that Avron's relevance logic RMImin has a strong form of the finite model property, so it has a solvable deducibility problem. This strengthens Avron's result that RMImin is decidable.

This study investigates the mechanism of how sensory experiences influence visitors’ digital engagement with a destination through establishing a strong bond and identification between a destination and tourist utilizing a two-step process. First, visitors’ sensory experiences in a destination are identified through a content analysis of online review comments posted by visitors. Afterward, the effects of those sensory experiences on visitors’ digital engagement through destination dependence and identification with that destination are examined. Findings suggest that sensory experiences are critical (...) antecedents of visitors’ bond and identification with a destination. Visitors’ positive destination-related sensory experiences increase their dependence on and identification with the destination, and this dependence and identification positively influence their digital engagement behavior on social media. (shrink)

Civics and Moral Educationwas implemented as a new moral education programme in Singapore schools in 1992. This paper argues that the underlying theme is that of citizenship training and that new measures are under way to strengthen the capacity of the school system to transmit national values for economic and political socialisation. The motives and motivation for retaining a formal moral education programme have remained strong. A discussion of the structure and content of key modules in Civics and Moral (...) Education shows how curriculum writers have attempted to integrate separate strands of civics and moral education from earlier programmes and present them in a less divisive manner. The paper also assesses the impact of the written civics and moral education programme on teachers and pupils, and the way in which strategies have been used by curriculum agencies to overcome possible obstacles at the curriculum implementation phase. (shrink)

This is a proposal for the re-activation of the essentially stillborn automatic car project for which the cart was originally obtained, and presents a process through which this activation could be accomplished painlessly. The project would be financed from the lab's operating grant, and would interact strongly with, while being independent of, any Mars rover research initiated by Lynn Quam. Since I seem to be the only one, apart from John McCarthy, with an active interest in this aspect of things, (...) the following is written entirely from my point of view. (shrink)

Searle (1980) constructed the Chinese Room (CR) to argue against what he called \Strong AI": the claim that a computer can understand by virtue of running a program of the right sort. Margaret Boden (1990), in giving the English Reply to the Chinese Room argument, has pointed out that there isunderstanding in the Chinese Room: the understanding required to recognize the symbols, the understanding of English required to read the rulebook, etc. I elaborate on and defend this response to (...) Searle. In particular, I use the insight of the English Reply to contend that Searle's Chinese Room cannot argue against what I call the claim of \Weak Strong AI": there are some cases of understanding that a computer can achieve solely by virtue of that computer running a program. I refute several objections to my defense of the Weak Strong AI thesis. (shrink)

This paper defends Searle against the misconstrual of a key claim of “Minds, Brains, and Programs” and goes on to explain why an attempt to turn the tables by using the Chinese Room to argue for intentionality in computers fails.

This paper defends Searle against the misconstrual of a key claim of “Minds, Brains, and Programs” and goes on to explain why an attempt to turn the tables by using the Chinese Room to argue for intentionality in computers fails.

John Searle's attack on the Strong AI thesis, and the published replies, are all based on a failure to distinguish two interpretations of that thesis, a strong one, which claims that the mere occurrence of certain process patterns will suffice for the occurrence of mental states, and a weak one which requires that the processes be produced in the right sort of way. Searle attacks strong strong AI, while most of his opponents defend weak strong (...) AI. This paper explores some of Searle's concepts and shows that there are interestingly different versions of the 'Strong AI' thesis, connected with different kinds of reliability of mechanisms and programs. (shrink)

Summary The controversy about the strong AI-thesis was recently revived by two interrelated contributions stemming from J. R. Searle on the one hand and from P. M. and P. S. Churchland on the other hand. It is shown that the strong AI-thesis cannot be defended in the formulation used by the three authors. It violates some well accepted criterions of scientific argumentation, especially the rejection of essentialistic definitions. Moreover, Searle's ‘proof’ is not conclusive. Though it may be reconstructed (...) in a conclusive manner, the modified proof is trivial. Beyond that, the most interesting aspect is formulated as an axiom that is not justified either. Therefore Searle's criticism of strong AI-thesis fails to be a convincing proof — it can be reduced to an unjustified presupposition. (shrink)

