When one looks into the role of artificial languages in philosophy of language it seems appropriate to start with making a distinction between philosophy of language proper and formal semantics of natural language. Although the distinction between the two disciplines may not always be easy to make since there arguably exist substantial historical and systematic relationships between the two, it nevertheless pays to keep the two apart, at least initially, since the motivation commonly given for the use (...) of artificial languages in philosophy of language is often rather different from the one that drives the use of such languages in semantics. Of course, this difference in motivation should not blind us for the commonalities that exists between the two disciplines. Philosophy of language and formal semantics have a common history, and arguably also share some of their substance. Philosophy of language is by and large an outgrowth of work in the analytical tradition in philosophy in the first half of the twentieth century. Both ordinary language philosophy, with its emphasis on the description of actual language use, as well as the more logic-oriented and formally inclined school of logical positivism contributed to the definition of philosophy of language as a separate philosophical discipline, with its own set of problems and methods to solve them. Another major contributor to the establishment of philosophy of language as a distinct discipline has been modern linguistics, in particular generative grammar in the tradition of Chomsky that became the dominant paradigm in linguisticsin the fifties and sixties of the previous century. And as it happens, both the generative tradition of Chomsky and analytic philosophy in its formal and less formal guises have been important factors in the development of formal semantics as well. Thus, it should come as no surprise that the two have something in common. That the communalities go beyond a common ancestry, but are reflected in substance and methods as well, will be argued later on. However, be that as it may, it is still a good idea to keep philosophy of language and formal semantics separate, at least initially, since the role that is assigned to artificial languages and the ways in which these languages are employed in both does differ in a number of respects that are worth keeping in mind.. (shrink)

The syllables and series of sounds composing Gayatri Mantra, and the sense and meaning attached to them are analyzed using Upanishadic Wisdom, Advaitha Philosophy and Sabdabrahma Siddhanta. The physical structure of mind as revealed by this analysis is presented. An insight of various phases of mind, their rise and set, their significance and implications to cognitive sciences and natural language comprehension branch of artificial intelligence are discussed. The possible applications of such an insight in the fields of cognitive (...) sciences, modeling of human mental processes and language learning/communication processes are hinted. (shrink)

Upanishads are traditionally commented upon as texts of theology and religion. But the contents of the Upanishads can also be viewed and commented from modern science point of view. Elements of modern science present in the Upanishads and Advaita Siddhanta will be listed. -/- The nature of maya will be discussed with modern scientific awareness. This awareness will be further used in understanding human mental processes and the ways to model them contributing to the natural language comprehension field of (...) artificial intelligence. -/- The nature of the Dvaita and Advaita conscious states and their implications to human cognitive processes will be discussed. The use and significance of the ‘absence of mind’ conscious state will also be presented. -/- The use of above understanding in modeling human understanding process and its comparison to physiological psychology – the physics and chemistry of psychology - will be proposed. (shrink)

The notion of Simulative Reasoning in the study of propositional attitudes within Artificial Intelligence (AI) is strongly related to the Simulation Theory of mental ascription in Philosophy. Roughly speaking, when an AI system engages in Simulative Reasoning about a target agent, it reasons with that agent’s beliefs as temporary hypotheses of its own, thereby coming to conclusions about what the agent might conclude or might have concluded. The contrast is with non-simulative meta-reasoning, where the AI system reasons within a (...) detailed theory about the agent’s (conjectured) reasoning acts. The motive within AI for preferring Simulative Reasoning is that it is more convenient and efficient, because of a simplification of the representations and reasoning processes. The chapter discusses this advantage in detail. It also sketches the use of Simulative Reasoning in an AI natural language processing system, ATT-Meta, that is currently being implemented. This system is directed at the understanding of propositional attitude reports. In ATT-Meta, Simulative Reasoning is yoked to a somewhat independent set of ideas about how attitude reports should be treated. Central here are the claims that (a) speakers often employ commonsense (and largely metaphorical) models of mind in describing agents’ attitudes, (b) the listener accordingly needs often to reason within the terms of such models, rather than on the basis of any objectively justifiable characterization of the mind, and (c) the commonsense models filter the suggestions that Simulative Reasoning comes up with concerning target agents’ reasoning conclusions. There is a yet tighter connection between the commonsense models and the Simulative Reasoning. It turns out that Simulative Reasoning can be rationally reconstructed in terms of a more general type of reasoning about the possibly-counterfactual “world” that the target agent believes in, together with an assumption that that agent has a faithful representation of the world. In the ATT-Meta approach, the reasoner adopts that assumption when it views the target agent through a particular commonsense model (called IDEAS-AS-MODELS). (shrink)

Contemporary natural-language semantics began with the assumption that the meaning of a sentence could be modeled by a single truth condition, or by an entity with a truth-condition. But with the recent explosion of dynamic semantics and pragmatics and of work on non- truth-conditional dimensions of linguistic meaning, we are now in the midst of a shift away from a truth-condition-centric view and toward the idea that a sentence’s meaning must be spelled out in terms of its various roles (...) in conversation. This communicative turn in semantics raises historical questions: Why was truth-conditional semantics dominant in the first place, and why were the phenomena now driving the communicative turn initially ignored or misunderstood by truth-conditional semanticists? I offer a historical answer to both questions. The history of natural-language semantics—springing from the work of Donald Davidson and Richard Montague—began with a methodological toolkit that Frege, Tarski, Carnap, and others had created to better understand artificial languages. For them, the study of linguistic meaning was subservient to other explanatory goals in logic, philosophy, and the foundations of mathematics, and this subservience was reflected in the fact that they idealized away from all aspects of meaning that get in the way of a one-to-one correspondence between sentences and truth-conditions. The truth-conditional beginnings of natural- language semantics are best explained by the fact that, upon turning their attention to the empirical study of natural language, Davidson and Montague adopted the methodological toolkit assembled by Frege, Tarski, and Carnap and, along with it, their idealization away from non-truth-conditional semantic phenomena. But this pivot in explana- tory priorities toward natural language itself rendered the adoption of the truth-conditional idealization inappropriate. Lifting the truth-conditional idealization has forced semanticists to upend the conception of linguistic meaning that was originally embodied in their methodology. (shrink)

