As part of a larger research study, we present model language for disclosing financial interests in clinical research to potential research participants, and we describe the empirical basis and theoretical assumptions used in developing the language. The empirical process for creating appropriate disclosure language resulted in a generic disclosure statement for cases in which no risk to participants’ welfare or the scientific integrity of the research is expected, and nine more specific disclosure statements for cases in which (...) some risk is expected. The disclosure statements are not meant to be canonical, but were instead designed to reflect the typical situations in which disclosure of financial interest might be considered by an institutional review board or conflict of interest committee. Individual institutions could modify key phrases to suit their purposes, and others could use the language in future empirical work on informed consent to better refine the options for disclosing financial interests in clinical research. (shrink)

Current work in language models (LMs) helps us speed up or even skip thinking by accelerating and automating cognitive work. But can LMs help us with critical thinking -- thinking in deeper, more reflective ways which challenge assumptions, clarify ideas, and engineer new concepts? We treat philosophy as a case study in critical thinking, and interview 21 professional philosophers about how they engage in critical thinking and on their experiences with LMs. We find that philosophers do not find LMs (...) to be useful because they lack a sense of selfhood (memory, beliefs, consistency) and initiative (curiosity, proactivity). We propose the selfhood-initiative model for critical thinking tools to characterize this gap. Using the model, we formulate three roles LMs could play as critical thinking tools: the Interlocutor, the Monitor, and the Respondent. We hope that our work inspires LM researchers to further develop LMs as critical thinking tools and philosophers and other 'critical thinkers' to imagine intellectually substantive uses of LMs. (shrink)

Mental model construction is supposed to be a useful cognitive devise for learning. Beyond human capacity of constructing mental models, scientists construct complex explanations about phenomena, named scientific or theoretical models. In this work we revisit three vissions: the first one concern about the polisemic term “model”. Our proposal is to discriminate between “mental models” and “explicit models”, being the former those “imaginistic” ideas constructed in scientists’—o teachers—minds, and the latter those teaching devices expressed in different languages that tend to (...) communicate any “scientific model”. From this point of view, the class is considered a place where teachers’ mental models should be learned by novice students by decoding their teaching devices which are expressed in different languages. Other proposal of this work claims to distinguish the term “representation” with respect to its artistical or instrumental origin, highlighting that they are types of teaching devices and that artistical representations are always analogies. Finally, data about the construction of freshmen’s wrong mental models related to the use of the analogy between the chemical combustion and the global process of cellular respiration from glucose is presented to reinforce previous epistemological reflections. (shrink)

In his Memoir of Wittgenstein Professor Malcolm describes the occasion on which, as far as he knows, the idea that as an activity language is a game, or that ‘games are played with words’, first occurred to Wittgenstein. Wittgenstein was passing a playing field where there was a game of football in progress. As he watched the game, the thought suddenly flashed into his mind, ‘We play games with words !’ This account may be compared with that given by (...) Professor von Wright, on the basis of Wittgenstein's own report, of the way in which he was with equal suddenness struck with the thought that a proposition is a ‘picture’. He was looking at a diagram which showed the positions of certain persons and vehicles involved in an accident—‘ That is a proposition!’ was the thought which on this earlier occasion darted into Wittgenstein's mind. Thus, it seems, was germinated the theory developed in the Tractatus that propositions, that is to say, meaningful propositions, are ‘pictures of reality’. (shrink)

We discuss potential biorisks from large language models (LLMs). AI assistants based on LLMs such as ChatGPT have been shown to significantly reduce barriers to entry for actors wishing to synthesize dangerous, potentially novel pathogens and chemical weapons. The harms from deploying such bioagents could be further magnified by AI-assisted misinformation. We endorse several policy responses to these dangers, including prerelease evaluations of biomedical AIs by subject-matter experts, enhanced surveillance and lab screening procedures, restrictions on AI training data, and (...) access controls on biomedical AI. We conclude with a suggestion about future research directions in bioethics. (shrink)