인공지능을 논의할 때 사람들은 흔히 ‘약한weak’ 인공지능과 ‘강한 strong’ 인공지능의 구별을 사용하고 있다. 이 구별은 인공지능들을 서로 구별할때만 흔히 사용될 뿐 아니라, 인공지능을 인간과 구별하는 데에서도 사용된다. 이 점은 인공지능에 대한 세 가지 유형의 관점에서 살펴볼 수 있다. 첫째는 인간의 창의적인 마음과 인공지능을 구별하는 이론이며, 둘째는 인간의 포괄적인 능력을 강한 지능의 기준으로 삼는 관점이며, 셋째는 인간보다 우월한 종을 강한 인공지능의 기준과 목표로 삼는 관점이다.BR 본 연구는 그 관점들이 전제하는 명제나 주장의 적절성 및 모호성을 살펴볼 것이다. 그러나 본 연구는 다른 (...) 한편으로 그들 모두에게 공통된 전제가 두 가지 있다는 것을 분석할 것이다. 첫째 전제는 종 차원에서의 인간과 기계의 ‘대립의 가설’이라고 부를 수 있는 이분법이다. 둘째 전제는 강함과 약함의 위계질서이다. 위의 세 가지 유형이 개별적으로 설정하거나 전제하는 내용 가운데 핵심은 단순히 이 위계질서가 아닐 터이지만, 강함과 약함의 기준에서 인간지능과 인공지능을 구별하는 과정에서 이 위계질서가 과도하게 일반화되었다고 할 수 있다. 더욱이 이 두 전제가 강한 지능과 약한 지능을 구별하는 과정에서 결합되면서, 서로를 강화시켜주는 일이 일어난다. (shrink)

John Searle has argued that the aim of strong AI of creating a thinking computer is misguided. Searle’s Chinese Room Argument purports to show that syntax does not suffice for semantics and that computer programs as such must fail to have intrinsic intentionality. But we are not mainly interested in the program itself but rather the implementation of the program in some material. It does not follow by necessity from the fact that computer programs are defined syntactically that the (...) implementation of them cannot suffice for semantics. Perhaps our world is a world in which any implementation of the right computer program will create a system with intrinsic intentionality, in which case Searle’s Chinese Room Scenario is empirically (nomically) impossible. But, indeed, perhaps our world is a world in which Searle’s Chinese Room Scenario is empirically (nomically) possible and that the silicon basis of modern day computers are one kind of material unsuited to give you intrinsic intentionality. The metaphysical question turns out to be a question of what kind of world we are in and I argue that in this respect we do not know our modal address. The Modal Address Argument does not ensure that strong AI will succeed, but it shows that Searle’s challenge on the research program of strong AI fails in its objectives. (shrink)

This paper offers a "tu quoque" defense of strong AI, based on the argument that phenomena of self-consciousness and intentionality are nothing but the "negative space" drawn around the concrete phenomena of brain states and causally connected utterances and objects. Any machine that was capable of concretely implementing the positive phenomena would automatically inherit the negative space around these that we call self-consciousness and intention. Because this paper was written for a literary audience, some examples from Greek tragedy, noir (...) fiction, science fiction and Dada are deployed to illustrate the view. (shrink)

This paper argues against John Searle in defense of the potential for computers to understand language (“Strong AI”) by showing that semantic meaning is itself a second-order system of rules that connects symbols and syntax with extralinguistic facts. Searle’s Chinese Room Argument is contested on theoretical and practical grounds by identifying two problems in the thought experiment, and evidence about “machine learning” is used to demonstrate that computers are already capable of learning to form true observation sentences in the (...) same way humans do. Finally, sarcasm is used as an example to extend the argument to more complex uses of language. (shrink)

John Searle has argued that the aim of strong AI to create a thinking computer is misguided. Searle's "Chinese Room Argument" purports to show that syntax does not suffice for semantics and that computer programs as such must fail to have intrinsic intentionality But we are not mainly interested in the program itself, but rather the implementation of the program in some material. It does not follow by necessity from the fact that computer programs are defined syntactically that the (...) implementation of them cannot suffice for semantics. Perhaps our world is a world in which any implementation of the right computer program will create a system with intrinsic intentionality, in which case Searle's "Chinese Room Scenario" is empirically impossible. But perhaps our world is a world in which Searle's "Chinese Room Scenario" is empirically possible, and the silicon basis of modern-day computers is one kind of material unsuited to give you intrinsic intentionality. The metaphysical question turns out to be a question of what kind of world we are in, and I argue that in this respect we do not know our model address. The "Model Address Argument" does not ensure that strong AI will succeed, but it shows that Searle's challenge to the research program of strong AI fails in its objectives. (shrink)