Thomas Reid distinguished between natural and artificial language and argued that natural language has a very specific sort of priority over artificial language. This paper critically interprets Reid's discussion, extracts a Reidian explanatory argument for the priority of natural language, and places Reid's thought in the broad tradition of Cartesian linguistics.

The famous advaitic expressions -/- Brahma sat jagat mithya jivo brahma eva na apraha and Asti bhaati priyam namam roopamcheti amsa panchakam AAdya trayam brahma roopam tato dwayam jagat roopam -/- will be analyzed through physics and electronics and interpreted. -/- Four phases of mind, four modes of language acquisition and communication and seven cognitive states of mind participating in human cognitive and language acquisition and communication processes will be identified and discussed. -/- Implications and application of such an identification (...) and analysis to the fields of cognitive sciences, mind-machine modeling, natural language comprehension field of artificial intelligence and physiological psychology will be hinted. A comprehensive modern scientific understanding of human consciousness, mind and mental functions will be presented. (shrink)

Some of the systems used in natural language generation (NLG), a branch of applied computational linguistics, have the capacity to create or assemble somewhat original messages adapted to new contexts. In this paper, taking Bernard Williams’ account of assertion by machines as a starting point, I argue that NLG systems meet the criteria for being speech actants to a substantial degree. They are capable of authoring original messages, and can even simulate illocutionary force and speaker meaning. Background intelligence embedded (...) in their datasets enhances these speech capacities. Although there is an open question about who is ultimately responsible for their speech, if anybody, we can settle this question by using the notion of proxy speech, in which responsibility for artificial speech acts is assigned legally or conventionally to an entity separate from the speech actant. (shrink)

Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? In (...) this paper, we propose a number of steps that help answer these questions. We start by developing a philosophical analysis of the building blocks of linguistic communication between conversational agents and human interlocutors. We then use this analysis to identify and formulate ideal norms of conversation that can govern successful linguistic communication between humans and conversational agents. Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains. We conclude by discussing the practical implications of our proposal for the design of conversational agents that are aligned with these norms and values. (shrink)

This article contains the results of a theoretical analysis of the phenomenon of natural language understanding (NLU), as a methodological problem. The combination of structural-ontological and informational-psychological approaches provided an opportunity to describe the subject matter field of NLU, as a composite function of the mind, which systemically combines the verbal and discursive structural layers. In particular, the idea of NLU is presented, on the one hand, as the relation between the discourse of a specific speech message and the (...) meta-discourse of a language, in turn, activated by the need-motivational factors. On the other hand, it is conceptualized as a process with a specific structure of information metabolism, the study of which implies the necessity to differentiate the affective (emotional) and need-motivational influences on the NLU, as well as to take into account their interaction. At the same time, the hypothesis about the influence of needs on NLU under the scenario similar to the pattern of Yerkes-Dodson is argued. And the theoretical conclusion that emotions fulfill the function of the operator of the structural features of the information metabolism of NLU is substantiated. Thus, depending on the modality of emotions in the process of NLU, it was proposed to distinguish two scenarios for the implementation of information metabolism - reduction and synthetic. The argument in favor of the conclusion about the productive and constitutive role of emotions in the process of NLU is also given. (shrink)

The objective of this work is to explore the notion of “action” and “agency” in artificial intelligence (AI). It employs a metaphysical notion of action and agency as an epistemological tool in the critique of the notion of “action” and “agency” in artificial intelligence. Hence, both a metaphysical and cognitive analysis is employed in the investigation of the quiddity and nature of action and agency per se, and how they are, by extension employed in the language and science (...) of artificial intelligence. The advent of the science of artificial intelligence and cognitive science, and the technological applications of artificial intelligence in the production of agents such as driverless cars and expert systems, have raised the question of moral, ethical and/or legal responsibility in AI agents. This has re-emphasized the importance of the philosophical discourse on the notions of action and agency, which in contemporary intellectual discourse are now perceived to be phenomena within the epistemic competence of the natural sciences. This paper argues that AI systems do not and cannot possess free agency and autonomy, thus cannot be morally and ethically responsible, hence, it recommends a socio-political response to the question of responsibility in AI. It is then the duty of individual nations, or the global community to define and enact policies on who shoulders the responsibility of actions executed by AIs. (shrink)

This essay discusses how medieval authors from the thirteenth and fourteenth centuries dealt with a philosophical problem that social institutions pose for the Aristotelian dichotomy between natural and artificial entities. It is argued that marriage, political community, and language provided a particular challenge for the conception that things which are designed by human beings are artefacts. Medieval philosophers based their arguments for the naturalness of social institutions on the anthropological view that human beings are political animals by nature, (...) but this strategy required rethinking the borderline between nature and art. The limits of nature were extended, as social institutions were considered to be natural even though they are in many ways similar to artificial products. (shrink)

