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)

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)

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)

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)

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)

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)

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)

Contre toute attente is the manifesto of a thought that refuses passivity and can only endlessly seek the "weak force" of hope for a different, spectral, melancholy future. This book brings together several works around Gerard Bensussan which retrace the paths of his philosophical reflection.

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)

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)

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)

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)

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 (...) class='Hi'>weak AI and to explain a notion of representation common to human and artificial agents. (shrink)

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 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.

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)

Racial, Gender, and Intersectional Biases in AI / -/- Dominant View of Intersectional Fairness in the AI Literature / -/- Three Fundamental Problems with the Dominant View / 1. Overemphasis on Intersections of Attributes / 2. Dilemma between Infinite Regress and Fairness Gerrymandering / 3. Narrow Understanding of Fairness as Parity / -/- Rethinking AI Fairness: from Weak to Strong Fairness.

A growing number of studies on fairness in artificial intelligence (AI) use the notion of intersectionality to measure AI fairness. Most of these studies take intersectional fairness to be a matter of statistical parity among intersectional subgroups: an AI algorithm is “intersectionally fair” if the probability of the outcome is roughly the same across all subgroups defined by different combinations of the protected attributes. This paper identifies and examines three fundamental problems with this dominant interpretation of intersectional fairness in AI. (...) First, the dominant approach is so preoccupied with the intersection of attributes/categories (e.g., race, gender) that it fails to address the intersection of oppression (e.g., racism, sexism), which is more central to intersectionality as a critical framework. Second, the dominant approach faces a dilemma between infinite regress and fairness gerrymandering: it either keeps splitting groups into smaller subgroups or arbitrarily selects protected groups. Lastly, the dominant view fails to capture what it really means for AI algorithms to be fair, in terms of both distributive and non-distributive fairness. I distinguish a strong sense of AI fairness from a weak sense that is prevalent in the literature, and conclude by envisioning paths towards strong intersectional fairness in AI. (shrink)

Over the past decades, due to the course towards digitalization of all areas of life, interest in modeling and creating intelligent systems has increased significantly. However, there are now a stagnation in the industry, a lack of attention to analog and bionic approaches as alternatives to digital, numerous speculations on “neuro” issues for commercial and other purposes, and an increase in social and environmental risks. The article provides an overview of the development of artificial intelligence (AI) conceptions toward increasing the (...) human likeness of machines: from the key ideas of A. Turing and J. von Neumann, who initiated the digitalization of society, to discussions about the definition of AI and the emergence of conceptions of strong and weak AI. Special attention is paid to the approach of A. Sloman, to ideas about the architecture and design of complex artificial systems are considered, which make it possible to “emotionally” expand the idea of weak/strong AI. In the article's section on the necessity and possibility of incorporating emotions into the architecture of AI, the authors reveal the goals and methodological limitations for creating an emotional artificial agent. In addition, the article briefly presents the main principles of the authors' architectural approach to the creation of emotional intellectual systems on the example of the cognitive-affective model of architecture, which allow modeling the impact of emotions on the cognitive processes involved in decision-making processes. The described architectural approach to modeling intelligent systems can be used as a conceptual basis for discussing and formulating a strategy for the development of neurocomputing, philosophy of artificial intelligence, and experimental philosophy, for developing innovative research programs, formulating and solving theoretical and methodological problems. (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.

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)

Pietroski’s model of semantic composition is introduced and compared to the standard type hierarchy. Particular focus is then given to Pietroski’s account of quantifi cation. The question is raised of how the model might account for the weak/strong distinction in natural language quantifi cation. A number of options are addressed and one proposal is tentatively recommended.

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)

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous (...) and precise fashion. But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (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)

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)

This paper develops an account of the meaning of `ought', and the distinction between weak necessity modals (`ought', `should') and strong necessity modals (`must', `have to'). I argue that there is nothing specially ``strong'' about strong necessity modals per se: uses of `Must p' predicate the (deontic/epistemic/etc.) necessity of the prejacent p of the actual world (evaluation world). The apparent ``weakness'' of weak necessity modals derives from their bracketing whether the necessity of the prejacent is (...) verified in the actual world. `Ought p' can be accepted without needing to settle that the relevant considerations (norms, expectations, etc.) that actually apply verify the necessity of p. I call the basic account a modal-past approach to the weak/strong necessity modal distinction (for reasons that become evident). Several ways of implementing the approach in the formal semantics/pragmatics are critically examined. The account systematizes a wide range of linguistic phenomena: it generalizes across flavors of modality; it elucidates a special role that weak necessity modals play in discourse and planning; it captures contrasting logical, expressive, and illocutionary properties of weak and strong necessity modals; and it sheds light on how a notion of `ought' is often expressed in other languages. These phenomena have resisted systematic explanation. In closing I briefly consider how linguistic inquiry into differences among necessity modals may improve theorizing on broader philosophical issues. (shrink)

The term ‘emergence’ often causes confusion in science and philosophy, as it is used to express at least two quite different concepts. We can label these concepts _strong_ _emergence_ and _weak emergence_. Both of these concepts are important, but it is vital to keep them separate.

This chapter focuses on the issue of methodological usefulness of a strong versus weak adaptationist position in attempting to gain significant insight and to make scientifically important advances and discoveries in human cognition. Strong adaptationism holds that complex design is best explained by task-specific adaptations to particular ancestral environments; whereas weak adaptationism claims that we should not assume that complex design is the result of such narrowly determined task- or niche-specific evolutionary pressures in the absence of (...) substantial corroborating evidence. It argues that in cases of certain domain-specific cognitive competencies strong adaptationism has proven useful but not necessary to recent progress in the field. In other cases, a weak adaptationist strategy has been arguably most productive in advancing scientific understanding, without precluding that the structures uncovered by other means are actually adaptations. (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)