This paper argues against John Searle in defense of the potential for computers to understand language by showing that semantic meaning is itself a second-order system of rules that connects symbols and syntax with extralinguistic facts. Searle’s Chinese Room Argument is contested on theoretical and practical grounds by identifying two problems in the thought experiment, and evidence about “machine learning” is used to demonstrate that computers are already capable of learning to form true observation sentences in the same way humans (...) do. Finally, sarcasm is used as an example to extend the argument to more complex uses of language. (shrink)

Discussion of Searle's case against strong AI has usually focused upon his Chinese Room thought-experiment. In this paper, however, I expound and then try to refute what I call his abstract argument against strong AI, an argument which turns upon quite general considerations concerning programs, syntax, and semantics, and which seems not to depend on intuitions about the Chinese Room. I claim that this argument fails, since it assumes one particular account of what a program is. I suggest (...) an alternative account which, however, cannot play a role in a Searle-type argument, and argue that Searle gives no good reason for favoring his account, which allows the abstract argument to work, over the alternative, which doesn't. This response to Searle's abstract argument also, incidentally, enables the Robot Reply to the Chinese Room to defend itself against objections Searle makes to it. (shrink)

John Searle's attack on the Strong AI thesis, and the published replies, are all based on a failure to distinguish two interpretations of that thesis, a strong one, which claims that the mere occurrence of certain process patterns will suffice for the occurrence of mental states, and a weak one which requires that the processes be produced in the right sort of way. Searle attacks strong strong AI, while most of his opponents defend weak strong (...) AI. This paper explores some of Searle's concepts and shows that there are interestingly different versions of the 'Strong AI' thesis, connected with different kinds of reliability of mechanisms and programs. (shrink)

The viewpoint that consciousness, including feeling, could be fully expressed by a computational device is known as strong artificial intelligence or strong AI. Here I offer a defense of strong AI based on machine-state functionalism at the quantum level, or quantum-state functionalism. I consider arguments against strong AI, then summarize some counterarguments I find compelling, including Torkel Franzén’s work which challenges Roger Penrose’s claim, based on Gödel incompleteness, that mathematicians have nonalgorithmic levels of “certainty.” Some consequences (...) of strong AI are then considered. A resolution is offered of some problems including John Searle’s Chinese Room problem and the problem of consciousness propagation under isomorphism. (shrink)

More intellectual modesty, but also conceptual clarity is urgently needed in AI, perhaps more than in many other disciplines. AI research has been coined by hypes and hubris since its early beginnings in the 1950s. For instance, the Nobel laureate Herbert Simon predicted after his participation in the Dartmouth workshop that “machines will be capable, within 20 years, of doing any work that a man can do”. And expectations are in some circles still high to overblown today. This paper addresses (...) the demand for conceptual clarity and introduces precise definitions of “strong AI”, “superintelligence”, the “technological singularity”, and “artificial general intelligence” which ground in the work by the computer scientist Judea Pearl and the psychologist Howard Gardner. These clarifications allow us to embed famous arguments from the philosophy of AI in a more analytic context. (shrink)

I argue that John Searle's (1980) influential Chinese room argument (CRA) against computationalism and strong AI survives existing objections, including Block's (1998) internalized systems reply, Fodor's (1991b) deviant causal chain reply, and Hauser's (1997) unconscious content reply. However, a new ``essentialist'' reply I construct shows that the CRA as presented by Searle is an unsound argument that relies on a question-begging appeal to intuition. My diagnosis of the CRA relies on an interpretation of computationalism as a scientific theory about (...) the essential nature of intentional content; such theories often yield non-intuitive results in non-standard cases, and so cannot be judged by such intuitions. However, I further argue that the CRA can be transformed into a potentially valid argument against computationalism simply by reinterpreting it as an indeterminacy argument that shows that computationalism cannot explain the ordinary distinction between semantic content and sheer syntactic manipulation, and thus cannot be an adequate account of content. This conclusion admittedly rests on the arguable but plausible assumption that thought content is interestingly determinate. I conclude that the viability of computationalism and strong AI depends on their addressing the indeterminacy objection, but that it is currently unclear how this objection can be successfully addressed. (shrink)

