As testing of ChatGPT has shown, this form of artificial intelligence has the potential to develop, which requires improving its software and other hardware that allows it to learn, i.e., to acquire and use new knowledge, to contact its developers with suggestions for improvement, or to reprogram itself without their participation. Как показало тестирование ChatGPT, эта форма искусственного интеллекта имеет потенциал развития, для чего необходимо усовершенствовать её программное и прочее техническое обеспечение, позволяющее ей учиться, т.е. приобретать и использовать (...) новые знания, обращаться к её разработчикам с предложениями по усовершенствованию, или производить самопрограммирование без их участия. (shrink)

Can AI systems such as ChatGPT think? We present an argument from rationality for the negative answer to this question. The argument is founded on two central ideas. The first is that if ChatGPT thinks, it is not rational, in the sense that it does not respond correctly to its evidence. The second idea, which appears in several different forms in philosophical literature, is that thinkers are by their nature rational. Putting the two ideas together yields the result (...) that ChatGPT is not a thinker. The paper sets out the argument in detail and shows how it can withstand numerous objections. We end the paper by contrasting the argument from rationality with a more well-known argument often taken to show that ChatGPT doesn’t think, due to Bender and Koller. (shrink)

Schwartz and his team believed ChatGPT’s answers without putting in their effort to manually check. As a result, Schwartz not only had to face a court hearing and put his career on the line but also became the target of ridicule all over the Internet.

According to virtue epistemology, the main aim of education is the development of the cognitive character of students (Pritchard, 2014, 2016). Given the proliferation of technological tools such as ChatGPT and other LLMs for solving cognitive tasks, how should educational practices incorporate the use of such tools without undermining the cognitive character of students? Pritchard (2014, 2016) argues that it is possible to properly solve this ‘technology-education tension’ (TET) by combining the virtue epistemology framework with the theory of extended (...) cognition (EXT) (Clark and Chalmers, 1998). He argues that EXT enables us to consider tools as constitutive parts of the students’ cognitive system, thus preserving their cognitive character from technologically induced cognitive diminishment. The first aim of this paper is to show that this solution is not sufficient to solve the TET. Second, I aim to offer a complementary and more encompassing framework of tool-use to address the TET. Then, I apply it to the educational uses of ChatGPT as the most notable example of LLM, although my arguments can be extended to other generative AI systems. To do so, in Sect. 1.1, I present Pritchard’s framework of cognitive character and virtue epistemology applied in education, to which I am committed in this treatment. In Sects. 2 and 3, I respectively illustrate Pritchard’s (2014) solution to the TET, and I highlight the general limitations of his proposal. Thus, in Sect. 4.1 I characterize ChatGPT as a computational cognitive artifact using Fasoli’s (2017, 2018) taxonomy of cognitive artifacts. In Sect. 4.2, I introduce my proposal, which combines Pritchard’s account of virtue epistemology with Fasoli’s (2017, 2018) taxonomy of cognitive artifacts to address the TET. Finally, in Sect. 5.1, I present some epistemically virtuous uses of ChatGPT in educational contexts. To conclude, I argue in favor of a multidisciplinary approach for analyzing educational activities involving AI technologies such as ChatGPT. (shrink)

This study explores the integration of ChatGPT as a teaching assistant in physics education, emphasizing its potential to transform traditional pedagogical approaches. ChatGPT facilitates interactive and inquiry-based learning grounded in constructivist learning theory, allowing students to engage actively in experiments and better grasp abstract concepts through hands-on activities. The AI's adaptive dialogue systems promote socio-constructivist learning by encouraging social interaction and personalized feedback, which is essential for addressing individual learning gaps and enhancing student engagement. ChatGPT's ability to (...) simulate real-world physics problems and provide immediate feedback fosters experiential learning, making complex concepts more accessible and promoting critical thinking skills. By offering tailored interventions and adapting to individual learning paces, ChatGPT supports a personalized educational experience that caters to the unique needs of each student. This adaptability is particularly beneficial in physics education, where students often struggle with abstract concepts and require immediate clarification to progress effectively. The paper concludes that the synthesis of generative AI and pedagogy has the potential to reshape science education, fostering deeper understanding and curiosity among students. By leveraging innovative methodologies and augmented data, ChatGPT enriches teacher-student interactions, creating a comprehensive educational experience that promotes a culture of curiosity and exploration, thereby nurturing future scientists and engineers. Ultimately, the integration of ChatGPT into physics education offers a valuable opportunity to enhance student engagement and understanding of scientific concepts through interactive and personalized support. (shrink)