Four modes of language acquisition and communication are presented translating ancient Indian expressions on human consciousness, mind, their form, structure and function clubbing with the Sabdabrahma theory of language acquisition and communication. The modern scientific understanding of such an insight is discussed. . A flowchart of language processing in humans will be given. A gross model of human language acquisition, comprehension and communication process forming the basis to develop software for relevantmind-machine modeling will be presented. The implications of such a (...) model to artificial intelligence and cognitive sciences will be discussed. The essential nature and necessity of a physics, communication engineering , biophysical and biochemical insight as both complementary and supplementary to using mathematical and computational methods in delineating the theory of language processing in humans is put forward. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...) technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

A fun piece discussing the challenges to and prospects of building machines that are able to produce and understand natural language.

e first group of articles attempt to give some insight into how we behave that is reasonably free of theoretical delusions. In the next three groups I comment on three of the principal delusions preventing a sustainable world— technology, religion and politics (cooperative groups). People believe that society can be saved by them, so I provide some suggestions in the rest of the book as to why this is unlikely via short articles and reviews of recent books by well-known writers. (...) -/- America and the world are in the process of collapse from excessive population growth, most of it for the last century and now all of it due to 3rd world people. Consumption of resources and the addition of 4 billion more ca. 2100 will collapse industrial civilization and bring about starvation, disease, violence and war on a staggering scale. Billions will die and nuclear war is all but certain. In America this is being hugely accelerated by massive immigration and immigrant reproduction, combined with abuses made possible by democracy. Depraved human nature inexorably turns the dream of democracy and diversity into a nightmare of crime and poverty. The root cause of collapse is the inability of our innate psychology to adapt to the modern world, which leads people to treat unrelated persons as though they had common interests. This, plus ignorance of basic biology and psychology, leads to the social engineering delusions of the partially educated who control democratic societies. Few understand that if you help one person you harm someone else—there is no free lunch and every single item anyone consumes destroys the earth beyond repair. Consequently, social policies everywhere are unsustainable and one by one all societies without stringent controls on selfishness will collapse into anarchy or dictatorship. Without dramatic and immediate changes, there is no hope for preventing the collapse of America, or any country that follows a democratic system. Hence my essay “Suicide by Democracy”. It is also now clear that the seven sociopaths who rule China are winning world war 3, and so my concluding essay on them. The only greater threat is Artificial Intelligence which I comment on briefly in the last paragraph. (shrink)

Recent research into the nature of self in artificial and biological systems raises interest in a uniquely determining immutable sense of self, a “metaphysical ‘I’” associated with inviolable personal values and moral convictions that remain constant in the face of environmental change, distinguished from an object “me” that changes with its environment. Complementary research portrays processes associated with self as multimodal routines selectively enacted on the basis of contextual cues informing predictive self or world models, with the notion of (...) the constant, per-vasive and invariant sense of self associated with a multistable attractor set aiming to ensure personal integrity against threat of disintegrative change. This paper proposes that an immutable sense of self emerges as a global attractor which can be described as a project ideal self-situation embodied in frontal medial processes during more or less normal adolescent development, and that thereafter serves to orient agency in the more or less free development of embodied potentials over the life course in effort to realize project conditions, phenomenally identified with the felt pull towards this end as purpose of and source of meaning in life. So oriented, life-long self-development aims to embody solutions to problems at different timescales depending on this embodied purpose, ultimately in the service of evolutionary processes securing organism populations against threats of disintegrative change over timespans far beyond that of the individual. After characterizing the target sense of self, research circling this target is briefly surveyed. Self as global project and developmental neural correlates are proposed. Then, the paper discusses some implications for research in biological and artificial systems. Building from earlier work in cognitive neurorobotics, discussion affirms the value of reinforcement rituals including prayer in metaphysical self-development, considers implications for value alignment and rights associated with free will in the context of artificial intelligence and robot religion, and concludes by emphasizing the importance of self-development toward project ideals as source of meaning in life in the current social-political environment. (shrink)

In this commentary, we discuss the nature of reversible and irreversible questions, that is, questions that may enable one to identify the nature of the source of their answers. We then introduce GPT-3, a third-generation, autoregressive language model that uses deep learning to produce human-like texts, and use the previous distinction to analyse it. We expand the analysis to present three tests based on mathematical, semantic, and ethical questions and show that GPT-3 is not designed to pass any of them. (...) This is a reminder that GPT-3 does not do what it is not supposed to do, and that any interpretation of GPT-3 as the beginning of the emergence of a general form of artificial intelligence is merely uninformed science fiction. We conclude by outlining some of the significant consequences of the industrialisation of automatic and cheap production of good, semantic artefacts. (shrink)

The theory and philosophy of artificial intelligence has come to a crucial point where the agenda for the forthcoming years is in the air. This special volume of Minds and Machines presents leading invited papers from a conference on the “Philosophy and Theory of Artificial Intelligence” that was held in October 2011 in Thessaloniki. Artificial Intelligence is perhaps unique among engineering subjects in that it has raised very basic questions about the nature of computing, perception, reasoning, learning, (...) language, action, interaction, consciousness, humankind, life etc. etc. – and at the same time it has contributed substantially to answering these questions. There is thus a substantial tradition of work, both on AI by philosophers and of theory within AI itself. - The volume contains papers by Bostrom, Dreyfus, Gomila, O'Regan and Shagrir. (shrink)

