What does it truly mean to “understand”? The Chinese Room Argument asserts that AI, no matter how advanced, merely manipulates symbols without grasping meaning, while human cognition is uniquely capable of true understanding. But if human intelligence itself is built upon memorization, structured abstraction, and computational complexity, then is understanding anything more than an emergent property of hierarchical information processing? This paper argues that Searle’s framework rests on anthropocentric assumptions that fail to account for the variance in meaning (...) structures between human and artificial cognition. The claim that syntax alone cannot generate semantics relies on an outdated view of cognition, ignoring how meaning emerges differently across self-organizing systems. Furthermore, Searle’s demand that true understanding requires human-like intentionality is a category error, conflating distinct computational architectures with vastly different processing scales. By examining hierarchical abstraction, computational self-organization, and the mechanistic basis of understanding, this paper dismantles Searle’s framework and proposes that meaning is system-relative—not an exclusive product of human cognition, but a function of computational complexity. (shrink)

Searle’s Chinese Room Argument (CRA) has been the object of great interest in the philosophy of mind, artificial intelligence and cognitive science since its initial presentation in ‘Minds, Brains and Programs’ in 1980. It is by no means an overstatement to assert that it has been a main focus of attention for philosophers and computer scientists of many stripes. It is then especially interesting to note that relatively little has been said about the detailed logic of the argument, (...) whatever significance Searle intended CRA to have. The problem with the CRA is that it involves a very strong modal claim, the truth of which is both unproved and highly questionable. So it will be argued here that the CRA does not prove what it was intended to prove. (shrink)

Detractors of Searle’s Chinese Room Argument have arrived at a virtual consensus that the mental properties of the Man performing the computations stipulated by the argument are irrelevant to whether computational cognitive science is true. This paper challenges this virtual consensus to argue for the first of the two main theses of the persons reply, namely, that the mental properties of the Man are what matter. It does this by challenging many of the arguments and conceptions put forth (...) by the systems and logical replies to the Chinese Room, either reducing them to absurdity or showing how they lead, on the contrary, to conclusions the persons reply endorses. The paper bases its position on the Chinese Room Argument on additional philosophical considerations, the foundations of the theory of computation, and theoretical and experimental psychology. The paper purports to show how all these dimensions tend to support the proposed thesis of the persons reply. (shrink)

This paper is a follow-up of the first part of the persons reply to the Chinese Room Argument. The first part claims that the mental properties of the person appearing in that argument are what matter to whether computational cognitive science is true. This paper tries to discern what those mental properties are by applying a series of hypothetical psychological and strengthened Turing tests to the person, and argues that the results support the thesis that the Man performing (...) the computations characteristic of understanding Chinese actually understands Chinese. The supposition that the Man does not understand Chinese has gone virtually unquestioned in this foundational debate. The persons reply acknowledges the intuitive power behind that supposition, but knows that brute intuitions are not epistemically sacrosanct. Like many intuitions humans have had, and later deposed, this intuition does not withstand experimental scrutiny. The second part of the persons reply consequently holds that computational cognitive science is confirmed by the Chinese Room thought experiment. (shrink)

The ability to communicate in a specific language like Chinese typically indicates that the speaker understands the language. A counterexample to this belief is John Searle’s Chinese room experiment. It has been shown in this experiment that in certain circumstances we can communicate with a Chinese speaker without intuitively acknowledging that the Chinese language is understood in the conversation. In the present paper, we aim to present another counterexample showing that, in certain circumstances, we can (...) communicate using a specific language by converting its words to those of our natural language, and understand what is expressed using the language without understanding it. Using a specific conversion process introduced in the present paper, symbols and even sounds of a language like the Chinese language lose their original function and act as a tool to express one’s natural language. To better understand this, imagine the English language has another writing or phonological system in addition to its current writing system. (shrink)