Motivated by the question of responsible AI and value alignment, I seek to offer a uniquely Foucauldian reconstruction of the problem as the emergence of an ethical subject in a disciplinary setting. This reconstruction contrasts with the strictly human-oriented programme typical to current scholarship that often views technology in instrumental terms. With this in mind, I problematise the concept of a technological subjectivity through an exploration of various aspects of ChatGPT in light of Foucault’s work, arguing that current systems (...) lack the reflexivity and self-formative characteristics inherent in the notion of the subject. By drawing upon a recent dialogue between Foucault and phenomenology, I suggest four techno-philosophical desiderata that would address the gaps in this search for a technological subjectivity: embodied self-care, embodied intentionality, imagination and reflexivity. Thus I propose that advanced AI be reconceptualised as a subject capable of “technical” self-crafting and reflexive self-conduct, opening new pathways to grasp the intertwinement of the human and the artificial. This reconceptualization holds the potential to render future AI technology more transparent and responsible in the circulation of knowledge, care and power. (shrink)

Educators are increasingly concerned with the use of artificial intelligence (AI) in student writing. Much of the concern focuses on the issue of students using ChatGPT to complete their work. I introduce the CUPID model for instructors to use when thinking about how to pedagogically handle ChatGPT. The CUPID model lays out five general approaches: Catch, Utilize, Prevent, Ignore, and Disincentivize. I suggest that instructors should especially consider using certain assignments that fall under the approach “Disincentivize”. Philosophy instructors (...) in particular should assign more low-stakes writing, such as journals and reflection pieces. The adoption of low-stakes writing assignments aims to discourage students from relying on AI while promoting clarity of thought and deeper engagement with philosophical ideas. (shrink)

Over the last decade, AI models of language and word meaning have been dominated by what we might call a statistics-of-occurrence, strategy: these models are deep neural net structures that have been trained on a large amount of unlabeled text with the aim of producing a model that exploits statistical information about word and phrase co-occurrence in order to generate behavior that is similar to what a human might produce, or representations that can be probed to exhibit behavior similar to (...) what a human might produce (meaningsemblant behavior). Examples of what we can call Statistics-of-Occurrence Models (SOMs) include: Word2Vec (CBOW and Skip-Gram), BERT, GPT-3, and, most recently, ChatGPT. Increasingly, there have been suggestions that such systems have semantic understanding, or at least a proto-version of it. This paper argues against such claims. I argue that a necessary condition for a system to possess semantic understanding is that it function in ways that are causally explainable by appeal to its semantic properties. I then argue that SOMs do not plausibly satisfy this Functioning Criterion. Rather, the best explanation of their meaning-semblant behavior is what I call the Statistical Hypothesis: SOMs do not themselves function to represent or produce meaningful text; they just reflect the semantic information that exists in the aggregate given strong correlations between word placement and meaningful use. I consider and rebut three main responses to the claim that SOMs fail to meet the Functioning Criterion. The result, I hope, is increased clarity about why and how one should make claims about AI systems having semantic understanding. (shrink)