In scientific communications, journal articles, and philosophical aesthetic debates the words “art”, “creativity”, and “machine” are put together more and more frequently. Since some machines are designed to, or happens to, imitate human artistic creativity, it seems natural to use the same words to talk about human artists and machines which imitate them. However, the evolution of language in light of technology may conceal specific features of the phenomena it is supposed to describe. This makes it difficult to understand (...) what machine creativity actually is and how it is connected to human creativity. The aim of the paper is to investigate why, and in what sense, the functioning of some machines may be described in terms of artistic creativity and what is the relation between machine creativity and its human archetype. I start (§1) by introducing some general ideas concerning how language evolves alongside new technologies and focusing on the case of machine creativity. In §2 I review how some creative machines have been presented to the public, thus showing that a linguistic habit which connects machines to artistic creativity has already formed. In §3 I survey the debate on machine aesthetics and I highlight the main traits of the issue. In §4 I submit a primary scheme of machine creativity which draws on the concept of functional autonomy. Finally (§5), I argue that machine creativity cannot be mistaken for a substitute of the human kind, despite the fact that the word is the same. Creative machines are technological mediums by which new forms of human aesthetic experience can be explored. The kind of creativity machines enjoy displays a functional nature. (shrink)

The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to the collection of partial data, rare updates, and significant demands on resources. To address these issues, the article suggests that specific data management and Machine Learning (ML) techniques, such as natural language (...) processing and sentiment analysis, can improve the measurement (ML about) and practice (ML for) of IPD. The article concludes by considering some of the principal risks of ML for IPD, including concerns over data privacy, the potential for manipulation, and the dangers of overreliance on technology. (shrink)

The rapid advancement of Information Technology (IT) platforms and programming languages has transformed the dynamics and development of human society. The cyberspace and associated utilities are expanding, leading to a gradual shift from real-world living to virtual life (also known as cyberspace or digital space). The expansion and development of Natural Language Processing (NLP) models and Large Language Models (LLMs) demonstrate human-like characteristics in reasoning, perception, attention, and creativity, helping humans overcome operational barriers. Alongside the immense potential of (...) artificial intelligence (AI) are new security loopholes and more complex information security risks. As society is still trying to transition to a new phase to adapt to technological changes, the AI revolution continues to unfold, necessitating a reconsideration of the trajectory of societal transformation as it could exacerbate the aforementioned information security risks. Specifically, how should society evolve to keep pace with the transformative impact of the current AI technology wave? How can we manage and harness their power while ensuring information security as our presence in the virtual world increases? This article aims to shed light on and address these questions. (shrink)

The semantic rules governing natural language quantifiers (e.g. "all," "some," "most") neither coincide with nor resemble the semantic rules governing the analogues of those expressions that occur in the artificial languages used by semanticists. Some semanticists, e.g. Peter Strawson, have put forth data-consistent hypotheses as to the identities of the semantic rules governing some natural-language quantifiers. But, despite their obvious merits, those hypotheses have been universally rejected. In this paper, it is shown that those hypotheses are (...) indeed correct. Moreover, data-consistent hypotheses are put forth as to the identities of the semantic rules governing the words "most" and "many," the semantic rules governing which semanticists have thus far been unable to identify. The points made in this paper are anticipated in a paper, published in the same issue of the Journal of Pragmatics, by Andrzej Boguslawski. (shrink)

Sabdabrahma Siddhanta, popularized by Patanjali and Bhartruhari will be scientifically analyzed. Sphota Vada, proposed and nurtured by the Sanskrit grammarians will be interpreted from modern physics and communication engineering points of view. Insight about the theory of language and modes of language acquisition and communication available in the Brahma Kanda of Vakyapadeeyam will be translated into modern computational terms. A flowchart of language processing in humans will be given. A gross model of human language acquisition, comprehension and communication process forming (...) the basis to develop software for relevant mind-machine modeling will be presented. The implications of such a model to artificial intelligence and cognitive sciences will be discussed. The essentiality and necessity of a physics, communication engineering , biophysical and biochemical insight as both complementary and supplementary to using mathematical and computational methods in delineating the theory of Sanskrit language is put forward. Natural language comprehension as distinct and different from natural language processing is pointed out. (shrink)

On the basis of a revision of the real dynamics of Greek poiesis and autopoiesis as evolutionary processes of meaning and knowledge-of-the-World evaluative-construction, Cognitive Poetics proposes key philological, ontological and cultural adjustments to improve our understanding of thought, conceptual activity, and the origins and social nature of language. It searches for an integrated theory of social problems in general Cognitive Science: from Linguistics or Psychology, through Anthropology, Neurophilosophy or Literary Studies, to Neurobiology or Artificial Life Sciences. From an essential (...) turn to the real thing, cognition as (social) action, it provides new unforeseen accounts of the complex dynamics of human essentially creative understanding studying and analyzing all form of texts as active fossils as mentils (Guerra 2009). (shrink)

This essay considers what it means to understand natural language and whether a computer running an artificial-intelligence program designed to understand natural language does in fact do so. It is argued that a certain kind of semantics is needed to understand natural language, that this kind of semantics is mere symbol manipulation (i.e., syntax), and that, hence, it is available to AI systems. Recent arguments by Searle and Dretske to the effect that computers cannot understand (...) class='Hi'>natural language are discussed, and a prototype natural-language-understanding system is presented as an illustration. (shrink)