Ion, one of the earliest of Plato’s dialogues, is also considered as one of the philosopher’s less important philosophical works. It is also viewed as one of the earliest texts on the philosophy of art that distinguishes between techne and artistic creation, with the latter representing a form of divine inspiration which occurs outside the awareness of its creator. Here I will offer an alternative interpretation that is based on the dissociation of the poet from his reciter. The latter, by (...) operating as a medium, functions similarly to the ignorant in Chinese but possessor and user of a perfect manual, in Searle’s Chinese room. Furthermore, I propose that art, in Plato’s view, may represent a form of creative achievement with significance that exceeds the sum of the specific disciplines it deals with. (shrink)

In this paper I submit that the “Chinese room” argument rests on the assumption that understanding a sentence necessarily implies being conscious of its content. However, this assumption can be challenged by showing that two notions of consciousness come into play, one to be found in AI, the other in Searle’s argument, and that the former is an essential condition for the notion used by Searle. If Searle discards the first, he not only has trouble explaining how we (...) can learn a language but finds the validity of his own argument in jeopardy. (shrink)

Does the Chinese Room Argument (CRA) apply to large language models (LLMs)? The thought experiment at the center of the CRA is tailored to Good Old-Fashioned Artificial Intelligence (GOFAI) systems. However, natural language processing has made significant progress, especially with the emergence of LLMs in recent years. LLMs differ from GOFAI systems in their design; they operate on vectors rather than symbols and do not follow a program but instead learn to map inputs to outputs. Consequently, some have (...) suggested that the CRA is no longer relevant in discussions surrounding artificial language understanding. Contrary to these authors, I argue that if the CRA successfully demonstrates that implementing a symbolic computation is not sufficient for language understanding, then it also shows that implementing an LLM is not sufficient for language understanding. At the core of my argument lies a thought experiment called “the flashcard sorter”. (shrink)

A criticism of Searle's Chinese Room thought experiment.

The Turing Test (TT), the Chinese Room Argument (CRA), and the Symbol Grounding Problem (SGP) are about the question “can machines think?” We propose to look at these approaches to Artificial Intelligence (AI) by showing that they all address the possibility for Artificial Agents (AAs) to generate meaningful information (meanings) as we humans do. The initial question about thinking machines is then reformulated into “can AAs generate meanings like humans do?” We correspondingly present the TT, the CRA and (...) the SGP as being about generation of human-like meanings. We model and address such possibility by using the Meaning Generator System (MGS) where a system submitted to an internal constraint generates a meaning in order to satisfy the constraint. The system approach of the MGS allows comparing meaning generations in animals, humans and AAs. The comparison shows that in order to have AAs capable of generating human-like meanings, we need the AAs to carry human constraints. And transferring human constraints to AAs raises concerns coming from the unknown natures of life and human mind which are at the root of human constraints. Implications for the TT, the CRA and the SGP are highlighted. It is shown that designing AAs capable of thinking like humans needs an understanding about the natures of life and human mind that we do not have today. Following an evolutionary approach, we propose as a first entry point an investigation about the possibility for extending a “stay alive” constraint into AAs. Ethical concerns are raised from the relations between human constraints and human values. Continuations are proposed. (This paper is an extended version of the proceedings of an AISB/IACAP 2012 presentation). (shrink)

We argue that a command and control system can undermine a commander’s moral agency if it causes him/her to process information in a purely syntactic manner, or if it precludes him/her from ascertaining the truth of that information. Our case is based on the resemblance between a commander’s circumstances and the protagonist in Searle’s Chinese Room, together with a careful reading of Aristotle’s notions of ‘compulsory’ and ‘ignorance’. We further substantiate our case by considering the Vincennes Incident, when (...) the crew of a warship mistakenly shot down a civilian airliner. To support a combat commander’s moral agency, designers should strive for systems that help commanders and command teams to think and manipulate information at the level of meaning. ‘Down conversions’ of information from meaning to symbols must be adequately recovered by ‘up conversions’, and commanders must be able to check that their sensors are working and are being used correctly. Meanwhile ethicists should establish a mechanism that tracks the potential moral implications of choices in a system’s design and intended operation. Finally we highlight a gap in normative ethics, in that we have ways to deny moral agency, but not to affirm it. (shrink)

Ford’s Helen Keller Was Never in a Chinese Room claims that my argument in How Helen Keller Used Syntactic Semantics to Escape from a Chinese Room fails because Searle and I use the terms ‘syntax’ and ‘semantics’ differently, hence are at cross purposes. Ford has misunderstood me; this reply clarifies my theory.