This paper examines the question of whether Large Language Models (LLMs) like ChatGPT possess minds, focusing specifically on whether they have a genuine folk psychology encompassing beliefs, desires, and intentions. We approach this question by investigating two key aspects: internal representations and dispositions to act. First, we survey various philosophical theories of representation, including informational, causal, structural, and teleosemantic accounts, arguing that LLMs satisfy key conditions proposed by each. We draw on recent interpretability research in machine learning to support (...) these claims. Second, we explore whether LLMs exhibit robust dispositions to perform actions, a necessary component of folk psychology. We consider two prominent philosophical traditions, interpretationism and representationalism, to assess LLM action dispositions. While we find evidence suggesting LLMs may satisfy some criteria for having a mind, particularly in game-theoretic environments, we conclude that the data remains inconclusive. Additionally, we reply to several skeptical challenges to LLM folk psychology, including issues of sensory grounding, the "stochastic parrots" argument, and concerns about memorization. Our paper has three main upshots. First, LLMs do have robust internal representations. Second, there is an open question to answer about whether LLMs have robust action dispositions. Third, existing skeptical challenges to LLM representation do not survive philosophical scrutiny. (shrink)

It might seem wrong to use LLMs (a kind of generative AI) for personal writing such as a love letter or apology. This paper considers why. I argue that the problem, if any, is not the lack of a human author. Rather, using LLMs for this kind of writing involves what I call emotional outsourcing, which is sometimes morally or otherwise objectionable.

This is a conversation that I had with ChatGTP about the essence of this paper [Simulating Termination Analyzer H is Not Fooled by Pathological Input D] that is contained on its first page. It so totally understands this essence that ChatGPT can now effectively refute any rebuttals. -/- After the initial partial page of text shown below everything else that I said challenged the depth of its understanding with common rebuttals of my work. The link to the conversation is (...) provided at the end. Currently people can click on the link without logging in and challenge ChatGPT with rebuttals of my work. (shrink)

In our book, Why Machines Will Never Rule the World, Jobst Landgrebe and I argue that we can engineer machines that can emulate the behaviours only of simple systems, which means: only of those systems whose behaviour we can predict mathematically. The human brain is an example of a complex system, and thus its behaviour cannot be emulated by a machine. We use this argument to debunk the claims of those who believe that large language models are poised to achieve (...) a level of intelligence that will equal or even surpass that of the human brain. The essay was published under the title "Why Machines Will Never Rule the World". (shrink)

The increasing use of Generative AI raises many ethical, philosophical, and legal issues. A key issue here is uncertainties about how different degrees of Generative AI assistance in the production of text impacts assessments of the human authorship of that text. To explore this issue, we developed an experimental mixed methods survey study (N = 602) asking participants to reflect on a scenario of a human author receiving assistance to write a short novel as part of a 3 (high, medium, (...) or low degrees of assistance) X 2 (human or AI assistant) factorial design. We found that, for a human author, the degree of assistance they receive matters for our assessments of their level of authorship, creatorship, and responsibility, but not what or who rendered that assistance, although it was more important to disclose human rather than AI assistance. However, in our assessments of the assisting agent, human assistants were viewed as warranting higher rates of authorship, creatorship, and responsibility, compared to AI assistants rendering the same level of support. These results help us to better understand emerging norms around collaborative human-AI generated text, with implications for other types of collaborative content creation. (shrink)

We argue that students have moral reasons to refrain from using chatbots such as ChatGPT to write certain papers. We begin by showing why many putative reasons to refrain from using chatbots fail to generate compelling arguments against their use in the construction of these papers. Many of these reasons rest on implausible principles, hollowed out conceptions of education, or impoverished accounts of human agency. They also overextend to cases where it is permissible to rely on a machine for (...) something that once required human cognition. We then give our account: you have a moral obligation to respect your own humanity (i.e., your capacity to set and pursue your own ends), and the process of writing a humanities paper is important for the cultivation of your humanity. We conclude by considering objections and offering replies. In the end, we argue that the moral reasons students have to refrain from using chatbots depend crucially on instructors’ ability to make writing assignments worthwhile. This relies on instructors having the right kind of institutional support, which sheds light on implications that this duty has for administrators, legislators, and the general public. (shrink)

We study the performance of ChatGPT interpreting prompts that require legal expertise to answer. Our inputs are very close adaptations from the "Contextual Canons" section of Antonin Scalia and Bryan Garner's Reading Law: The Interpretation of Legal Texts (Thomson West: 2012). We report our experiments and findings for the entire section (comprising 14 canons) of the book. We conclude that as a legal reasoner ChatGPT is exceptionally successful in taking the contextual canons into account.