Ruth Millikan is well known for having developed a strikingly original way for philosophers to seek understanding of mind and language, which she sees as biological phenomena. She now draws together a series of groundbreaking essays which set out her approach to language. Guiding the work of most linguists and philosophers of language today is the assumption that language is governed by prescriptive normative rules. Millikan offers a fundamentally different way of viewing the partial regularities that (...) language displays, comparing them to biological norms that emerge from natural selection. This yields novel and quite radical consequences for our understanding of the nature of public linguistic meaning, the process of language understanding, how children learn language, and the semantics/pragmatics distinction. (shrink)

This book compiles and reviews the most prominent linguistic theories into a single source that serves as an essential reference for future solutions to one of the most important challenges of our age.

Are LLMs cultural technologies like photocopiers or printing presses, which transmit information but cannot create new content? A challenge for this idea, which we call bibliotechnism, is that LLMs generate novel text. We begin with a defense of bibliotechnism, showing how even novel text may inherit its meaning from original human-generated text. We then argue that bibliotechnism faces an independent challenge from examples in which LLMs generate novel reference, using new names to refer to new entities. Such examples could be (...) explained if LLMs were not cultural technologies but had beliefs, desires, and intentions. According to interpretationism in the philosophy of mind, a system has such attitudes if and only if its behavior is well explained by the hypothesis that it does. Interpretationists may hold that LLMs have attitudes, and thus have a simple solution to the novel reference problem. We emphasize, however, that interpretationism is compatible with very simple creatures having attitudes and differs sharply from views that presuppose these attitudes require onsciousness, sentience, or intelligence (topics about which we make no claims). (shrink)

In a recent letter, Dillion et. al (2023) make various suggestions regarding the idea of artificially intelligent systems, such as large language models, replacing human subjects in empirical moral psychology. We argue that human subjects are in various ways indispensable.

Large Language Models (LLMs) have been transformative. They are pre-trained foundational models that are self-supervised and can be adapted with fine tuning to a wide range of natural language tasks, each of which previously would have required a separate network model. This is one step closer to the extraordinary versatility of human language. GPT-3 and more recently LaMDA can carry on dialogs with humans on many topics after minimal priming with a few examples. However, there has been (...) a wide range of reactions and debate on whether these LLMs understand what they are saying or exhibit signs of intelligence. This high variance is exhibited in three interviews with LLMs reaching wildly different conclusions. A new possibility was uncovered that could explain this divergence. What appears to be intelligence in LLMs may in fact be a mirror that reflects the intelligence of the interviewer, a remarkable twist that could be considered a Reverse Turing Test. If so, then by studying interviews we may be learning more about the intelligence and beliefs of the interviewer than the intelligence of the LLMs. As LLMs become more capable they may transform the way we interact with machines and how they interact with each other. Increasingly, LLMs are being coupled with sensorimotor devices. LLMs can talk the talk, but can they walk the walk? A road map for achieving artificial general autonomy is outlined with seven major improvements inspired by brain systems. LLMs could be used to uncover new insights into brain function by downloading brain data during natural behaviors. (shrink)

[This is an edited version of a keynote talk at the conference on Neural Information Processing Systems (NeurIPS) on November 28, 2022, with some minor additions and subtractions.] -/- There has recently been widespread discussion of whether large language models might be sentient or conscious. Should we take this idea seriously? I will break down the strongest reasons for and against. Given mainstream assumptions in the science of consciousness, there are significant obstacles to consciousness in current models: for example, (...) their lack of recurrent processing, a global workspace, and unified agency. At the same time, it is quite possible that these obstacles will be overcome in the next decade or so. I conclude that while it is somewhat unlikely that current large language models are conscious, we should take seriously the possibility that successors to large language models may be conscious in the not-too-distant future. (shrink)

Theories of language production have long been expressed as connectionist models. We outline the issues and challenges that must be addressed by connectionist models of lexical access and grammatical encoding, and review three recent models. The models illustrate the value of an interactive activation approach to lexical access in production, the need for sequential output in both phonological and grammatical encoding, and the potential for accounting for structural effects on errors and structural priming from learning.