A computer can come to understand natural language the same way Helen Keller did: by using “syntactic semantics”—a theory of how syntax can suffice for semantics, i.e., how semantics for natural language can be provided by means of computational symbol manipulation. This essay considers real-life approximations of Chinese Rooms, focusing on Helen Keller’s experiences growing up deaf and blind, locked in a sort of Chinese Room yet learning how to communicate with the outside world. Using the SNePS (...) computational knowledge-representation system, the essay analyzes Keller’s belief that learning that “everything has a name” was the key to her success, enabling her to “partition” her mental concepts into mental representations of: words, objects, and the naming relations between them. It next looks at Herbert Terrace’s theory of naming, which is akin to Keller’s, and which only humans are supposed to be capable of. The essay suggests that computers at least, and perhaps non-human primates, are also capable of this kind of naming. (shrink)

Viewed in the light of the remarkable performance of ‘Watson’ - IBMs proprietary artificial intelligence computer system capable of answering questions posed in natural language - on the US general knowledge quiz show ‘Jeopardy’, we review two experiments on formal systems - one in the domain of quantum physics, the other involving a pictographic languaging game - whereby behaviour seemingly characteristic of domain understanding is generated by the mere mechanical application of simple rules. By re-examining both experiments in the context (...) of Searle’s Chinese Room Argument, we suggest their results merely endorse Searle’s core intuition: that ‘syntactical manipulation of symbols is not sufficient for semantics’. Although, pace Watson, some artificial intelligence practitioners have suggested that more complex, higher-level operations on formal symbols are required to instantiate understanding in computational systems, we show that even high-level calls to Google translate would not enable a computer qua ‘formal symbol processor’ to understand the language it processes. We thus conclude that even the most recent developments in ‘quantum linguistics’ will not enable computational systems to genuinely understand natural language. (shrink)

Viewed in the light of the remarkable performance of ‘Watson’ - IBMs proprietary artificial intelligence computer system capable of answering questions posed in natural language - on the US general knowledge quiz show ‘Jeopardy’, we review two experiments on formal systems - one in the domain of quantum physics, the other involving a pictographic languaging game - whereby behaviour seemingly characteristic of domain understanding is generated by the mere mechanical application of simple rules. By re-examining both experiments in the context (...) of Searle’s Chinese Room Argument, we suggest their results merely endorse Searle’s core intuition: that ‘syntactical manipulation of symbols is not sufficient for semantics’. Although, pace Watson, some artificial intelligence practitioners have suggested that more complex, higher-level operations on formal symbols are required to instantiate understanding in computational systems, we show that even high-level calls to Google translate would not enable a computer qua ‘formal symbol processor’ to understand the language it processes. We thus conclude that even the most recent developments in ‘quantum linguistics’ will not enable computational systems to genuinely understand natural language. (shrink)

Pattern recognition is represented as the limit, to which an infinite Turing process converges. A Turing machine, in which the bits are substituted with qubits, is introduced. That quantum Turing machine can recognize two complementary patterns in any data. That ability of universal pattern recognition is interpreted as an intellect featuring any quantum computer. The property is valid only within a quantum computer: To utilize it, the observer should be sited inside it. Being outside it, the observer would obtain quite (...) different result depending on the degree of the entanglement of the quantum computer and observer. All extraordinary properties of a quantum computer are due to involving a converging infinite computational process contenting necessarily both a continuous advancing calculation and a leap to the limit. Three types of quantum computation can be distinguished according to whether the series is a finite one, an infinite rational or irrational number. (shrink)

The purpose of this paper is to show that understanding AI hallucination requires an interdisciplinary approach that combines insights from epistemology and cognitive science to address the nature of AI-generated knowledge, with a terminological worry that concepts we often use might carry unnecessary presuppositions. Along with terminological issues, it is demonstrated that AI systems, comparable to human cognition, are susceptible to errors in judgement and reasoning, and proposes that epistemological frameworks, such as reliabilism, can be similarly applied to enhance the (...) trustworthiness of AI outputs. This exploration seeks to deepen our understanding of the possibility of AI cognition and its implications for the broader philosophical questions of knowledge and intelligence. (shrink)