Thomas Reid's philosophy of mind, epistemology, and philosophy of language all rely on his account of signs and signification. On Reid's view, some entities play a role of indicating other entities to our minds. In some cases, our sensitivity to this indication is learned through experience, whereas in others, the sensitivity is built in to our natural constitutions. Unlike representation, which was presumed to depend on resemblances and necessary connections, signification is the sort of relationship that can occur without (...) any intrinsic ties between the sign and the thing signified. Of particular interest is the priority Reid gives to natural signs. Reid deploys his robust account of natural signs in a way that allows him to sidestep some skeptical worries common in the early modern period, as well as in grounding “artificial” human languages like English or Cantonese on a more basic human capacity for communication, that is, itself, the result of our sensitivity to natural signs of other people's minds. (shrink)

Sample chapter from H. Asgeirsson, The Nature and Value of Vagueness in the Law (Hart Publishing, 2020), in which I present and partially defend a version of what has come to be called the communicative-content theory of law. Book abstract: Lawmaking is – paradigmatically – a type of speech act: people make law by saying things. It is natural to think, therefore, that the content of the law is determined by what lawmakers communicate. However, what they communicate is sometimes (...) vague and, even when it is clear, the content itself is sometimes vague. The monograph examines the nature and consequences of these two linguistic sources of indeterminacy in the law with the aim of providing plausible answers to three related questions: In virtue of what is the law vague? What might be good about vague law? How should courts resolve cases of vagueness? (shrink)

This paper explores the integration of multiple intelligences and artificial intelligence (AI) in language learning, focusing on its potential to enhance personalization and engagement. Drawing from existing research and studies conducted in various contexts, including the Philippines, this study aims to contribute to the understanding of the benefits, challenges, and effectiveness of this integration. The paper begins with an introduction that highlights the background and significance of integrating multiple intelligences and AI in language learning, identifying research gaps, objectives, research (...) questions, and the theoretical framework. A literature review provides an overview of multiple intelligences theory by Howard Gardner, the role of AI in language learning, and identifies gaps in the existing literature. The methodology section outlines the research design and approach, participant selection, data collection methods, validity and reliability measures, and ethical considerations. Findings and results are presented through the analysis of qualitative data, exploring emergent themes and patterns. The discussion section critically examines the identified research gaps, discusses the validity and reliability of the study, addresses the scope and limitations, and explores the implications of the findings for theory, practice, and future research. The conclusion summarizes the key findings and contributions of the study, reflects on the achievement of research objectives, offers recommendations for further research, and provides final remarks tying together the main points of the study. This paper contributes to the existing body of knowledge by providing insights into the integration of multiple intelligences and AI in language learning and its impact on personalization, engagement, and language learning outcomes. (shrink)

This paper is in two parts. Part 1 examines the phenomenon of making space as a process involving one or other kind of legal decision-making, for example when a state authority authorizes the creation of a new highway along a certain route or the creation of a new park in a certain location. In cases such as this a new abstract spatial entity comes into existence – the route, the area set aside for the park – followed only later by (...) concordant changes in physical reality. In Part 2 we show that features identified in studying this phenomenon of legal spacemaking can be detected in other spheres of human activity, for example in planning (where spacemaking is projected into the future), and in reasoning about history (where spacemaking is projected back through time). We shall see that these features display themselves in especially complex ways in our everyday use of language, and we conclude by examining the implications of this complexity for attempts to create an artificial intelligence that would enjoy a mastery of language that would be equivalent to that of human beings. (shrink)

For any natural (human) or formal (mathematical) language L we know that an expression X of language L is true if and only if there are expressions Γ of language L that connect X to known facts. -/- By extending the notion of a Well Formed Formula to include syntactically formalized rules for rejecting semantically incorrect expressions we recognize and reject expressions that evaluate to neither True nor False.

This paper tries to determine the philosophical nature of language, its functions, structure and content. It also explains the concept of natural language, ordinary and ideal language i.e. how there is a need of artificial perfect logical language without errors and unclearness in that language. This paper further shows the logical form of language with its syntactical, semantical, innate and acquired criteria for the evaluation of the languages. It deals with the analysis of language to clear what (...) is unclear, to know what is unknown, to make definite what is vague. In this paper I used the method; logical method for interpretation and argumentation, analytical method for simplification, and critical method to investigate the real domain of language. This paper does not deal with ordinary functions of the language but it deals with the conceptual and modular functions of the language. The fundamental aim of this written up is to determine the analytical approach of Wittgenstein to sketch the ‘language as a tool to discuss the state of affairs or facts of the worlds that is also what philosophy does. This paper describes the contributions of rationalism and empiricism in the field of knowing the truths of language. (shrink)

A presentation and discussion of Gottlob Frege's understanding of language, both natural and artificial, with close attention to his texts.