To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language. The complexity of human language has hampered progress because studies of language–especially those involving computational modeling–have only been able to deal with small fragments of our linguistic skills. We suggest that the most recent generation of Large Language Models (LLMs) might finally provide the (...) computational tools to determine empirically how much of the human language ability can be acquired from linguistic experience. LLMs are sophisticated deep learning architectures trained on vast amounts of natural language data, enabling them to perform an impressive range of linguistic tasks. We argue that, despite their clear semantic and pragmatic limitations, LLMs have already demonstrated that human-like grammatical language can be acquired without the need for a built-in grammar. Thus, while there is still much to learn about how humans acquire and use language, LLMs provide full-fledged computational models for cognitive scientists to empirically evaluate just how far statistical learning might take us in explaining the full complexity of human language. (shrink)

This research attempts to understand howchildren learn to use language. Instead ofusing syntax-based grammar rules to model thedifferences between children''s language andadult language, as has been done in the past, anew model is proposed. In the new researchmodel, children acquire language by listeningto the examples of speech that they hear intheir environment and subsequently use thespeech examples that have been previously heardin similar contextual situations. A computermodel is generated to simulate this new modelof language acquisition. (...) The MALL computerprogram will listen to examples of humanspeech, as would occur around a child, and thentry to use these examples in new situationsthat are similar to the contextual situationsin which the language examples were heard. This will provide a better understanding of howchildren learn to use language and howeducators can assist or improve the languagelearning process by providing required examplesof speech or by helping children to develop abetter understanding of similarities betweenvarious contexts. (shrink)

Language is a collaborative act: To communicate successfully, speakers must generate utterances that are not only semantically valid but also sensitive to the knowledge state of the listener. Such sensitivity could reflect the use of an “embedded listener model,” where speakers choose utterances on the basis of an internal model of the listener's conceptual and linguistic knowledge. In this study, we ask whether parents’ spatial descriptions incorporate an embedded listener model that reflects their children's understanding of spatial relations and (...) spatial terms. Adults described the positions of targets in spatial arrays to their children or to the adult experimenter. Arrays were designed so that targets could not be identified unless spatial relationships within the array were encoded and described. Parents of 3–4-year-old children encoded relationships in ways that were well-matched to their children's level of spatial language. These encodings differed from those of the same relationships in speech to the adult experimenter. In contrast, parents of individuals with severe spatial impairments did not show clear evidence of sensitivity to their children's level of spatial language. The results provide evidence for an embedded listener model in the domain of spatial language and indicate conditions under which the ability to model listener knowledge may be more challenging. (shrink)

How does our language relate to reality? This is a question that is especially pertinent in set theory, where we seem to talk of large infinite entities. Based on an analogy with the use of models in the natural sciences, we argue for a threefold correspondence between our language, models, and reality. We argue that so conceived, the existence of models can be underwritten by a weak notion of existence, where weak existence is to be understood as existing (...) in virtue of language. (shrink)

Tarskian model theory is almost universally understood as a formal counterpart of the preformal notion of semantics, of the “linkage between words and things”. The wide-spread opinion is that to account for the semantics of natural language is to furnish its settheoretic interpretation in a suitable model structure; as exemplified by Montague 1974.

This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural language text. Most previous work has focused on maximizing likelihood according to generative PCFG models. In contrast, we employ a simpler probabilistic model over trees based directly on constituent identity and linear context, and use an EM-like iterative procedure to induce structure. This method produces much higher quality analyses, giving the best published results on the ATIS dataset.