Is it OK to lie to Siri? Is it bad to mistreat a robot for our own pleasure? Under what condition should we grant a moral status to an artificial intelligence (AI) system? This paper looks at different arguments for granting moral status to an AI system: the idea of indirect duties, the relational argument, the argument from intelligence, the arguments from life and information, and the argument from sentience. In each but the last case, we find unresolved issues with (...) the particular argument, which leads us to move to a different one. We leave the idea of indirect duties aside since these duties do not imply considering an AI system for its own sake. The paper rejects the relational argument and the argument from intelligence. The argument from life may lead us to grant a moral status to an AI system, but only in a weak sense. Sentience, by contrast, is a strong argument for the moral status of an AI system—based, among other things, on the Aristotelian principle of equality: that same cases should be treated in the same way. The paper points out, however, that no AI system is sentient given the current level of technological development. (shrink)

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

Is it OK to lie to Siri? Is it bad to mistreat a robot for our own pleasure? Under what condition should we grant a moral status to an artificial intelligence system? This paper looks at different arguments for granting moral status to an AI system: the idea of indirect duties, the relational argument, the argument from intelligence, the arguments from life and information, and the argument from sentience. In each but the last case, we find unresolved issues with the (...) particular argument, which leads us to move to a different one. We leave the idea of indirect duties aside since these duties do not imply considering an AI system for its own sake. The paper rejects the relational argument and the argument from intelligence. The argument from life may lead us to grant a moral status to an AI system, but only in a weak sense. Sentience, by contrast, is a strong argument for the moral status of an AI system—based, among other things, on the Aristotelian principle of equality: that same cases should be treated in the same way. The paper points out, however, that no AI system is sentient given the current level of technological development. (shrink)

Over the last three decades, the rise of embodied cognition (EC) articulated in various schools (or versions) of embodied, embedded, extended and enacted cognition (Gallagher’s 4E) has offered AI a way out of traditional computationalism—an approach (or an understanding) loosely referred to as embodied AI. This view has split into various branches ranging from a weak form on the brink of functionalism (loosely represented by Clarks’ parity principle) to a strong form (often corresponding to autopoietic-friendly enactivism) suggesting that body−world (...) interactions constitute cognition. From an ontological perspective, however, constitution is a problematic notion with no obvious empirical or technical advantages. This paper discusses the ontological issues of these two approaches in regard to embodied AI and its ontological commitments: circularity, epiphenomenalism, mentalism, and disguised dualism. The paper also outlines an even more radical approach that may offer some ontological advantages. The new approach, called the mind-object identity, is then briefly compared with sensorimotor direct realism and with the embodied identity theory. (shrink)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

As the artificial intelligence (AI) ethics field is currently working towards its operationalisation, ethics review as carried out by research ethics committees (RECs) constitutes a powerful, but so far underdeveloped, framework to make AI ethics effective in practice at the research level. This article contributes to the elaboration of research ethics frameworks for research projects developing and/or using AI. It highlights that these frameworks are still in their infancy and in need of a structure and criteria to ensure AI research (...) projects advance in a way that respects norms and principles. This article proposes to draw from the European Union’s AI Act currently in development to shape these frameworks. Although, in the current form of the draft (as of August 2023), the obligations of the AI Act do not apply to scientific research, it is most likely that they will still have a strong impact on AI research considering the need to anticipate market placement or to test new tools in real world conditions. This article investigates what the risk-based approach in the AI Act implies for research ethics and highlights some AI Act obligations of particular value to implement for ethics review processes. (shrink)

Machine learning-based AI algorithms lack transparency. In this article, I offer an interpretation of AI’s explainability problem and highlight its ethical saliency. I try to make the case for the legal enforcement of a strong explainability requirement: human organizations which decide to automate decision-making should be legally obliged to demonstrate the capacity to explain and justify the algorithmic decisions that have an impact on the wellbeing, rights, and opportunities of those affected by the decisions. This legal duty can be (...) derived from the demands of Rawlsian public reason. In the second part of the paper, I try to show that the argument from the limitations of human cognition fails to get AI off the hook of public reason. Against a growing trend in AI ethics, my main argument is that the analogy between human minds and artificial neural networks fails because it suffers from an atomistic bias which makes it blind to the social and institutional dimension of human reasoning processes. I suggest that developing interpretive AI algorithms is not the only possible answer to the explainability problem; social and institutional answers are also available and in many cases more trustworthy than techno-scientific ones. (shrink)