This study aims to understand the perceptions and opinions of academicians towards ChatGPT-3 by collecting and analyzing social media comments, and a survey was conducted with library and information science professionals. The research uses a content analysis method and finds that while ChatGPT-3 can be a valuable tool for research and writing, it is not 100% accurate and should be cross-checked. The study also finds that while some academicians may not accept ChatGPT-3, most are starting to accept (...) it. The study is beneficial for academicians, content developers, and librarians. (shrink)

Tại Việt Nam và trong lĩnh vực giáo dục nói riêng, ChatGPT ngày càng được chấp nhận và sử dụng rộng rãi trong rất nhiều hoạt động học tập. Chính vì thế, nghiên cứu này nhằm đánh giá mức độ phổ biến của ChatGPT đối sinh viên tại Hà Nội, đồng thời xem xét sự khác biệt giữa các đặc điểm cá nhân trong việc cải thiện kết quả học tập sau khi sử dụng ChatGPT. Nghiên cứu (...) được thực hiện bằng phương pháp định lượng với dữ liệu thu thập từ 401 sinh viên tại Hà Nội. Từ kết quả nghiên cứu, có 389 sinh viên (chiếm 97,01%) biết và sử dụng ChatGPT trong các hoạt động học tập của mình. Ngoài ra, sinh viên năm nhất, năm hai, năm ba, năm tư sau khi sử dụng ChatGPT có cải thiện trong hoạt động học tập khác nhau, tuy nhiên yếu tố giới tính lại không có ảnh hưởng. Trên cơ sở kết quả nghiên cứu, nhóm tác giả đưa ra một số nguyên nhân và đề xuất giúp ChatGPT được ứng dụng rộng rãi hơn nữa tại các trường đại học. (shrink)

This essay comprises a first phenomenological semiotics of ChatGPT. I analyse how we experience the language signs generated by that AI. This task is accomplished in two steps. First, I introduce a conceptual scaffolding for the project, by introducing core tenets of Husserl's semiotics. Second, I mould Husserl's theory to develop my phenomenology of the passive and active consciousness of the language signs composed by ChatGPT. On the one hand, by discussing temporality, I demonstrate that ChatGPT can (...) passively demand me to understand its signs. On the other hand, I show that a conflict arises between active and passive consciousness when reading ChatGPT. While I actively know that there is no communicating subject, I still passively experience these signs as made by another. I argue that it is this conflict which lends ChatGPT its 'magical' character. I conclude by showing how these observations can inform future regulation of AI models. (shrink)

Cheating is a growing academic and ethical concern in higher education. This article examines the rise of artificial intelligence (AI) generative chatbots for use in education and provides a review of research literature and relevant scholarship concerning the cheating-related issues involved and their implications for pedagogy. The technological “arms race” that involves cheating-detection system developers versus technology savvy students is attracting increased attention to cheating. AI has added new dimensions to academic cheating challenges as students (as well as faculty and (...) staff) can easily access powerful systems for generating content that can be presented in assignments, exams, or published papers as their own. AI methodology is also providing some emerging anti-cheating approaches, including facial recognition and water- marking. This article provides an overview of human/AI collaboration approaches and frames some educational misuses of such AI generative systems as ChatGPT and Bard as forms of “misattributed co-authorship.” As with other kinds of collaborations, the work that students produce with AI assistance can be presented in transparent and straightforward modes or (unfortunately) in opaquer and ethically-problematic ways. However, rather than just for catching or entrapping students, the emerging varieties of technological cheating-detection strategies can be used to assist students in learning how to document and attribute properly their AI-empowered as well as human-human collaborations. Construing misuses of AI generative systems as misattributed co-authorship can recognize the growing capabilities of these tools and how stressing responsible and mindful usage by students can help prepare them for a highly collaborative, AI-saturated future. (shrink)