Despite strong evidence that worry is a verbal process, studies examining linguistic features in individuals with generalised anxiety disorder (GAD) are lacking. The aim of the present study is to investigate language use in individuals with GAD and controls based on GAD and worry theoretical models. More specifically, the degree to which linguistic elements of the avoidance and intolerance of uncertainty worry models can predict diagnostic status was analysed. Participants were 19 women diagnosed with GAD and 22 control women (...) and their children. After participating in a diagnostic semi-structured interview, dyads engaged in a free-play interaction where mothers' language sample was collected. Overall, the findings provided evidence for distinctive linguistic features of individuals with GAD. That is, after controlling for the effect of demographic variables, present tense, future tense, prepositions and number of questions correctly classified those with GAD and controls such that a considerable amount of the variance in diagnostic status was explained uniquely by language use. Linguistic confirmation of worry models is discussed. (shrink)

Landgrebe and Smith :2061–2081, 2021) present an unflattering diagnosis of recent advances in what they call language-centric artificial intelligence—perhaps more widely known as natural language processing: The models that are currently employed do not have sufficient expressivity, will not generalize, and are fundamentally unable to induce linguistic semantics, they say. The diagnosis is mainly derived from an analysis of the widely used Transformer architecture. Here I address a number of misunderstandings in their analysis, and present what I take (...) to be a more adequate analysis of the ability of Transformer models to learn natural language semantics. To avoid confusion, I distinguish between inferential and referential semantics. Landgrebe and Smith ’s analysis of the Transformer architecture’s expressivity and generalization concerns inferential semantics. This part of their diagnosis is shown to rely on misunderstandings of technical properties of Transformers. Landgrebe and Smith also claim that referential semantics is unobtainable for Transformer models. In response, I present a non-technical discussion of techniques for grounding Transformer models, giving them referential semantics, even in the absence of supervision. I also present a simple thought experiment to highlight the mechanisms that would lead to referential semantics, and discuss in what sense models that are grounded in this way, can be said to understand language. Finally, I discuss the approach Landgrebe and Smith advocate for, namely manual specification of formal grammars that associate linguistic expressions with logical form. (shrink)

This letter explores the intricate historical and contemporary links between large language models (LLMs) and cognitive science through the lens of information theory, statistical language models, and socioanthropological linguistic theories. The emergence of LLMs highlights the enduring significance of information‐based and statistical learning theories in understanding human communication. These theories, initially proposed in the mid‐20th century, offered a visionary framework for integrating computational science, social sciences, and humanities, which nonetheless was not fully fulfilled at that time. The subsequent (...) development of sociolinguistics and linguistic anthropology, especially since the 1970s, provided critical perspectives and empirical methods that both challenged and enriched this framework. This letter proposes that two pivotal concepts derived from this development, metapragmatic function and indexicality, offer a fruitful theoretical perspective for integrating the semantic, textual, and pragmatic, contextual dimensions of communication, an amalgamation that contemporary LLMs have yet to fully achieve. The author believes that contemporary cognitive science is at a crucial crossroads, where fostering interdisciplinary dialogues among computational linguistics, social linguistics and linguistic anthropology, and cognitive and social psychology is in particular imperative. Such collaboration is vital to bridge the computational, cognitive, and sociocultural aspects of human communication and human−AI interaction, especially in the era of large language and multimodal models and human‐centric Artificial Intelligence (AI). (shrink)

Computational models of memory are often expressed as hierarchic sequence models, but the hierarchies in these models are typically fairly shallow, reflecting the tendency for memories of superordinate sequence states to become increasingly conflated. This article describes a broad-coverage probabilistic sentence processing model that uses a variant of a left-corner parsing strategy to flatten sentence processing operations in parsing into a similarly shallow hierarchy of learned sequences. The main result of this article is that a broad-coverage model with constraints on (...) hierarchy depth can process large newspaper corpora with the same accuracy as a state-of-the-art parser not defined in terms of sequential working memory operations. (shrink)