People’s comfort with and acceptability of artificial intelligence (AI) instantiations is a topic that has received little systematic study. This is surprising given the topic’s relevance to the design, deployment and even regulation of AI systems. To help fill in our knowledge base, we conducted mixed-methods analysis based on a survey of a representative sample of the US population (_N_ = 2254). Results show that there are two distinct social dimensions to comfort with AI: as a peer and as a (...) superior. For both dimensions, general and technological efficacy traits—locus of control, communication apprehension, robot phobia, and perceived technology competence—are strongly associated with acceptance of AI in various roles. Female and older respondents also were less comfortable with the idea of AI agents in various roles. A qualitative analysis of comments collected from respondents complemented our statistical approach. We conclude by exploring the implications of our research for AI acceptability in society. (shrink)

AI’s new ability to create artworks is seen as a major challenge to today’s understanding of art. There is a strong tension between people who predict that AI will replace artists and critics who claim that AI art will never be art. Furthermore, recent studies have documented a negative bias towards AI art. This paper provides a philosophical explanation for this negative bias, based on our shared understanding of the ontological differences between objects. We argue that our perception of (...) art depends on our understanding of the context of its creation: human-made art is experienced as an interplay between the artist and nature. In AI-generated art, this interplay is either absent or minimised. We conclude that the displacement of the “human factor” in art will not lead to an evolution of art, but to the end of art. (shrink)

It has been claimed that a great deal of AI research is an attempt to discover the empirical laws describing a new type of entity in the world—the artificial computing system. I call this enterprise 'medium AI', since it is in some respects stronger than Searle's 'weak AI', and in other respects weaker than 'strong AI'. Bruce Buchanan, among others, conceives of medium AI as an empirical science entirely on a par with psychology or chemistry. I argue that medium (...) AI is not an empirical science at all. Depending on how artificial computing systems are categorized, it is either an a priori science like mathematics, or a branch of engineering. (shrink)

One of the primary, if not most critical, difficulties in the design and implementation of autonomous systems is the black-boxed nature of the decision-making structures and logical pathways. How human values are embodied and actualised in situ may ultimately prove to be harmful if not outright recalcitrant. For this reason, the values of stakeholders become of particular significance given the risks posed by opaque structures of intelligent agents (IAs). This paper explores how decision matrix algorithms, via the belief-desire-intention model for (...) autonomous vehicles, can be designed to minimize the risks of opaque architectures. Primarily through an explicit orientation towards designing for the values of explainability and verifiability. In doing so, this research adopts the Value Sensitive Design (VSD) approach as a principled framework for the incorporation of such values within design. VSD is recognized as a potential starting point that offers a systematic way for engineering teams to formally incorporate existing technical solutions within ethical design, while simultaneously remaining pliable to emerging issues and needs. It is concluded that the VSD methodology offers at least a strong enough foundation from which designers can begin to anticipate design needs and formulate salient design flows that can be adapted to the changing ethical landscapes required for utilisation in autonomous vehicles. (shrink)

The use of artificial intelligence (AI) to aid legal decision making has become prominent. This paper investigates the use of AI in a critical issue in employment law, the determination of a worker’s status—employee vs. independent contractor—in two common law countries (the U.S. and Canada). This legal question has been a contentious labor issue insofar as independent contractors are not eligible for the same benefits as employees. It has become an important societal issue due to the ubiquity of the gig (...) economy and the recent disruptions in employment arrangements. To address this problem, we collected, annotated, and structured the data for all Canadian and Californian court cases related to this legal question between 2002 and 2021, resulting in 538 Canadian cases and 217 U.S. cases. In contrast to legal literature focusing on complex and correlated characteristics of the employment relationship, our statistical analyses of the data show very strong correlations between the worker’s status and a small subset of quantifiable characteristics of the employment relationship. In fact, despite the variety of situations in the case law, we show that simple, off-the-shelf AI models classify the cases with an out-of-sample accuracy of more than 90%. Interestingly, the analysis of misclassified cases reveals consistent misclassification patterns by most algorithms. Legal analyses of these cases led us to identify how equity is ensured by judges in ambiguous situations. Finally, our findings have practical implications for access to legal advice and justice. We deployed our AI model via the open-access platform, https://MyOpenCourt.org/, to help users answer employment legal questions. This platform has already assisted many Canadian users, and we hope it will help democratize access to legal advice to large crowds. (shrink)

