Biases and misunderstanding stemming from pre-training in Generative Pre-Trained Transformers are more likely for users of underrepresented English varieties, since the training dataset favors dominant Englishes (e.g., American English). We investigate (potential) bias in GPT-4 when it interacts with Trinidadian English Creole (TEC), a non-hegemonic English variety that partially overlaps with standardized English (SE) but still contains distinctive characteristics. (1) Comparable responses: we asked GPT-4 18 questions in TEC and SE and compared the content and detail of the responses. (2) (...) Accurate translation: we assessed how accurate and authentic 29 TEC and 34 SE translations were. (3) Language knowledge and attitudes: we asked what language the prompts were written in and categorized the responses and examined any language attitudes that were exhibited. Content and detail in prompts were comparable. The model was proficient at translating TEC pronouns and many grammatical categories. It was weaker at processing spelling and vocabulary items. In addition, it produced several inauthentic features. Only 39% of TEC-generated sentences were fully grammatical. While GPT-4 was perfect at identifying SE, it was 21% accurate at identifying TEC, which it sometimes classified as English with “errors” and “corrected”. GPT-4’s scope of use is limited for non-hegemonic English users. It is problematic that some of its analyses perpetuate bias against underrepresented Englishes. Increased research on lesser-documented Englishes is necessary and we anticipate that this problem affects dialects of other languages. We intend to partner with Trinidadian stakeholders to train GPT-4 in the future. (shrink)

Perhaps the most widely touted of GPT-4’s at-launch, zero-shot capabilities has been its reported 90th-percentile performance on the Uniform Bar Exam. This paper begins by investigating the methodological challenges in documenting and verifying the 90th-percentile claim, presenting four sets of findings that indicate that OpenAI’s estimates of GPT-4’s UBE percentile are overinflated. First, although GPT-4’s UBE score nears the 90th percentile when examining approximate conversions from February administrations of the Illinois Bar Exam, these estimates are heavily skewed towards repeat test-takers (...) who failed the July administration and score significantly lower than the general test-taking population. Second, data from a recent July administration of the same exam suggests GPT-4’s overall UBE percentile was below the 69th percentile, and $$\sim$$ ∼ 48th percentile on essays. Third, examining official NCBE data and using several conservative statistical assumptions, GPT-4’s performance against first-time test takers is estimated to be $$\sim$$ ∼ 62nd percentile, including $$\sim$$ ∼ 42nd percentile on essays. Fourth, when examining only those who passed the exam (i.e. licensed or license-pending attorneys), GPT-4’s performance is estimated to drop to $$\sim$$ ∼ 48th percentile overall, and $$\sim$$ ∼ 15th percentile on essays. In addition to investigating the validity of the percentile claim, the paper also investigates the validity of GPT-4’s reported scaled UBE score of 298. The paper successfully replicates the MBE score, but highlights several methodological issues in the grading of the MPT + MEE components of the exam, which call into question the validity of the reported essay score. Finally, the paper investigates the effect of different hyperparameter combinations on GPT-4’s MBE performance, finding no significant effect of adjusting temperature settings, and a significant effect of few-shot chain-of-thought prompting over basic zero-shot prompting. Taken together, these findings carry timely insights for the desirability and feasibility of outsourcing legally relevant tasks to AI models, as well as for the importance for AI developers to implement rigorous and transparent capabilities evaluations to help secure safe and trustworthy AI. (shrink)

What is Understanding? This is the first of a series of Chats with OpenAI’s ChatGPT (Chat). The main goal is to obtain Chat’s response to a series of questions about the concept of ’understand- ing’. The approach is a conversational approach where the author (labeled as user) asks (prompts) Chat, obtains a response, and then uses the response to formulate followup questions. David Deutsch’s assertion of the primality of the process / capability of understanding is used as the starting point. (...) Understanding is posited as a major step in the Big Picture description of emergence in the Universe. Understanding follows the emergence of Brains and Minds, culminating in Power Over the Universe. My evaluation suggests that Chat does indeed offer utility, but also has limitations manifested by overly general responses that do not focus tightly on the questions asked. Chat does yield some gems that could be easily missed. The approach used here includes creating Metaprompts, wherein Chat is asked to create more efficient prompts based on initial promptsThis approach appears to hold significant promise, and will likely be of benefit to those exploring various facets of Artificial Intelligence (AI), Large Language Models (LLMs) and designers of Advanced General Intelligence (AGI). Other findings include the need for creating new metrics and ways of discussing intelligence, and various adjacent areas and subprocesses associated with intelligence. Discussions of future directions is also included. (shrink)