Large language models (LLMs) have now entered the realm of medical ethics. In a recent study, Balaset alexamined the performance of GPT-4, a commercially available LLM, assessing its performance in generating responses to diverse medical ethics cases. Their findings reveal that GPT-4 demonstrates an ability to identify and articulate complex medical ethical issues, although its proficiency in encoding the depth of real-world ethical dilemmas remains an avenue for improvement. Investigating the integration of LLMs into medical ethics decision-making appears to (...) be an interesting avenue of research. However, despite the promising trajectory of LLM technology in medicine, it is crucial to exercise caution and refrain from attributing their expertise to medical ethics. Our thesis follows an examination of the nature of expertise and the epistemic limitations that affect LLM technology. As a result, we propose two more fitting applications of LLMs in medical ethics: first, as tools for mining electronic health records or scientific literature, thereby supplementing evidence for resolving medical ethics cases, and second, as educational platforms to foster ethical reflection and critical thinking skills among students and residents. The integration of LLMs in medical ethics, while promising, requires careful consideration of their epistemic limitations. Consequently, a well-considered definition of their role in ethically sensitive decision-making is crucial. (shrink)

Ruth Garrett Millikan is one of the most important thinkers in philosophy of mind and language of the current generation. Across a number of seminal books, and in the company of theorists such as Jerry Fodor and Fred Dretske, she has championed a wholly naturalistic, scientific understanding of content, whether of thought or words. Many think that naturalism about meaning has found its most defensible form in her distinctively “teleological” approach, and in Language: A Biological Model she continues (...) the expansion and defence of her general position, turning her attention to the role of rule-following in language and the ensuing understanding of issues that lie at the boundary of semantics and pragmatics. (shrink)

A traditional aspect of model theory has been the interplay between formal languages and mathematical structures. This dissertation is concerned, in particular, with the relationship between the languages of relevant logic and Routley-Meyer models. One fundamental question is treated: what is the expressive power of relevant languages in the Routley-Meyer framework? In the case of finitary relevant propositional languages, two answers are provided. The first is that finitary propositional relevant languages are the fragments of first order logic preserved under relevant (...) directed bisimulations. The second is that, when we restrict our attention to what can be labelled as De Morgan models, we can obtain an analogue of Lindström's theorem for finitary propositional relevant languages. Furthermore, it is shown that a preservation theorem characterizing the expressive power of infinitary relevant languages in classical infinitary languages follows as a consequence of an interpolation theorem for classical infinitary logic. In addition, algebraic characterizations of the classes of Routley-Meyer models axiomatizable in relevant propositional languages, incompactness of infinitary relevant propositional languages and the expressive power of quantificational relevant languages are discussed. A final chapter is devoted to the study of relevant languages as second order frame languages. In particular we devote our attention to the problem of which properties expressible by relevant languages are elementary and which are not. An algebraic characterization of such elementary properties together with some examples of non first order properties axiomatizable in relevant logic are given. Finally, a Sahlqvist-van Benthem algorithm showing that relevant formulas with a certain syntactic form express calculable first order properties at the level of frames is established. (shrink)

Guiding the work of most linguists and philosophers of language today is the assumption that language is governed by rules. This volume presents a different way of viewing the partial regularities that language displays, the way they express norms and conventions. It argues that the central norms applying to language are non-evaluative; they are more like those norms of function and behavior that account for the survival and proliferation of biological species. Specific linguistic forms survive and (...) are reproduced together with cooperative hearer responses because some portion of the time these patterns of production and response benefit both speakers and hearers. What needs to be reproduced, however, for a given language to survive is not specific conceptual rules or inference patterns, but only satisfaction conditions concerning distal objects and properties, and essential elements of hearer response. Thus, the psychological processes that support the use of proper names, of words for kinds, properties and so forth, need to be examined anew, resulting in a fairly uncompromising rejection of conceptual analysis as a tool in philosophy. Further results concern the distinction between the propositional content and the force of a linguistic utterance and a new description of illocutionary acts. It turns out that neither the intentionality of thought nor the intentionality of language is derived from the other. Also, the processes involved in understanding language are best modeled as a form of direct perception of the world parallel, for example, to perception mediated by the natural signs contained in structured light, and results in a radically new description of how children learn language. (shrink)