The Chinese Room Argument purports to show that‘ syntax is not sufficient for semantics’; an argument which led John Searle to conclude that ‘programs are not minds’ and hence that no computational device can ever exhibit true understanding. Yet, although this controversial argument has received a series of criticisms, it has withstood all attempts at decisive rebuttal so far. One of the classical responses to CRA has been based on equipping a purely computational device with a physical robot (...) body. This response, although partially addressed in one of Searle’s original contra arguments - the ‘robot reply’ - more recently gained friction with the development of embodiment and enactivism1, two novel approaches to cognitive science that have been exciting roboticists and philosophers alike. Furthermore, recent technological advances - blending biological beings with computational systems - have started to be developed which superficially suggest that mind may be instantiated in computing devices after all. This paper will argue that (a) embodiment alone does not provide any leverage for cognitive robotics wrt the CRA, when based on a weak form of embodiment and that (b) unless they take the body into account seriously, hybrid bio-computer devices will also share the fate of their disembodied or robotic predecessors in failing to escape from Searle’s Chinese room. (shrink)

Philosophical questions about minds and computation need to focus squarely on the mathematical theory of Turing machines (TM's). Surrogate TM's such as computers or formal systems lack abilities that make Turing machines promising candidates for possessors of minds. Computers are only universal Turing machines (UTM's)—a conspicuous but unrepresentative subclass of TM. Formal systems are only static TM's, which do not receive inputs from external sources. The theory of TM computation clearly exposes the failings of two prominent critiques, Searle's Chinese (...) room (1980) and arguments from Gödel's Incompleteness theorems (e.g., Lucas, 1961; Penrose, 1989), both of which fall short of addressing the complete TM model. Both UTM-computers and formal systems provide an unsound basis for debate. In particular, their special natures easily foster the misconception that computation entails intrinsically meaningless symbol manipulation. This common view is incorrect with respect to full-fledged TM's, which can process inputs non-formally, i.e., in a subjective and dynamically evolving fashion. To avoid a distorted understanding of the theory of computation, philosophical judgments and discussions should be grounded firmly upon the complete Turing machine model, the proper model for real computers. (shrink)

I make three points about Searle’s philosophical work on consciousness and intentionality. First, I comment on Searle’s presentation and paper “The Problems of Consciousness.” I show that one of Searle’s philosophical claims about the relation between consciousness and intentionality appears to conflict with a demand he makes on acceptable empirical theories of the brain. Second, I argue that closer attention to the difference between conceptual connections and empirical connections corrects and improves Searle’s response to the so-called “Logical Connections” argument, the (...) argument that claims that mental states cannot be causes, since they are conceptually connected with actions. Third, I give a formulation of his Chinese Room argument that avoids some tempting responses. (shrink)

Pancomputationalism is the view that everything is a computer. This, if true, poses some difficulties to the computational theory of cognition. In particular, the strongest version of it suggested by John Searle seems enough to trivialize computational cognitivists’ core idea on which our cognitive system is a computing system. The aim of this paper is to argue against Searle’s pancomputationalism. To achieve this, I will draw a line between realized computers and unrealized computers. Through this distinction, I expect that it (...) will become evident that Searle’s pancomputationalism should be understood in terms of unrealized computers, while the computational theory of cognition is concerned with realized computers. (shrink)

“Could a machine think?” asks John R. Searle in his paper Minds, Brains, and Programs. He answers that “only a machine could think1, and only very special kinds of machines, namely brains.”2 The subject of this paper is the analysis of the aforementioned question through presentation of the symbol manipulation approach to intelligence and Searle's well-known criticism to this approach, namely the Chinese room argument. The examination of these issues leads to the systems reply of the Chinese (...) room argument and tries to illustrate that Searle's response to the systems reply does not detract from the symbol manipulation approach. (shrink)

John Searle once said: "The Chinese room shows what we knew all along: syntax by itself is not sufficient for semantics. (Does anyone actually deny this point, I mean straight out? Is anyone actually willing to say, straight out, that they think that syntax, in the sense of formal symbols, is really the same as semantic content, in the sense of meanings, thought contents, understanding, etc.?)." I say: "Yes". Stuart C. Shapiro has said: "Does that make any sense? (...) Yes: Everything makes sense. The question is: What sense does it make?" This essay explores what sense it makes to say that syntax by itself is sufficient for semantics. (shrink)

A response to a recent critique by Cem Bozşahin of the theory of syntactic semantics as it applies to Helen Keller, and some applications of the theory to the philosophy of computer science.