This paper investigates the responses of GPT-4, a state-of-the-art AI language model, to ten prominent philosophical paradoxes, and evaluates its capacity to reason and make decisions in complex and uncertain situations. In addition to analyzing GPT-4's solutions to the paradoxes, this paper assesses the model's Theory of Mind (ToM) capabilities by testing its understanding of mental states, intentions, and beliefs in scenarios ranging from classic ToM tests to complex, real-world simulations. Through these tests, we gain insight into AI's potential for (...) social reasoning and its capacity for more sophisticated forms of human-AI interaction. The paper also explores the limitations and biases of AI-generated reasoning and its implications for our comprehension of complex philosophical problems. (shrink)

This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI’s ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors identifying AI-generated content. These submissions were marked by 15 academic staff members alongside genuine student submissions. Although the AI detection tool identified 91% of the experimental submissions as containing AI-generated content, only (...) 54.8% of the content was identified as AI-generated, underscoring the challenges of detecting AI content when advanced prompting techniques are used. When academic staff members marked the experimental submissions, only 54.5% were reported to the academic misconduct process, emphasising the need for greater awareness of how the results of AI detectors may be interpreted. Similar performance in grades was obtained between student submissions and AI-generated content (AI mean grade: 52.3, Student mean grade: 54.4), showing the capabilities of AI tools in producing human-like responses in real-life assessment situations. Recommendations include adjusting the overall strategies for assessing university students in light of the availability of new Generative AI tools. This may include reducing the overall reliance on assessments where AI tools may be used to mimic human writing, or by using AI-inclusive assessments. Comprehensive training must be provided for both academic staff and students so that academic integrity may be preserved. (shrink)

Integrating large language models (LLMs) like GPT-4 into medical ethics is a novel concept, and understanding the effectiveness of these models in aiding ethicists with decision-making can have significant implications for the healthcare sector. Thus, the objective of this study was to evaluate the performance of GPT-4 in responding to complex medical ethical vignettes and to gauge its utility and limitations for aiding medical ethicists. Using a mixed-methods, cross-sectional survey approach, a panel of six ethicists assessed LLM-generated responses to eight (...) ethical vignettes.The main outcomes measured were relevance, reasoning, depth, technical and non-technical clarity, as well as acceptability of GPT-4’s responses. The readability of the responses was also assessed. Of the six metrics evaluating the effectiveness of GPT-4’s responses, the overall mean score was 4.1/5. GPT-4 was rated highest in providing technical (4.7/5) and non-technical clarity (4.4/5), whereas the lowest rated metrics were depth (3.8/5) and acceptability (3.8/5). There was poor-to-moderate inter-rater reliability characterised by an intraclass coefficient of 0.54 (95% CI: 0.30 to 0.71). Based on panellist feedback, GPT-4 was able to identify and articulate key ethical issues but struggled to appreciate the nuanced aspects of ethical dilemmas and misapplied certain moral principles.This study reveals limitations in the ability of GPT-4 to appreciate the depth and nuanced acceptability of real-world ethical dilemmas, particularly those that require a thorough understanding of relational complexities and context-specific values. Ongoing evaluation of LLM capabilities within medical ethics remains paramount, and further refinement is needed before it can be used effectively in clinical settings. (shrink)

The article discusses the strategies of translation of «machine texts» on the example of generative transformers (GPT). Currently, the study and development of machine text generation has become an important task for processing and analyzing texts in different languages. Modern technologies of artificial intelligence and neural networks allow us to create powerful tools for activities in this field, which are becoming more and more effective every year. Generative transformers are one of such tools. The study of generative transformers also allows (...) developers to create more accurate and efficient machine translation algorithms, which improves the quality of translations and improves the user experience. In this context, the features of machine texts created by generative transformers, their patterns, errors and imperfections, which require special translation strategies, deserve special interest. Today we can say that the generation of unique and relevant texts is a routine task that has been automated. Nevertheless, certain restrictions for the use of such texts still exist, in particular, their use requires the use of appropriate translation strategies. The paper proposes the author’s typology of translation strategies, where, taking into account the features of AST, it is proposed to add a substrategy of tertiary-moderation translation. (shrink)