The use of language models in Web applications and other areas of computing and business have grown significantly over the last five years. One reason for this growth is the improvement in performance of language models on a number of benchmarks — but a side effect of these advances has been the adoption of a “bigger is always better” paradigm when it comes to the size of training, testing, and challenge datasets. Drawing on previous criticisms of this paradigm as applied (...) to large training datasets crawled from pre-existing text on the Web, we extend the critique to challenge datasets custom-created by crowdworkers. We present several sets of criticisms, where ethical and scientific issues in language model research reinforce each other: labour injustices in crowdwork, dataset quality and inscrutability, inequities in the research community, and centralized corporate control of the technology. We also present a new type of tool for researchers to use in examining large datasets when evaluating them for quality. (shrink)

This paper argues that to negate the ontological difference between the natural and the artificial, is not plausible; nor is the reduction of the natural to the artificial or vice versa possible. Except if one intends to empty the semantic content of the terms and notions: “natural” and “artificial.” Most philosophical discussions on Artificial Intelligence (AI) have always been in relation to the human person, especially as it relates to human intelligence, consciousness and/or (...) mind in general. This paper, intends to broaden the conversation, by discussing AI in relation to the notions of “nature” and the “artificial.” This intention is to more critically understand the artificiality in and of artificial intelligence. To achieve this, the notion of “nature” in Aristotle’s Philosophy of Nature, has been employed as an epistemological tool in interrogating the notion of the artificial and the objectives of the science and technology of Artificial Intelligence. (shrink)

Ideas about human consciousness and mental functions will be analyzed and developed using cognitive science information available in the Upanishads, Brahmajnaana, Advaita and Dvaita schools of thought. -/- The analysis and development so done will be used to theorize and give scheme of human language acquisition and communication process clubbing with Sabdabrahma Siddhanta/Sphota Vaada which put forward infrasonic wave oscillator issuing pulses in infrasonic range and are reflected as brain waves. -/- Thus a brain-wave modulation/demodulation model of human language acquisition (...) and communication will be advanced and put forward. Application of this study in mind-machine modeling, natural language comprehension field of artificial intelligence will also be hinted. -/- . (shrink)

We show that critically accumulating "difficult" problems, contradictions and stagnation in modern science have the unified and well-specified mathematical origin in the explicit, artificial reduction of any interaction problem solution to an "exact", dynamically single-valued (or unitary) function, while in reality any unreduced interaction development leads to a dynamically multivalued solution describing many incompatible system configurations, or "realisations", that permanently replace one another in causally random order. We obtain thus the universal concept of dynamic complexity and chaos impossible in (...) unitary mathematics. This huge difference between the unreduced mathematics of real-world dynamics and strongly limited unitary “models” of traditional mathematics inevitably induces a growing series of “unsolvable” problems and other “mysteries” that culminate now in the crisis of the “end of science”, where the stagnating unitary “science” in question includes only the described limitation of the traditional mathematical framework (unfortunately accepted as the unique possible basis for any scientific knowledge). In this brief review, we show that science extension to the unreduced, dynamically multivalued mathematics of the intrinsically complex real-world dynamics provides stagnating problem solutions and reconstitution of the intrinsic causality and unity of science desperately missing in its artificially limited unitary framework. Painful stagnation of the latter should be replaced now by the unlimited new progress of the extended, causally complete knowledge of the universal science of complexity, which provides the urgently needed issue from the current impasse of the global civilisation development. (shrink)

Machine-based automation has long been a key factor in the modern era. However, lately, many people have been shocked by artificial intelligence (AI) applications, such as ChatGPT (OpenAI), that can perform tasks previously thought to be human-exclusive. With recent advances in natural language processing (NLP) technologies, AI can generate written content that is similar to human-made products, and this ability has a variety of applications. As the technology of large language models continues to progress by making use of (...) colossal reservoirs of digital information, AI is becoming more capable of recognizing patterns and associations within given contexts, making it especially helpful in assisting with various professional writing tasks [1]. In this paper, we will discuss several key points regarding the current state and future of AI in academia. Furthermore, since in certain aspects, acts can speak louder than words, we will also present some of our hands-on experiences of working with ChatGPT. (shrink)