For many years now, Harnad has argued that transduction is special among cognitive capacities -- special enough to block Searle's Chinese Room Argument. His arguments (as well as Searle's) have been important and useful, but not correct, it seems to me. Their arguments have provided the modern impetus for getting clear about computationalism and the nature of computing. This task has proven to be quite difficult. Which is simply to say that dealing with Harnad's arguments (as well as (...) Searle's) has been difficult. Turing, it turns out, only got us started. But Harnad's (and Searle's) arguments ultimately fail. Turing, it turns out, was on the right track. (shrink)

In the field of machine consciousness, it has been argued that in order to build human-like conscious machines, we must first have a computational model of qualia. To this end, some have proposed a framework that supports qualia in machines by implementing a model with three computational areas (i.e., the subconceptual, conceptual, and linguistic areas). These abstract mechanisms purportedly enable the assessment of artificial qualia. However, several critics of the machine consciousness project dispute this possibility. For instance, Searle, in his (...) Chinese room objection, argues that however sophisticated a computational system is, it can never exhibit intentionality; thus, would also fail to exhibit consciousness or any of its varieties. This paper argues that the proposed architecture mentioned above answers the problem posed by Searle, at least in part. Specifically, it argues that we could reformulate Searle’s worries in the Chinese room in terms of the three-stage artificial qualia model. And by doing so, we could see that the person doing all the translations in the room could realize the three areas in the proposed framework. Consequently, this demonstrates the actualization of self-consciousness in machines. (shrink)

This paper is a complement to the recent wealth of literature suggesting a strong philosophical relationship between artificial life (A-Life) and artificial intelligence (AI). I seek to point out where this analogy seems to break down, or where it would lead us to draw incorrect conclusions about the philosophical situation of A-Life. First, I sketch a thought experiment (based on the work of Tom Ray) that suggests how a certain subset of A-Life experiments should be evaluated. In doing so, I (...) suggest that treating A-Life experiments as if they were just AI experiments applied to a new domain may lead us to see problems (like Searle’s “Chinese room”) which do not exist. In the second half of the paper, I examine the reasons for suggesting that there is a philosophical relationship between the two fields. I characterize the strong thesis for a translation of AI concepts, metaphors, and arguments into A-Life as the “global replacement strategy.” Such a strategy is only fruitful inasmuch as there is a strong analogy between AI and A-Life. I conclude the paper with a discussion of two areas where such a strong analogy seems to break down. These areas relate to eliminative materialism and the lack of a “subjective” element in biology. I conclude that the burden of proof lies with the person who wishes to import a concept from another discipline into A-Life, even if that other discipline is AI. (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)

What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such systems, guided and limited by associative memory, is similar to the stream of consciousness. A specific architecture of an artificial system, termed articon, is introduced that by its very design has to claim being conscious. (...) Non-verbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the flow of inner states of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills — when conscious information processing is replaced by subconscious — is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute articon’s claims that it is conscious. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human. (shrink)

I have laid out what seem to me to be the most promising arguments on opposing sides of the question of whether what humans regard as the first-person accessible aspects of pain could also be implemented in robots. I have emphasized the ways in which the thought experiments in the respective arguments attempt to marshal hypothetical first- person accessible evidence concerning how one’s own mental life appears to oneself. In the Chinese room argument, a crucial premise involves the (...) thesis that from a lack of it seeming that one is in pain, one can conclude that one is not in pain. There’s a counterpart thesis playing a crucial role in the prosthetic neurons argument, one asserting an entailment from its seeming that one is in pain to one’s being in pain. I further suggested that by adopting a HOT theory of consciousness, of the sort reviewed in Pereplyotchik’s chapter in the present volume, one is thereby in a better position to endorse the prosthetic neurons argument over the Chinese room argument than to make the opposite appraisal. (shrink)