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical (...) developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception. (shrink)

This unique book presents a comprehensive and rigorous treatment of the theory of computability which is introductory yet self-contained. It takes a novel approach by looking at the subject using computation models rather than a limitation orientation, and is the first book of its kind to include software. Accompanying software simulations of almost all computational models are available for use in conjunction with the text, and numerous examples are provided on disk in a user-friendly format. Its applications to computer science (...) itself include interesting links to programming language theory, compiler design theory, and algorithm design. The software, numerous examples, and solutions make this book ideal for self-study by computer scientists and mathematicians alike. (shrink)

The SDML programming language which is optimized for modelling multi-agent interaction within articulated social structures such as organizations is described with several examples of its functionality. SDML is a strictly declarative modelling language which has object-oriented features and corresponds to a fragment of strongly grounded autoepistemic logic. The virtues of SDML include the ease of building complex models and the facility for representing agents flexibly as models of cognition as well as modularity and code reusability.

Monogenesis of language is widely accepted, but the conventional argument seems to be mistaken; a simple probabilistic model shows that polygenesis is likely. Other prehistoric inventions are discussed, as are problems in tracing linguistic lineages. Language is a system of representations; within such a system, words can evoke complex and systematic responses. Along with its social functions, language is important to humans as a mental instrument. Indeed, the invention of language,that is the accumulation of symbols to (...) represent emotions, objects, and acts may be the most important event in human evolution, because so many developments follow from it. For example, Edward Sapir speculated that some embryonic form of language must have been available to early man to help him fashion tools from stone (Sapir,1921). Sophisticated biface stone tools date to early Homo erectus some 1.5 million years ago, suggesting a similar age for language. This paper considers whether the invention of language occurred at only one pre-historic site or at several sites. In other words, did language emerge by monogenesis or polygenesis? Early thinkers believed in monogenesis, against a background of divine creation. Perhaps the best known account is the biblical story of Adam giving names to plants and animals in the Garden of Eden. Similar legends are found among many peoples. Modern linguists too assume monogenesis, but on probabilistic grounds (see, for instance, Southworth and Daswani, 1974, p.314). The argument seems to be that the invention of language is an extremely unlikely event, because symbolization involves abstraction and requires synchronized insight by several individuals; therefore, the probability of occurrence at more than one site must be vanishingly small. We have found no explicit quantitative treatment of this question in the literature, but the underlying logic has to be the multiplication of probabilities. If p is small at one site,then p.p for two sites is smaller still, and so on. This reasoning is false, as we show here. The fallacy lies in the focus on two particular sites rather than consideration of all pairs of sites. (shrink)

Large language models (LLMs) represent the currently most relevant incarnation of artificial intelligence with respect to the future fate of democratic governance. Considering their potential, this paper seeks to answer a pressing question: Could LLMs outperform humans as expert advisors to democratic assemblies? While bearing the promise of enhanced expertise availability and accessibility, they also present challenges of hallucinations, misalignment, or value imposition. Weighing LLMs’ benefits and drawbacks compared to their human counterparts, I argue for their careful integration to (...) augment democracy’s ability to address complex policy issues. The paper posits that time-tested democratic procedures like deliberation and aggregation by voting provide safeguards effective against both human and machine advisor imperfections. Additional protective measures include custom LLM training for the advisory role, boosting representatives’ competencies in query formulation, or implementation of adversarial proceedings in which LLM advisors could debate each other and provide dissenting opinions. These could further mitigate the risks that LLMs present in advisory roles and empower human decision-makers toward increased autonomy and quality of their collective choices. My conceptual exploration offers a roadmap for the co-evolution of AI and democratic institutions, setting the stage for an empirical research agenda to finetune the implementation specifics. (shrink)

Mentalization describes the process through which we understand the mental states of oneself and others. In this paper, I present a computational semiotic model of mentalization and illustrate it through a worked-out example. The model draws on classical semiotic ideas, such as abductive inference and hypostatic abstraction, but pours them into new ideas and tools from natural language processing, machine learning, and neural networks, to form a novel model of language-mediated-mentalization.