Political theory as a discipline has long been skeptical of computational methods. In this paper, I argue that it is time for theory to make a perspectival shift on these methods. Specifically, we should consider integrating recently developed generative large language models like GPT-4 as tools to support our creative work as theorists. Ultimately, I suggest that political theorists should embrace this technology as a method of supporting our capacity for creativity—but that we should do so in a way that (...) is mindful of the content and value of theorizing, the technical constraints of the models, and the ethical questions that the technology raises. (shrink)

Michael A PetersBeijing Normal UniversityChatGPT is an AI chatbot released by OpenAI on November 30, 2022 and a ‘stable release’ on February 13, 2023. It belongs to OpenAI’s GPT-3 family (generativ...

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" (...) preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023. (shrink)

This essay analyzes the rise of Large Language Models (LLMs) such as GPT-4 or Gemini, which are now incorporated in a wide range of products and services in everyday life. Importantly, it considers some of their hidden human costs. First, is the question of who is left behind by the further infusion of LLMs in society. Second, is the issue of social inequalities between lingua franca and those which are not. Third, LLMs will help disseminate scientific concepts, but their meanings' (...) dissemination is at risk. Fourth, is the issue of information security and how nations and individuals might adapt. Fifth, is the issue of the environmental cost of LLMs for future generations. At the dawn of the conversational AI era, it would be wise to ponder what inequalities and empowerments will be co-created with LLMs. (shrink)

Generative artificial intelligence systems based on transformers, including both text-generators like GPT-4 and image generators like DALL-E 3, have recently entered the popular consciousness. These tools, while impressive, are liable to reproduce, exacerbate, and reinforce extant human social biases, such as gender and racial biases. In this paper, we systematically review the extent to which DALL-E Mini suffers from this problem. In line with the Model Card published alongside DALL-E Mini by its creators, we find that the images it produces (...) tend to represent dozens of different occupations as populated either solely by men (e.g., pilot, builder, plumber) or solely by women (e.g., hairdresser, receptionist, dietitian). In addition, the images DALL-E Mini produces tend to represent most occupations as populated primarily or solely by White people (e.g., farmer, painter, prison officer, software engineer) and very few by non-White people (e.g., pastor, rapper). These findings suggest that exciting new AI technologies should be critically scrutinized and perhaps regulated before they are unleashed on society. (shrink)

Powerful transformer models based on neural networks such as GPT-4 have enabled huge progress in natural language processing. This paper identifies three challenges for computer programs dealing with metaphors. First, the phenomenon of Twice-Apt-Metaphors shows that metaphorical interpretations do not have to be triggered by syntactical, semantic or pragmatic tensions. The detection of these metaphors seems to involve a sense of aesthetic pleasure or a higher-order theory of mind, both of which are difficult to implement into computer programs. Second, the (...) contexts relative to which metaphors are interpreted are not simply given but must be reconstructed based on pragmatic considerations that can involve presuppositional pretence. If computer programs cannot produce or understand such a form of pretence, they will have problems dealing with certain metaphors. Finally, adequately interpreting and reacting to some metaphors seems to require the ability to have internal, first-personal experiential and affective states. Since it is questionable whether computer programs have such mental states, it can be assumed that they will have problems with these kinds of metaphors. (shrink)

The grammatical manipulation and production of language is a great deceiver. We have become habituated to accept the use of well-constructed language to indicate intelligence, understanding and, consequently, intention, whether conscious or unconscious. But we are not always right to do so, and certainly not in the case of large language models (LLMs) like ChapGPT, GPT-4, LLaMA, and Google Bard. This is a perennial problem, but when one understands why it occurs, it ceases to be surprising that it so stubbornly (...) persists. This paper will have three main sections. In the Introduction I will say a little about language, its aetiology, and useful sub-divisions into natural and cultural. In the second section I will explain the current situation with regard to large language models and fill in the background debates which set the problem up as one of increased complexity rather than one of a qualitatively different kind from narrow or specific AI. In the third section I will present the case for the missing phenomenological background and why it is necessary for the co-creation of shared meaning in both natural and cultural language, and I will conclude this section by presenting a rationale for why this situation arises and will continue to arise. Before we do any of this, I need to clarify one point: I do not wish to challenge the ascription of artificial general intelligence (AGI) to LLMs, indeed I think agnosticism is best in this respect, but I do challenge the more serious, and erroneous, ascription of understanding, intention, reason, and consciousness to them. And so, I am making two points: an epistemological one about why we fall into error in our ascription of a mental life to LLMs, and an ontological one about the impossibility of LLMs being or becoming conscious. (shrink)