In their joint paper entitled The Replication of the Hard Problem of Consciousness in AI and BIO-AI (Boltuc et al. Replication of the hard problem of conscious in AI and Bio- AI: An early conceptual framework 2008), Nicholas and Piotr Boltuc suggest that machines could be equipped with phenomenal consciousness, which is subjective consciousness that satisfies Chalmer’s hard problem (We will abbreviate the hard problem of consciousness as H-consciousness ). The claim is that if we knew the inner workings of (...) phenomenal consciousness and could understand its’ precise operation, we could instantiate such consciousness in a machine. This claim, called the extra-strong AI thesis, is an important claim because if true it would demystify the privileged access problem of first-person consciousness and cast it as an empirical problem of science and not a fundamental question of philosophy. A core assumption of the extra-strong AI thesis is that there is no logical argument that precludes the implementation of H-consciousness in an organic or in-organic machine provided we understand its algorithm. Another way of framing this conclusion is that there is nothing special about H-consciousness as compared to any other process. That is, in the same way that we do not preclude a machine from implementing photosynthesis, we also do not preclude a machine from implementing H-consciousness. While one may be more difficult in practice, it is a problem of science and engineering, and no longer a philosophical question. I propose that Boltuc’s conclusion, while plausible and convincing, comes at a very high price; the argument given for his conclusion does not exclude any conceivable process from machine implementation. In short, if we make some assumptions about the equivalence of a rough notion of algorithm and then tie this to human understanding, all logical preconditions vanish and the argument grants that any process can be implemented in a machine. The purpose of this paper is to comment on the argument for his conclusion and offer additional properties of H-consciousness that can be used to make the conclusion falsifiable through scientific investigation rather than relying on the limits of human understanding. (shrink)

Penrose [40] has discussed a new point of view concerning the nature of physics that might underline conscious thought processes. He has argued that it might be the case that some physical laws are not computable, i.e. they cannot be properly simulated by computer; such laws can most probably arise on the “no-man’s-land” between classical and quantum physics. Furthermore, conscious thinking is a non-algorithmic activity. He is opposing both strong AI , and Searle’s [47] contrary viewpoint mathematical “laws”).

Since Chinese scholars are playing an increasingly important role in shaping the national landscape of discussion on AI ethics, understanding their ethical concerns and preferred solutions is essential for global cooperation on governance of AI. This article, therefore, provides the first elaborated analysis on the discourse on AI ethics in Chinese academia, via a systematic literature review. This article has three main objectives. to identify the most discussed ethical issues of AI in Chinese academia and those being left out ; (...) to analyze the solutions proposed and preferred by Chinese scholars ; and to map out whose voices are dominating and whose are in the marginal. Findings suggest that in terms of short-term implications, Chinese scholars’ concerns over AI resemble predominantly the content of international ethical guidelines. Yet in terms of long-term implications, there are some significant differences needed to be further addressed in a cultural context. Further, among a wide range of solution proposals, Chinese scholars seem to prefer strong-binding regulations to those weak ethical guidelines. In addition, this article also found that the Chinese academic discourse was dominated by male scholars and those who are from elite universities, which arguably is not a unique phenomenon in China. (shrink)

The modern program of operationalizing the mind, from Descartes to Kant, in the form of the externalization of human mind functions in logic and calculations, and its continuation in the program of formalization from the middle of the 19th century with Boole, Peirce and Turing, have led to the form of rationality that has become machine rationality: the digital computer as a logical-mathematical machine and algorithms as machine-rational interpretations of human thinking in the form of problem solving and decision making. (...) The modern program of the operationalization of the mind can be understood as the first program of an "artificial intelligence" on paper. With the advent of digital computers, it was transformed into the programs of "strong" and "weak" AI. As early as 2015, machine learning methods surpassed human competencies in image recognition and other fields, such turning AI increasingly into "superhuman AI." Thus, the philosophical question is, how to deal with this development as humans? (shrink)