Dear Editors, -/- I found the content of your editorials to be highly intriguing [1,2]. Scientific journals are witnessing a growing prevalence of publications related to artificial intelligence (AI). Three letters to the editor were recently published in your journal [3-5]. The renowned journal Nature has dedicated approximately 25 publications solely to the subject of ChatGPT. Moreover, a quick search on Pubmed using the term "ChatGPT" yields around 900 articles, with the vast majority originating in 2023. These statistics underscore (...) the substantial interest of the scientific community in this area. -/- AI, especially the ChatGPT tool and the recent Bard, have faced criticism and been portrayed as significant adversaries of science. It is evident that many authors or researchers, who may not be well-versed in writing, can greatly benefit from these tools, as mentioned earlier. Without taking a contrarian stance, one should consider the potential advantages of such technologies for researchers in less privileged regions, where access to new technologies is limited, and local or regional challenges abound [6]. AI cannot be confounded with other technologies, as it specifically focuses on replicating human-like intelligence and decision-making processes, rather than simply automating tasks or improving performance based on data patterns. -/- The translation into English poses challenges due to the dominance of English in worldwide publications, with over 95% of articles being published in this language, and even reaching 98% in some fields. Although this manuscript was partially translated using AI, it can still benefit researchers from non-native English regions. Even simple tasks like text editing can be problematic for researchers in underprivileged areas. AI can play a crucial role in the evolution of online lectures and classes, providing valuable support for African maxillofacial surgeons who lack the luxury of taking breaks from work to update their knowledge, as they may be the only available option [7]. One of the remarkable features of AI is its ability to discover knowledge gaps. The use of simple tools like reference organizers is rapidly evolving and can become automated or semi-automated through AI. However, we must carefully consider whether we should refrain from relying too heavily on AI in certain cases, as this could be seen as a significant regression. -/- On the other hand, we must be cautious about freely allowing AI tools to circulate in scientific journals and books without proper regulation [8]. Currently, accurately identifying texts generated by AI is challenging, and their effectiveness remains relatively low, at less than 30%. This means that only about 30 out of every 100 texts can be confidently classified as AI-generated. The technology must advance further to increase detection accuracy or at least raise suspicion. Academic journals no longer view authors acknowledging artificial intelligence tools as co-authors in their research favorably. This is crucial to prevent certain unscrupulous individuals, such as "false prophets," charlatans, and flat-earthers, from infiltrating the realm of science, potentially impeding the progress of serious research conducted by professionals dedicated to advancing humanity through science. -/- It is essential to keep in mind that AI does not generate anything novel. Human authors can not be fully substituted [9]. In research involving groundbreaking concepts, innovations, case reports, or technical notes, the use of AI tends to be less frequent due to its lack of capability in creating innovative outcomes. On the other hand, reviews, whether narrative, systematic, or scoping, are based on existing publications. To address potential fraudulent practices, editors, reviewers, and journals themselves should exercise greater vigilance and apply more stringent filters for this type of publication. -/- An article from over a decade ago already discussed the professions most susceptible to replacement by computerization [10]. Maxillofacial surgeons, physicians, dentists, and psychologists are among the professions that are less likely to be displaced. This position can be attributed to the significant level of patient-professional interaction, the development of specific manual skills over time, and the ability to make adaptable decisions during procedures. These intricacies pose formidable challenges for AI to grasp, regardless of its level of advancement. -/- We should maintain a composed yet vigilant stance at this juncture. Just as Portuguese navigators feared encountering serpents and sea monsters when they ventured into uncharted waters, the uncertainty and novelty of AI can evoke apprehension in us all. Nonetheless, we must embrace the benefits that AI can offer while imposing strict regulations and appropriate penalties to prevent any potential abuses carried out "in the name of science." -/- Sincerely yours,. (shrink)

One of the most important abilities we have as humans is the ability to think about number. In this chapter, we examine the question of whether there is an essential connection between language and number. We provide a careful examination of two prominent theories according to which concepts of the positive integers are dependent on language. The first of these claims that language creates the positive integers on the basis of an innate capacity to represent real numbers. The second claims (...) that language’s function is to integrate contents from modules that humans share with other animals. We argue that neither model is successful. (shrink)

A generative grammar for a language L generates one or more syntactic structures for each sentence of L and interprets those structures both phonologically and semantically. A widely accepted assumption in generative linguistics dating from the mid-60s, the Generative Grammar Hypothesis , is that the ability of a speaker to understand sentences of her language requires her to have tacit knowledge of a generative grammar of it, and the task of linguistic semantics in those early days was taken to be (...) that of specifying the form that the semantic component of a generative grammar must take. Then in the 70s linguistic semantics took a curious turn. Without rejecting GGH, linguists turned away from the task of characterizing the semantic component of a generative grammar to pursue instead the Montague-inspired project of providing for natural languages the same kind of model-theoretic semantics that logicians devise for the artificial languages of formal systems of logic, and “formal semantics” continues to dominate semantics in linguistics. This essay argues that the sort of compositional meaning theory that would verify GGH would not only be quite different from the theories formal semanticists construct, but would be a more fundamental theory that supersedes those theories in that it would explain why they are true when they are true, but their truth wouldn’t explain its truth. Formal semantics has undoubtedly made important contributions to our understanding of such phenomena as anaphora and quantification, but semantics in linguistics is supposed to be the study of meaning. This means that the formal semanticist can’t be unconcerned that the kind of semantic theory for a natural language that interests her has no place in a theory of linguistic competence; for if GGH is correct, then the more fundamental semantic theory is the compositional meaning theory that is the semantic component of the internally represented generative grammar, and if that is so, then linguistic semantics has so far ignored what really ought to be its primary concern. (shrink)

Between the end of the nineteenth century and the first twenty years of the twentieth century, stimulated by the impetuous development of logical studies and taking inspiration from Leibniz's idea of a characteristica universalis, the three founding fathers of the analytic tradition in philosophy, i.e., Frege, Russell, and Wittgenstein, started to talk of a logically perfect language, as opposed to natural languages, all feeling that the latter were inadequate to their (different) philosophical purposes. In the second half of (...) the twentieth century, however, the very idea of a logically perfect language ceased for various reasons to seem attractive to analytic philosophers. Thus, it might appear that this idea could be classified together with the many other bizarre ideas that from time to time surface in the history of philosophy-an idea that perhaps had a beneficial impact on the development of twentieth century logic, but which can now be put to rest. In this brief note, I contend that this conclusion may be too hasty. Indeed, if a well-known empirical hypothesis advanced in 1975 by Jerry Fodor turns out to be true, then there is a logically perfect language, after all. More precisely, I argue that, if it exists, Fodor's language of thought possesses the main characteristics a logically perfect language is required to have. (shrink)