This paper delves into the striking parallels between the linguistic patterns of Large Language Models (LLMs) and the concepts of psychosis in Lacanian psychoanalysis. Lacanian theory, with its focus on the formal and logical underpinnings of psychosis, provides a compelling lens to juxtapose human cognition and AI mechanisms. LLMs, such as GPT-4, appear to replicate the intricate metaphorical and metonymical frameworks inherent in human language. Although grounded in mathematical logic and probabilistic analysis, the outputs of LLMs echo the nuanced linguistic (...) associations found in metaphor and metonymy, suggesting a mirroring of human linguistic structures. A pivotal point in this discourse is the exploration of “absolute metaphors”—core gaps in reasoning discernible in both AI models and human thought processes and central to the Lacanian conceptualization of psychosis. Despite the traditional divide between AI research and continental philosophy, this analysis embarks on an innovative journey, utilizing Lacanian philosophy to unravel the logic of AI, using concepts established in the continental discourse on logic, rather than the analytical tradition. (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)

The paper continues my earlier Chat with OpenAI’s ChatGPT with a Focused LLM Experiment (FLEX). The idea is to conduct Large Language Model (LLM) based explorations of certain areas or concepts. The approach is based on crafting initial guiding prompts and then follow up with user prompts based on the LLMs’ responses. The goals include improving understanding of LLM capabilities and their limitations culminating in optimized prompts. The specific subjects explored as research subject matter include a) diagonalization techniques as practiced (...) by Cantor, Turing, Gödel, and the advances, such as the Forcing techniques introduced by Paul Cohen and later investigators, b) Knowledge Hierarchies & Mapping Exercises, c) Discussions of IJ Good’s Speculations Concerning the First Ultraintelligent Machine, AGI, and Super- intelligence. Results suggest variability between major models like ChatGPT-4, Llama-3, Cohere, Sonnet and Opus. Results also point to strong dependence on users’ preexisting knowledge and skill bases. The paper should be viewed as ’raw data’ rather than a polished authoritative reference. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Emergent Spacetime, the Megastructure Problem, and the Metaphysics of the SelfSusan Schneider (bio)The aim of this article is to introduce new thoughts on some pressing topics relating to my book, Artificial You, ranging from the fundamental nature of reality to quantum theory and emergence in large language models (LLM) like GPT-4. Since Artificial You was published, the innovations in the domain of AI chatbots like GPT-4 have been rapid-fire, (...) and at times dizzying. The thoughtful discussion in this symposium offers an important opportunity for cross-cultural engagement on these issues and more. I am grateful for the opportunity to learn from the insights of these authors and editors. [End Page 314]The order of my comments will be as follows: in the context of considering Christian Coseru's intriguing response (section 1), I begin by discussing the nature of the self, which I use as a springboard into larger, vexing issues about the nature of fundamental reality, given work on emergent spacetime (ES) (section 2). I offer a "Prototime Interpretation" of quantum mechanics in which spacetime emerges from a prototemporal arena, and use this idea to explore the nature of the self and mind.Using the assumption of ES, I devise an approach to metaphysically fundamental objects (which I call "quantum objects") based on quantum entanglement and the bundle theory of substance. I illustrate how the self or mind, insofar as it is a substance that supervenes on quantum objects, could be partly or fully determined by the properties of the quantum objects on which it supervenes. Intriguingly, this would mean that the self or mind is not located in any singular point in space at a given time. I also explain how the Prototime Interpretation of quantum mechanics is compatible with change and causation at the fundamental level.In section 3 I turn to Inês Hipólito's interesting article, discussing the import of not underestimating AI autonomy in light of the increasingly sophisticated large-language models (LLMs).1 I raise the "AI Megastructure Problem," a new problem of AI control arising in the context of the incipient AGI systems that are now populating the AI ecosystem. The problem, which arose from a collaboration with Kyle Kilian, considers the possibility of emergent properties from the interaction of the LLMs and other AI's in the AI ecosystem (Schneider and Kilian 2023). Then, turning to Noam Cook's intriguing discussion of Japanese philosophy, I consider the pressing issue of testing for machine consciousness, devising a modified version of my AI consciousness test (ACT), originally developed with astrophysicist Edwin Turner. I add an "interpretability component" to the ACT to deal with the case of LLMs (section 4).I. Patternism and the Biological Process ViewChristian Coseru's intriguing piece focuses on the chapters on personal identity in Artificial You, proposing that when it comes to the nature of the self, one should adopt "a biologically informed process-ontology perspective on personal identity," which "means recognizing that organic identity is different in kind: it is an identity of form rather than of matter."His position is akin to the patternist views that I argue against in Artificial You in the sense that it embraces the idea that form, rather than the particular matter that comprises one's brain and body, is key to one's survival over time. But there are important differences as well. For transhumanists tend to believe that the self can survive radical changes in one's substrate, transitioning from thinking with a biological brain to computing with a network of microchips, for instance. Coseru departs from this, writing: [End Page 315]As organisms, persons endure by undergoing constant change on a fundamentally cellular level, including the change in the neural pathways that realize the cognitive processes of thinking and expressing these very thoughts right now.Furthermore:[I]f persons are processes rather than things, then they cannot be cognitively enhanced by augmenting, replacing or transferring their functioning parts (e.g., brain cells).I will call this position the "Biological Process View" (BPV). I have several questions about it. First, how does this position solve one of the key problems I raised for... (shrink)