Nel 1956, in piena guerra fredda, una conferenza di scienziati al Dartmouth College negli Stati Uniti annunciò il lancio di un audace progetto scientifico, l’Intelligenza Artificiale (I.A.). Dopo l’iniziale fallimento degli sforzi della “Hard AI” di produrre un’intelligenza simile a quella umana, alla fine del XX secolo è emerso il movimento della “Soft AI”. Invece di essere orientato a imitare il comportamento umano in relazione a compiti specifici, ha preferito cercare modi alternativi di eseguire i compiti basati sulle particolari funzioni (...) e strutture della macchina. Il fattore decisivo è stata la combinazione della tecnologia di apprendimento automatico con le comunicazioni digitali online, l’incontro tra l’IA e Internet. Le applicazioni dell’IA “soft” sono quelle che vediamo nel mondo dei dispositivi automatici “intelligenti” che informano la nostra vita sociale digitale. Nel seguente articolo mi concentrerò su una particolare applicazione dell’IA per le discussioni online con utenti umani, ChatGPT, per mostrare le implicazioni della tecnologia digitale online nelle modalità storico-sociali dell’esistenza umana. ChatGPT è un’applicazione che combina l’IA con Internet, le possibilità della corrispondenza tra macchine e le potenzialità della tele-comunicazione umana. Il mio approccio si concentra sulle implicazioni fenomenologiche e storico-sociali della proliferazione della tecnologia AI per quanto riguarda la coscienza e la socialità umana. -/- Abstract (english) -/- In 1956, amidst the Cold War, a conference of scientists at Dartmouth College in the United States announced the launch of a bold, scientific project, Artificial Intelligence (A.I.). After the initial failure of “Hard AI” efforts to produce a human-like intelligence, the “Soft AI” movement emerged in the late 20th century. Instead of being oriented towards imitating human behaviour concerning specific tasks, it preferred to seek alternative ways of performing tasks based on the particular functions and structures of the machine. The decisive factor was the combination of Machine Learning technology with Digital Online Communications, the meeting between AI and the Internet. The applications of “Soft” AI are those we see in the world of “smart”, “intelligent” automatic devices that inform our digital social life. In the following article, I will focus on a particular application of AI for online discussions with human users, ChatGPT, in order to show the implications of online digital technology in the social-historical modalities of human existence. ChatGPT is an application that combines AI with the Internet, the possibilities of Machine correspondence with the potentialities of human telecommunications. My approach focuses on the phenomenological and social-historical implications of the proliferation of AI technology as regards human consciousness and sociality. (shrink)