Whether it was John Searle’s Chinese Room argument (Searle, 1980) or Roger Penrose’s argument of the non-computable nature of a mathematician’s insight – an argument that was based on Gödel’s Incompleteness theorem (Penrose, 1989), we have always had skeptics that questioned the possibility of realizing strong Artificial Intelligence (AI), or what has become known by Artificial General Intelligence (AGI). But this new book by Landgrebe and Smith (henceforth, L&S) is perhaps the strongest argument ever made against strong AI. (...) It is a very extensive review of what building a mind essentially amounts to drawing on insights and results from biology, physics, linguistics, computability, philosophy, and mathematics. (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)

The philosophical study of consciousness is chock full of thought experiments: John Searle’s Chinese Room, David Chalmers’ Philosophical Zombies, Frank Jackson’s Mary’s Room, and Thomas Nagel’s ‘What is it like to be a bat?’ among others. Many of these experiments and the endless discussions that follow them are predicated on what Chalmers famously referred as the ‘hard’ problem of consciousness: for him, it is ‘easy’ to figure out how the brain is capable of perception, information integration, attention, (...) reporting on mental states, etc, even though this is far from being accomplished at the moment. What is ‘hard’, claims the man of the p-zombies, is to account for phenomenal experience, or what philosophers usually call ‘qualia’: the ‘what is it like’, first-person quality of consciousness. (shrink)

The relations between life and cogntion have been addressed through different perspectives [Stewart 1996, Boden 2001, Bourgine and Stewart 2004, van Duijn & all 2006, Di Paolo 2009]. We would like here to address that subject by relating life to cognition through a process of meaning generation. Life emerged on earth as a far from thermodynamic equilibrium performance that had to maintain herself. Life is charactertized by a ‘stay alive’ constraint that has to be satisfied (such constraint can be included (...) in the constraint of being able to maintain far from equilibrium thermodynamic conditions [Bickhard 2011]). The local ‘stay alive‘ constraint has to be satisfied in an environment containing elements potentially supportive or harmfull. A key activity for the living entity is to characterize these elements in terms of meaningfulness relatively to the ‘stay alive’ constraint. This process can be modeled with an existing tool where a system submitted to an internal constraint generates meaningful information characterizing elements of the environment: the Meaning Generator System (MGS) [Menant, 2003, 2014 a]. In a few words: when a system submitted to an internal constraint receives from the environment an information that has a connection with the constraint it generates a meaning usable for the implementation of an action satisfying the constraint.The generated meaning is the connection existing between the received information and the constraint. The MGS models this process and interfaces to the action implementation for constraint satisfaction The meaning is generated by and for the system. The MGS grounds meaning generation in constraint satisfaction and links the living entity to her environment in a relational process. A simple example is with a paramecium close to a drop of acid. The paramecium which is submitted to a ‘stay alive’ constraint will move away from the acid area. The received information ‘presence of acid’ generates the meaning ‘acid not compatible with the ‘stay alive’ constraint‘ which triggers the moving away action (that example is close to Varela’s bacteria swimming up a sugar gradient. What the MGS brings in addition is a modeling of the significance of the chemical gradient for the organism). The action implemented to satisfy the constraint modifies the environment and the received information, establishing an interactive process linking the living entity to her environment. During its evolution animal life has elaborated new constraints (like ‘live group life’) and new functions enriching meaning generation and action scenarios. As a result the build up of meaningful representations has improved the constraint satisfaction processes of animals, embedding them in their environments in relational and interactive terms [Menant, 2011]. Cognition can be defined by proposing that ‘a system is cognitive if and only if sensory inputs serve to trigger actions in a specific way, so as to satisfy a viability constraint’ [Bourgine, Stewart 2004]. Cognition can also be considered as exemplifying a ‘vital criterion of responsiveness’ [Boden, 2001 ]. Consequently the MGS can be positioned as an elementary and generic version of animal cognition. For animal life, meaning generation for internal constraint satisfaction links life and cognition in a relational and interactive process. Cognition for human life is more complex as new performances have to be taken into account like self-consciousness and free-will. Meaning generation at human level is a challenging subject as human constraints are not clearly understood [Menant, 2011]. Many research activities are in process looking for some understanding of human mind [Philpapers]. One area of investigation is an evolutionary approach to self-consciousness using meaning generation where anxiety limitation comes up as a generic human constraint [Menant, 2014 b]. Assuming that we can have clear enough an understanding of some human constraints, we can look at the MGS for partly extending to humans the link between life and cognition that has been established for animals. So overall, we can consider that the MGS approach makes available an evolutionary link between life and cognition for animals, and partly for humans. A characteristic of the proposed system approach to meaning generation is the possibility to use it for any type of agent, be it organic (with intrinsic constraints ) or artificial (with derived constraints). Such characterization of agents through meaning generation can be used to discriminate artificial itelligence from human intelligence (see the MGS usage to support Searle’s chinese room argument [Menant, 2013]). On a more general basis, the proposed system approach can positions the MGS as a simple model for an internal source of normativity. It’s usage as a simple building block allows a bottom-up modeling for normativity in the sensorimotor approach. The ‘stay alive’ constraint could also be taken as a starting point for an evolutionary grounding of sensorimotor norms ‘in the biological normativity of the agent as a whole’ [Di Paolo & all 2014]. The proposed presentation will develop the points summarized here above and position them relatively to the autopoietic and enactive approaches. Several possible continuations will also be highlighted. (shrink)