The reasoning abilities of large language models (LLMs) are the topic of a growing body of research in artificial intelligence and cognitive science. In this paper, we probe the extent to which a dozen LLMs are able to distinguish logically correct inferences from logically fallacious ones. We focus on inference patterns involving conditionals (e.g., 'If Ann has a queen, then Bob has a jack') and epistemic modals (e.g., 'Ann might have an ace', 'Bob must have a king'). These inference patterns (...) have been of special interest to logicians, philosophers, and linguists, since they plausibly play a central role in human reasoning. Assessing LLMs on these inference patterns is thus highly relevant to the question of how much the reasoning abilities of LLMs match those of humans. Among the LLMs we tested, all but GPT-4 often make basic mistakes with conditionals. Moreover, even GPT-4 displays logically inconsistent judgments across inference patterns involving epistemic modals. (shrink)

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen et al. (Exp Theor Artif Intell 352:24–28, 2004) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. (...) Remarkably, they rated the AI’s moral reasoning as superior in quality to humans’ along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans’ raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality. (shrink)

In their article ‘Consent-GPT: is it ethical to delegate procedural consent to conversational AI?’, Allen et al 1 explore the ethical complexities involved in handing over parts of the process of obtaining medical consent to conversational Artificial Intelligence (AI) systems, that is, AI-driven large language models (LLMs) trained to interact with patients to inform them about upcoming medical procedures and assist in the process of obtaining informed consent.1 They focus specifically on challenges related to accuracy (4–5), trust (5), privacy (5), (...) click-through consent (5) and responsibility (5–6), alongside some pragmatic considerations (6). While the authors competently navigate these critical issues and present several key perspectives, we posit that their discussion on trust in what they refer to as ‘Consent-GPT’ significantly underestimates one vital factor: the interpersonal aspect of trust. Admittedly, this …. (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)

Since industrial trade fair Hannover Messe 2011, the term “Industrie 4.0” has ignited a vision of a new Industrial Revolution and has been inspiring a lively, ongoing debate among the German public about the future of work, and hence society, ever since. The discourse around this vision of the future eventually spread to other countries, with public awareness reaching a temporary peak in 2016 when the World Economic Forum’s meeting in Davos was held with the motto “Mastering the Fourth Industrial (...) Revolution.” How is it possible for a vision originally established by three German engineers to unfold and bear fruit at a global level in such a short period of time? This article begins with a summary of the key ideas that are discussed under the label Industrie 4.0. The main purpose, based on an in-depth discourse analysis, is to debunk the myth about the origin of this powerful vision and to trace the narrative back to the global economic crisis in 2009 and thus to the real actors, central discourse patterns, and hidden intentions of this vision of a new Industrial Revolution. In conclusion, the discourse analysis reveals that this is not a case of visioneering but one of a future told, tamed, and traded. (shrink)