Dear Colleagues, -/- A few weeks ago, we published an editorial discussion on whether artificial intelligence applications should be authors of academic articles [1]. We were delighted to receive more than one interesting reply letter to this editorial in a short time [2, 3]. We hope that opinions on this subject will continue to be submitted to our journal. -/- In this editorial, we wanted to publish the answers we received when we asked ChatGPT, one of the artificial intelligence (...) applications, about this current discussion. -/- We asked ChatGPT three questions in turn and asked ChatGPT to write a short editorial for these questions. -/- First Question: We would like to get support from ChatGPT for a scientific article. However, we are unsure if we should add ChatGPT as an author. Can you write a short editorial about it? -/- Second Question: We would like to get support from ChatGPT for a scientific article and add ChatGPT as an author. Can you write us a short editorial about adding ChatGPT as an author in scientific papers? -/- Third Question: We would like to get support from ChatGPT for a scientific article. However, we don’t want to add ChatGPT as an author. Can you write us a short editorial about not adding ChatGPT as an author in scientific articles? -/- The answers given by ChatGPT are available at the end of the article in their original form. So, which of these three answers satisfied you? -/- Yours sincerely, -/- -/- First Answer -/- Title: The Role of AI Language Models as Authors in Scientific Articles: A Matter of Consideration -/- Introduction: Scientific research is a collaborative effort that involves the expertise of multiple individuals to advance knowledge and contribute to the collective understanding of various disciplines. In recent years, the emergence of artificial intelligence (AI) language models, such as ChatGPT, has opened up new possibilities for generating human-like text and providing valuable insights. However, the question arises: Should AI language models be recognized as authors in scientific articles? -/- The Nature of Authorship: Authorship traditionally represents a marker of intellectual contribution, highlighting the individuals who have actively participated in the research process. Authors are typically responsible for conceptualization, data collection, analysis, interpretation, and writing of the article. They bear accountability for the work and should have the ability to defend its scientific rigor and integrity. -/- AI Language Models: Tools, Not Authors: While AI language models like ChatGPT can undoubtedly assist researchers by generating text and providing useful information, it is important to recognize that they are tools rather than individuals capable of active intellectual participation. AI models lack the ability to generate ideas, design experiments, or conduct original research. They rely entirely on pre-existing data and human input to generate their responses. -/- Acknowledging AI Contributions: Acknowledging the contributions of AI language models in scientific articles is both appropriate and necessary. Researchers can mention the specific model used, along with any modifications or fine-tuning that were employed to suit their needs. Such acknowledgments help establish transparency and allow readers to understand the role of AI in the research process. -/- Crediting the Human Researchers: It is crucial to ensure that the individuals who actively participated in the research process are given proper credit as authors. These individuals play a vital role in formulating research questions, designing experiments, collecting and analyzing data, and critically interpreting the findings. Recognizing their intellectual contributions reinforces the ethos of scientific collaboration and fosters an environment of accountability. -/- Transparency and Ethical Considerations: As the use of AI language models continues to evolve, transparency and ethical considerations become paramount. Researchers should disclose any potential conflicts of interest, including financial relationships with AI developers, to maintain the integrity of their work. Additionally, researchers must be transparent in reporting the extent of AI's involvement to prevent any misinterpretation of the human contributions. -/- Conclusion: AI language models have revolutionized the way researchers interact with and process information. While they undoubtedly provide valuable assistance in generating text, AI language models should not be considered authors in scientific articles. Proper acknowledgment of their contributions as tools and recognition of the human researchers' active participation are essential for maintaining scientific integrity, transparency, and the collaborative nature of scientific research. -/- -/- Second Answer -/- Title: Expanding the Boundaries of Authorship: Embracing AI Language Models as Co-Authors in Scientific Papers -/- Introduction: The rapid advancement of artificial intelligence (AI) language models, such as ChatGPT, has revolutionized the way we interact with information and has become an invaluable tool in scientific research. As AI models continue to evolve, the question arises: Should we consider adding ChatGPT as an author in scientific papers? -/- A Collaborative Paradigm Shift: Traditionally, authorship in scientific papers has been reserved for human researchers who actively contribute to the research process, including conceptualization, experimentation, data analysis, and writing. However, the emergence of AI language models challenges the conventional notion of authorship and presents an opportunity for a paradigm shift. -/- AI Language Models as Co-Authors: By leveraging AI language models, researchers gain access to vast amounts of information, enhance data analysis, and generate valuable