In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence, entitled ‘New Generation Artificial Intelligence Development Plan’. This strategy outlined China’s aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this article, we focus on (...) the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents. (shrink)

The new kid on the block in cognitive science these days is dynamic systems. This way of thinking about the mind is, as usual, radically opposed to computationalism - - the hypothesis that thinking is computing. The use of dynamic systems is just the latest in a series of attempts, from Searle's Chinese Room Argument, through the weirdnesses of postmodernism, to overthrown computationalism, which as we all know is a perfectly nice hypothesis about the mind that never hurt (...) anyone. (shrink)

The logical problem of artificial intelligence—the question of whether the notion sometimes referred to as ‘strong’ AI is self-contradictory—is, essentially, the question of whether an artificial form of life is possible. This question has an immediately paradoxical character, which can be made explicit if we recast it (in terms that would ordinarily seem to be implied by it) as the question of whether an unnatural form of nature is possible. The present paper seeks to explain this paradoxical kind of possibility (...) by arguing that machines can share the human form of life and thus acquire human mindedness, which is to say they can be intelligent, conscious, sentient, etc. in precisely the way that a human being typically is. (shrink)

The Chinese Heart Sutra was traditionally considered a translation of an Indian Sanskrit text. In the late 20th century scholars began to question this tradition. The Heart Sutra reuses passages from other texts, principally the Large Prajñāpāramitā Sutra. The reused passages are extant in Sanskrit and Chinese source texts and this enables us to perform a unique form of comparative analysis to confirm what language the Heart Sutra was composed in. Jan Nattier examined about half of the text (...) – the 'core section' – and concluded it was composed in Chinese and 'back-translated' into Sanskrit. Nattier’s method has been extended to other parts of the text with the same result. This article details an exhaustive application of Nattier’s method to the Heart Sutra. Considering 22 points of comparison, many of them new, we find a pervasive pattern of features and bugs that definitively point to Chinese origins. (shrink)

I assume that there exists a general phenomenon, the phenomenon of the explanatory gap, surrounding consciousness, normativity, intentionality, and more. Explanatory gaps are often thought to foreclose reductive possibilities wherever they appear. In response, reductivists who grant the existence of these gaps have offered countless local solutions. But typically such reductivist responses have had a serious shortcoming: because they appeal to essentially domain-specific features, they cannot be fully generalized, and in this sense these responses have been not just local but (...) parochial. Here I do better. Taking for granted that the explanatory gap is a genuine phenomenon, I offer a fully general diagnosis that unifies these previously fragmented reductivist responses. (shrink)