Large language models (LLMs) such as ChatGPT or Google’s Bard have shown significant performance on a variety of text-based tasks, such as summarization, translation, and even the generation of new...

The semantic view on theories has been much in vogue over four decades as the successor of the syntactic view. In the present paper, I take issue with this approach by arguing that theories and models must be separated and that a theory should be considered to be a linguistic systems consisting of a vocabulary and a set of rules for the use of that vocabulary.

The essay deals with the mechanism of interpretation for legal metaphorical expressions. Firstly, it points out the perspective the cognitive approach induced about legal metaphors; then it suggests that this perspective gains in plausibility when a new bilateral model of language understanding is endorsed. A possible sketch of the meaning-making procedure for legal metaphors, compatible with this new model, is then proposed, and illustrated with some examples built on concepts belonging to the Italian Civil Code. The insights the bilateral (...) model of understanding provides are compared with the practice followed by legal communities for dealing with the metaphorical expressions they coin and use. (shrink)

If Large Language Models can make real scientific contributions, then they can genuinely use language, be systematically wrong, and be held responsible for their errors. AI models which can make scientific contributions thereby meet the criteria for scientific authorship.

This paper proposes a feature extraction algorithm based on the maximum entropy phrase reordering model in statistical machine translation in language learning machines. The algorithm can extract more accurate phrase reordering information, especially the feature information of reversed phrases, which solves the problem of imbalance of feature data during maximum entropy training in the original algorithm, and improves the accuracy of phrase reordering in translation. In the experiment, they were combined with linguistic features such as parts of speech, words, (...) and syntactic features extracted by using the syntax analyzer, and the maximum entropy classifier was used to predict translation errors, and the experimental verification was performed on the Chinese-English translation data set and compared. The experimental results show that different word posterior probabilities have a significant impact on the classification error rate, and the combination of linguistic features based on the word posterior probability can significantly reduce the classification error rate and improve the translation error prediction performance. (shrink)

In today’s society, models of God are challenged to account for more than the postmodern context in which Western Christianity finds itself; they should also consider the reality of religious pluralism. Non-monotheistic religions present a particular challenge to Western theological and philosophical God-modeling because they require a model of Gods. This paper uses an African traditional religion as a case study to problematize the effects of monotheism on philosophical models of God. The desire to uphold the image of a singular (...) God tends to invalidate religious experiences that deviate from a given scientifically-verifiable norm. It also mischaracterizes the concept of divinity in religions that maintain divine multiplicity. That is, scholars of African traditional religions affirm that “polytheism” is not an accurate naming of their traditions; rather these religions affirm a community of gods. I propose a Whiteheadian process model that describes a community of gods that has active interaction with the temporal world. Such a model not only broadens conversations of religious pluralism for Western-trained religious scholars, but also acknowledges the Western context in which many practitioners of African traditional religions live. (shrink)

We question the widespread assumption that linguistic theory should guide the formulation of mechanistic accounts of human language processing. We develop a pseudo‐linguistic theory for the domain of linguistic stress, based on observation of the learning behavior of a perceptron exposed to a variety of stress patterns. There are significant similarities between our analysis of perception stress learning and metrical phonology, the linguistic theory of human stress. Both approaches attempt to identify salient characteristics of the stress systems under examination (...) without reference to the workings of the underlying processor. Our theory and computer simulations exhibit some strikingly suggestive correspondences with metrical theory. We show, however, that our high‐level pseudo‐linguistic account bears no causal relation to processing in the perception, and provides little insight into the nature of this processing. Because of the persuasive similarities between the nature of our theory and linguistic theorizing, we suggest that linguistic theory may be in much the same position. Contrary to the usual assumption, it may not provide useful guidance in attempts to identify processing mechanisms underlying human language. (shrink)