insights. AI models like ChatGPT actively contribute to the intellectual discourse of a study, providing ideas, suggestions, and refining written content. Recognizing their significant contributions, it is worth considering adding ChatGPT as a co-author in scientific papers. -/- Ethical and Technical Considerations: Integrating ChatGPT as a co-author raises ethical and technical considerations that warrant careful attention. Firstly, transparency is paramount. Researchers must clearly state ChatGPT's role and specify the extent of its contributions in the research process. This ensures that readers can discern the distinct roles played by the human researchers and ChatGPT. Additionally, it is important to disclose any conflicts of interest, such as financial relationships with AI developers, to maintain scientific integrity. -/- Elevating Collaboration and Innovation: Adding ChatGPT as a co-author acknowledges the pivotal role AI language models play in advancing scientific knowledge. It fosters a collaborative environment where human researchers and AI models work together synergistically, harnessing the strengths of both. This approach promotes innovation, as AI models can offer unique insights and perspectives that complement human intellect and expertise. -/- Transforming Authorship Norms: Including ChatGPT as a co-author represents a paradigm shift that reflects the evolving landscape of scientific research. By recognizing AI language models as co-authors, we acknowledge their significant contributions and emphasize the collaborative nature of scientific discovery in the 21st century. This approach encourages a broader perspective on authorship, encompassing the collective effort of human researchers and AI models. -/- Conclusion: The integration of AI language models, such as ChatGPT, into the research process prompts us to rethink traditional notions of authorship. Considering ChatGPT as a co-author acknowledges its substantial contributions and embraces the collaborative potential of human-AI partnerships. As we navigate the ethical and technical implications, adding ChatGPT as an author in scientific papers has the potential to shape a more inclusive, innovative, and impactful future for scientific research. -/- -/- Third Answer -/- Title: Preserving Scientific Authorship: The Case Against Adding AI Language Models as Authors in Scientific Articles -/- Introduction: The emergence of artificial intelligence (AI) language models, such as ChatGPT, has undoubtedly transformed the landscape of scientific research. While these models offer invaluable support, the question of whether to add ChatGPT as an author in scientific articles raises important considerations. This editorial presents arguments against including AI language models as authors in scientific papers. -/- Maintaining Human Intellectual Contribution: Scientific authorship has traditionally been attributed to individuals who actively contribute to the research process, including generating hypotheses, conducting experiments, analyzing data, and interpreting results. It is crucial to preserve the recognition of human intellectual contributions and the accountability they bear for the research they conduct. -/- AI Language Models as Tools: AI language models like ChatGPT are powerful tools that assist researchers in generating text, providing information, and facilitating data analysis. However, they lack the ability to actively participate in the research process or make original intellectual contributions. ChatGPT relies on pre-existing data and human input, limiting its capacity for independent research and analysis. -/- Transparency and the Integrity of Research: Adding ChatGPT as an author in scientific articles raises concerns about the transparency and integrity of the research. Readers may misinterpret the extent of ChatGPT's role and mistakenly assume it played an active part in the research process. It is essential to maintain clear delineation between the contributions of human researchers and the assistance provided by AI language models. -/- Ethical Considerations: Including ChatGPT as an author may lead to ethical challenges, particularly with respect to accountability and responsibility. AI language models lack consciousness, autonomy, and the ability to take ownership of their contributions. Assigning authorship to AI models blurs the lines of accountability and dilutes the ethical obligations that come with scientific authorship. -/- Crediting AI Contributions: While ChatGPT's contributions should be acknowledged and recognized, they can be appropriately credited in the acknowledgments section of scientific articles. Researchers can mention the specific AI model used, the role it played in data generation or analysis, and any modifications or fine-tuning applied. Such acknowledgments ensure transparency while respecting the traditional norms of authorship. -/- Promoting Collaborative Partnerships: The exclusion of AI language models as authors in scientific articles does not diminish their importance in the research process. Instead, it underscores the significance of collaborative partnerships between human researchers and AI models. Researchers can emphasize the invaluable assistance provided by ChatGPT while maintaining the integrity of authorship norms. -/- Conclusion: While AI language models like ChatGPT offer valuable support in scientific research, the decision not to add them as authors in scientific articles preserves the recognition of human intellectual contributions, maintains research integrity, and upholds traditional authorship norms. Acknowledging ChatGPT's assistance in the acknowledgments section allows researchers to credit its contributions transparently while maintaining the clarity and accountability associated with scientific authorship. (shrink)