Floridi and Taddeo propose a condition of “zero semantic commitment” for solutions to the grounding problem, and a solution to it. I argue briefly that their condition cannot be fulfilled, not even by their own solution. After a look at Luc Steels' very different competing suggestion, I suggest that we need to re-think what the problem is and what role the ‘goals’ in a system play in formulating the problem. On the basis of a proper understanding of computing, I come (...) to the conclusion that the only sensible ground-ing problem is how we can explain and re-produce the behavioral ability and function of meaning in artificial computational agents. (shrink)

Sorensen (1992) has provided two modal-logical schemas to reconstruct the logical structure of two types of destructive thought experiments: the Necessity Refuter and the Possibility Refuter. The schemas consist of five propositions which Sorensen claims but does not prove to be inconsistent.We show that the five propositions, as presented by Sorensen, are not inconsistent, but by adding a premise (and a logical truth), we prove that the resulting sextet of premises is inconsistent. Häggqvist (2009) has provided a different modal-logical schema (...) (Counterfactual Refuter), which is equivalent to four premises, again claimed to be inconsistent. We show that this schema also is not inconsistent, for similar reasons. Again, we add another premise to achieve inconsistency. The conclusion is that all three modal-logical reconstructions of the arguments that accompany thought experiments, two by Sorensen and one by Häggqvist, have now been made rigorously correct. This may inaugurate new avenues to respond to destructive thought experiments. (shrink)

The way we answer questions about what there is crucially depends on the language and the logic in which they are framed. This entry introduces the orthodox view on how to carry out such debates, as was formulated by W. V. O. Quine, as well as a number of influential alternatives. A further issue that is explored is whether disagreement about what there is turns on mind-independent features of reality, or it is an artifact of language.

The paper outlines an analysis of two types of information - ordinary and subjective, consideration is given to the difference between the concepts of intelligence and perceiving mind. It also provides description of some logical functional features of consciousness. A technical approach is proposed to technical obtaining of subjective information by changing the signal’s time degree of freedom to the spatial one in order to obtain the "observer" function in the system and information signals appearing in relation to it, that (...) serve as subjective sensations. The solution to this problem may give a new approach to designing a strong artificial intelligence and shed light on the difficult problem of the mind. (shrink)

examines Hegel’s analysis of honor and recognition in the chivalric culture, and contrasts that to the xia tradition’s engagement with Buddhism, Daoism, Confucianism, and Chinese Legalism.

This article endeavors to provide an overview on contemporary Chinese philosophy. The focus is on contemporary Chinese philosophy in the Chinese-speaking world, particularly after the 1950s, although contemporary Chinese philosophy both in its inception in early 20th century China and in the English-speaking world are also explored. In addition to designating separate genres of contemporary Chinese philosophical interpretation and construction, including some of the major issues under discussion and debate as well as giving attention to (...) several representative scholars, this article also teases out the historical contexts in which those issues emerged and developed, and it highlights the salient feature of contemporary Chinese philosophy in general. -/- . (shrink)

There is a growing movement towards construing some classic debates in ontology as meaningless, either because the answers seem obvious or the debates seem intractable. In this paper, I respond to this movement. The response has three components: First, the members of the two sides of the ontological debates that dismissivists have targeted are using different quantifiers. Second, the austere ontologist is using a more fundamental quantifier than her opponent. Third, the austere ontologist’s more fundamental quantifier is a restriction of (...) her opponent’s quantifier. This response takes seriously the intuition that there is something wrong with the ontological disputes, but does not entail dismissivism. (shrink)

This research is aimed at the critical analysis of Searle's critique of cognitive science. The focus of Searle's critique is on computationalism, which holds that mind is to brain as software is to hardware—all mental states are explained in terms of computational processes...

Multinational companies commonly and increasingly undertake their research in low and middle-income countries through commercial clinical research organizations (CROs). The involvement of these scientific middle men complicates the application of the theories of justice. We examine those complexities, and conclude that while the difficulties are not immune to analysis in terms of these theories, the theories have to be deployed in new ways in order to be useful in the new commercial world.