Our contribution aims at individuating a valid philosophical strategy for a fruitful confrontation between human and artificial representation. The ground for this theoretical option resides in the necessity to find a solution that overcomes, on the one side, strong AI (i.e. Haugeland) and, on the other side, the view that rules out AI as explanation of human capacities (i.e. Dreyfus). We try to argue for Analytic Pragmatism (AP) as a valid strategy to present arguments for a form of weak (...) AI and to explain a notion of representation common to human and artificial agents. (shrink)

The prelims comprise: Historical Background The Turing Test Physical Machines Symbol Systems The Chinese Room Weak AI Strong AI Folk Psychology Eliminative Materialism Processing Syntax Semantic Engines The Language of Thought Formal Systems Mental Propensities The Frame Problem Minds and Brains Semiotic Systems Critical Differences The Hermeneutic Critique Conventions and Communication Other Minds Intelligent Machines.

This article examines argument structures and strategies in pro and con argumentation about the possibility of human-level artificial intelligence (AI) in the near term future. It examines renewed controversy about strong AI that originated in a prominent 1999 book and continued at major conferences and in periodicals, media commentary, and Web-based discussions through 2002. It will be argued that the book made use of implicit, anticipatory refutation to reverse prevailing value hierarchies related to AI. Drawing on Perelman and Olbrechts-Tyteca's (...) (1969) study of refutational argument, this study considers points of contact between opposing arguments that emerged in opposing loci, dissociations, and casuistic reasoning. In particular, it shows how perceptions of AI were reframed and rehabilitated through metaphorical language, reversal of the philosophical pair artificial/natural, appeals to the paradigm case, and use of the loci of quantity and essence. Furthermore, examining responses to the book in subsequent arguments indicates the topoi characteristic of the rhetoric of technology advocacy. (shrink)

Purpose The paper aims to analyze the content of the newly published National AI Ethics Guideline in Thailand. Thailand’s ongoing political struggles and transformation has made it a good case to see how a policy document such as a guideline in AI ethics becomes part of the transformations. Looking at how the two are interrelated will help illuminate the political and cultural dynamics of Thailand as well as how governance of ethics itself is conceptualized. Design/methodology/approach The author looks at the (...) history of how the National AI Ethics Guidelines came to be and interprets its content, situating the Guideline within the contemporary history of the country as well as comparing the Guideline with some of the leading existing guidelines. Findings It is found that the Guideline represents an ambivalent and paradoxical nature that characterizes Thailand’s attempt at modernization. On the one hand, there is a desire to join the ranks of the more advanced economies, but, on the other hand, there is also a strong desire to maintain its own traditional values. Thailand has not been successful in resolving this tension yet, and this lack of success is shown in the way that content of the AI Ethics Guideline is presented. Practical implications The findings of the paper could be useful for further attempts in drafting and revising AI ethics guidelines in the future. Originality/value The paper represents the first attempt, so far as the author is aware, to analyze the content of the Thai AI Ethics Guideline critically. (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)

On virtue ethical grounds, Barbro Fröding and Martin Peterson recently recommended that near-future AIs should be developed as ‘Friendly AI’. AI in social interaction with humans should be programmed such that they mimic aspects of human friendship. While it is a reasonable goal to implement AI systems interacting with humans as Friendly AI, I identify four issues that need to be addressed concerning Friendly AI with Fröding’s and Peterson’s understanding of Friendly AI as a starting point. In a first step, (...) I briefly recapitulate Fröding’s and Peterson’s arguments for Friendly AI. I then highlight some issues with Fröding’s and Peterson’s approach and line of reasoning and identify four problems related to the notion of Friendly AI, which all pertain to the role and need for humans’ moral development. These are that one should consider the moral tendencies and preferences of the humans interacting with a friendly AI, it needs to be considered whether the humans interacting with a Friendly AI are still developing their virtues and character traits, the indirect effects of replacing humans with Friendly AI should be considered with respect to the possibilities for humans to develop their moral virtues and that the question whether the AI is perceived as some form of Artificial General Intelligence cannot be neglected. In conclusion, I argue that all of these four problems are related to humans moral development and that this observation strongly emphasizes the role and need for humans moral development in correlation to the accelerating development of AI-systems. (shrink)