The appearance of openly accessible Artificial Intelligence Applications such as Large Language Models, nowadays capable of almost human-level performances in complex reasoning tasks had a tremendous impact on public opinion. Are we going to be "replaced" by the machines? Or - even worse - "ruled" by them? The behavior of these systems is so advanced they might almost appear "alive" to end users, and there have been claims about these programs being "sentient". Since many of our relationships of power (...) and social dominance are apparently based on intelligence, where the "smarter" seems to prevail on the "dumber" (as we see for example with livestock farming, or with the distribution of salaries according to formal and intellectual qualifications etc.) the emergence of programs and AI applications of this level of sophistication brings us to the question "what will AI do to us?". Our error is to frame Artificial Intelligence in the same way we frame Natural Intelligence and mistakenly address it as we would do with biological life, even applying to it the same biopolitical categories. We here try to reverse the common arguments in relationship between life and intelligence and show that our progresses in Artificial Intelligence might have no immediate consequence for the topic of life as such, as life and intelligence are well separated issues, and - even when we would like to somehow fuse them together, we are very far away from having an "Artificial Life" to the degree of complexity and effectiveness we have with "Artificial Intelligence". (shrink)

Yoshimi has attempted to defuse my argument concerning the identification of network abstraction with empiricist abstraction - thus entailing psychologism - by claiming that the argument does not generalize from the example of simple feed-forward networks. I show that the particular details of networks are logically irrelevant to the nature of the abstractive process they employ. This is ultimately because deep artificial neural networks (ANNs) and dynamical systems theory applied to the mind (DST) are both associationisms - that is, (...) empiricist theories that derive the principles of thought from the causal history of the organism/system. On this basis, I put forward a new aspect of the old argument by noting that ANNs & DST are the causal bases of the phenomena of passive synthesis, whereas the language of thought hypothesis (LOT) and the symbolic computational theory of mind (CTM) are the causal bases of the phenomena of active synthesis. If the phenomena of active synthesis are not distinct in kind from and are thus reducible to those of passive synthesis, psychologism results. Yoshimi’s program, insofar as it denies this fundamental phenomenological distinction, is revealed to be the true anti-pluralist program, by essentially denying the causal efficacy of the mechanistic foundations of active synthesis by referring phenomenology exclusively to associationism for its causal foundation. (shrink)

In this article, the results of conceptualization of the definition of mind as an object of interdisciplinary applied research are described. The purpose of the theoretical analysis is to generate a methodological discourse suitable for a functional understanding of the mind in the context of the problem of natural language processing as one of the components of developments in the field of artificial intelligence. The conceptual discourse was realized with the help of the author's method of structural-ontological analysis, (...) and developed in the mainstream of the system-methodological tradition of the school of G.P. Shchedrovitsky and intended for descriptive research of subject areas of interdisciplinary objects of scientific study. As a result of the structural and ontological analysis of the super-system, the relevant place and role of the directly studied system (mind) are determined, and its primary process and material are localized and structural and functional connections are described. At the basic level, the mind is conceptualized as an energy process unfolding in a spatio-temporal environment and accompanied by archetypal structuring of neural impulses into images. The genesis of the system is separately analyzed by constructing a structural-ontological matrix that reflects the initial stage of the development of the mind. The primary process is concretized with the help of hetero- and homeostatic dichotomy, and also the most significant features of the consistent transformation of the material of the system and its ascent to verbal morphology are described. The structural-ontological comparison of the functioning of the verbal intelligence with the preverbal level has been carried out. The transformation of neural impulses of needs into words, as verbal units fixing semiotic values is analyzed. Structural-ontological connections that determine the reactive and prospective characteristics of the functioning of the system are disclosed. The position of the chronological primacy of "semiotic readiness" for language with respect to the debut of the latter as an information-sign model of the environment is argued. The hypothesis of domination at the initial stage of the development of the mind of exopsychic functions over endopsychic ones is formulated. The theoretical substantiation of the hypothesis of the inevitability of the ontology of language in the functional understanding of the mind is given, corresponding structural and ontological arguments are given, including those based on the ideas about the information relationship between affects and needs, according to the views of P.V. Simonov. The arguments are presented in favor of the non-alternative methodology of A. Turing in studies of artificial intelligence. (shrink)

The representation of propositional attitudes (beliefs, desires, etc.) and the analysis of natural-language, propositional-attitude reports presents difficult problems for cognitive science and artificial intelligence. In particular, various representational approaches to attitudes involve the incorrect “imputation,” to cognitive agents, of the use of artificial theory-laden notions. Interesting cases of this problem are shown to occur in several approaches to attitudes. The imputation problem is shown to arise from the way that representational approaches explicate properties and relationships, and in (...) particular from the way they explicate propositional attitudes themselves. Another factor contributing to imputation is the compositional nature of typical semantic approaches to propositional-attitude reports. Some strategies for avoiding undesirable imputation are examined. One of the main conclusions is that the importance of imputations that arise in a representation scheme depends strongly on the use to which the scheme is put -- on whether, for instance, the scheme is used as part of a formal, objective account of natural language, or is used rather as a representational tool within an agent. (shrink)

Logic has its roots in the study of valid argument, but while traditional logicians worked with natural language directly, modern approaches first translate natural arguments into an artificial language. The reason for this step is that some artificial languages now have very well developed inferential systems. There is no doubt that this is a great advantage in general, but for the study of natural reasoning it is a drawback that the original linguistic forms get (...) lost in translation. An alternative approach would be to develop a general theory of the natural logic behind human reasoning and human information processing by studying formal logics that operate directly on linguistic representations. That this is possible we will try to make plausible in this paper. It will turn out that one level of representation, that of Logical Form, can meaningfully be identified with the language of an existing and well-understood logic, a restricted form of the theory of types. It is not difficult to devise inference systems for this language, and it is thus possible to study reasoning systems that are based directly on language. (shrink)