This article deals with the material presence of the past and the recent call in the human sciences for a " things." This renewed interest in things signals a rejection of constructivism and textualism and the longing for what is "real," where "regaining" the object is conceived as a means for re-establishing contact with reality. In the context of this turn, we might wish to reconsider the status of relics of the past and their function in mediating relations between the (...) organic and the inorganic, between people and things, and among various kinds of things themselves for reconceptualizing the study of the past. I argue that the future will depend on whether and how various scholars interested in the past manage to modify their understanding of the material remnants of the past, that is, things as well as human, animal, and plant remains. In discussing this problem I will refer to Martin Heidegger's distinction between an object and a thing, to Bruno Latour's idea of the agency of things and object-oriented democracy, and to Don Ihde's material hermeneutics. To illustrate my argument I will focus on some examples of the ambivalent status of the disappeared person in Argentina, which resists the oppositional structure of present versus absent. In this context, the disappeared body is a paradigm of the past itself, which is both continuous with the present and discontinuous from it, which simultaneously is and is not. Since there are no adequate terms to analyze the "contradictory" or anomalous status of the present-absent dichotomy, I look for them outside the binary oppositions conventionally used to conceptualize the present-absent relationship in our thinking about the past. for this purpose I employ Algirdas Julien Greimas's semiotic square. (shrink)

In Ephesians 4:17–32, the concept of a “new man” is explored, emphasizing the transformation that occurs when one becomes a follower of Christ. This passage delves into the teachings and principles that guide believers in their spiritual growth and maturity journey. The keywords in this section highlight key aspects of the teaching, such as “teaching,” which implies the importance of instruction and guidance in understanding and living out the Christian faith. Additionally, the phrase “new man” signifies a radical change within (...) an individual when they surrender their old ways and embrace a new life in Christ. Ephesians 4:17–32 offers practical insights on how to live as a transformed individual. It addresses various aspects of personal conduct, including honesty, integrity, self-control, kindness, forgiveness, and love. These virtues are not only encouraged for personal growth but also for fostering healthy relationships within the community of believers. (shrink)

Class I PI 3‐kinases signal by producing the signaling lipid phosphatidylinositol(3,4,5) trisphosphate, which in turn acts by recruiting downstream effectors that contain specific lipid‐binding domains. The class I PI 3‐kinases comprise four distinct catalytic subunits linked to one of seven different regulatory subunits. All the class I PI 3‐kinases produce the same signaling lipid, PIP3, and the different isoforms have overlapping expression patterns and are coupled to overlapping sets of upstream activators. Nonetheless, studies in cultured cells and in animals have (...) demonstrated that the different isoforms are coupled to distinct ranges of downstream responses. This review focuses on the mechanisms by which the production of a common product, PIP3, can produce isoform‐specific signaling by PI 3‐kinases.Editor's suggested further reading in BioEssays: Phosphatidylinositol 4,5‐bisphosphate: Targeted production and signaling AbstractHow does SHIP1/2 balance PtdIns(3,4)P2 and does it signal independently of its phosphatase activity? Abstract. (shrink)

It has become extremely questionable whether, in the flux of life, it is a genuinely worthwhile intellectual problem to seek to discover fixed and immutable ideas or absolutes.

There has been much discussion of the phrase ‘perpetuum… carmen’ and of its relation to Callimachus' rejection of the . Much less has been written about deducite in line 4. However it is, I believe, equally important for the ‘programme’ of Ovid's poem. Deducere is a metaphor frequently applied to poetry in general with the sense of ‘to write, compose something.’ In certain contexts the reference is much more specific and indicates subtle and polished writing of the kind associated with (...) Callimachus. (shrink)

puri in 1026 can hardly be right. Bed-wetting is normally confined to children, and tum quibus…in 1030 presupposes the mention of an earlier stage of life in the previous sentence. And what does puri mean? Munro and Bailey translated it as ‘cleanly people’ , though Munro himself pointed out that the Latin for this was mundi rather than puri, and in any case there is no reason to suppose that in ancient Rome cleanly people were addicted to bed-wetting. Giussani, followed (...) by Merrill and by Leonard and Smith, tried to give the required sense by supposing that puri meant ‘innocents’ and hence ‘children’, an expedient which is very far from convincing. Emendation seems called for. M. F. Smith in the 1982 edition of the Loeb Lucretius adopts Avancius' multi,2 but this does not provide the reference we need to the age of those concerned. (shrink)