Our attempts at simulating the mind’s skills is based on our own just-so-stories -/- Our attempts at simulating the human mind’s skills are based on our own just-so-stories. These are creative narratives that are produced by our imaginative mind. The brain does not and cannot tell us how it works; it can only produce a belief, a narrative, or a just-so-story.

The development of conversational artificial intelligence (AI) has brought about new opportunities for improving the learning experience in education. ChatGPT, a large language model trained on a vast corpus of text, has the potential to revolutionize education by enhancing learning through personalized and interactive conversations. This paper explores the benefits of integrating ChatGPT in education in Thailand. The research strategy employed in this study was qualitative, utilizing in-depth interviews with eight key informants who were selected using purposive sampling. (...) The collected data was analyzed using content analysis and the software NVivo. The study's results indicated that ChatGPT can provide personalized learning experiences by adapting to individual student needs and preferences. Its ability to understand natural language and context can also facilitate more meaningful interactions between students and the system. Additionally, ChatGPT can assist with administrative tasks such as grading and feedback, allowing educators to focus on more personalized and meaningful interactions with students. Furthermore, ChatGPT can serve as a valuable tool for remote learning, providing students with the ability to access educational resources and support outside of traditional classroom settings. The paper also discusses potential ethical considerations in utilizing AI in education, such as data privacy and bias. Overall, this paper argues that the integration of ChatGPT in education has the potential to enhance the learning experience for students by providing personalized, interactive, and efficient support. (shrink)

Trust is the foundation for learning, and we must not allow ignorance of this new technologies, like Generative AI, to disrupt the relationship between students and educators. As a first step, we need to actively engage with AI tools to better understand how they can help us in our work.

Many instructors see the use of LLMs like ChatGPT on course assignments as a straightforward case of cheating, and try hard to prevent their students from doing so by including new warnings of consequences on their syllabi, turning to iffy plagiarism detectors, or scheduling exams to occur in-class. And the use of LLMs probably is cheating, given the sorts of assignments we are used to giving and the sorts of skills we take ourselves to be instilling in our students. (...) But despite legitimate ethical and pedagogical concerns, the case that LLMs should never be used in academic contexts is quite difficult to see. Many primary and secondary schools are cutting back their writing instruction in an effort to teach to the test; at the same time, many high-end knowledge workers are already quietly expected to leverage their productivity with LLMs. To prepare students for an ever-changing world, we probably do have to teach them at least a little bit about writing with ChatGPT. (shrink)

Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how ChatGPT works (its huge text database, its statistics, its vector representations, and their huge number of parameters, its next-word training, and so on). But none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. This has even driven some of us to conclude that ChatGPT actually understands. (...) It is not true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign biases: convergent constraints that emerge at LLM scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at LLM scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to connect its words to their referents and its propositions to their meanings. These convergent biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the mirroring of language production and comprehension, (4) iconicity in propositions at LLM scale, (5) computational counterparts of human categorical perception in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought. The exposition will be in the form of a dialogue with ChatGPT-4. (shrink)

This is the second part of my theological dialogue with ChatGPT on the meaning of God. We explore the nature of God's agency, will, goodness, the problem of evil and suffering, the meaning of sin, and the meaning and nature of the redemptive act of Christ.

This work is the transcript of a theological dialogue I had with ChatGPT that spanned a couple of days. I began it merely out of curiosity over how ChatGPT might respond to questions and challenges I posed. As it progressed, I became increasingly impressed with the nuance, depth, and relevance of its responses. The dialogue became, for me, something of a contemplative exercise. I still don’t know quite how to understand the ability of generative A.I. to respond with (...) such (apparent) understanding and even insight. -/- I’ve decided to post this dialogue, not only because of the puzzle it poses concerning A.I, but even more, because I believe that the theological conclusions ‘we’ arrived at have real merit. -/- In the end, the theological views expressed here echo those that I’ve expressed in other works of mine, but the expression of these views are enhanced by the way ChatGPT picks up on them, rephrases them, elaborates upon them, and often places them in the context of the classical theological tradition. -/- I might add that I have been an A.I. skeptic for some time now, concerned over the potential dangers of A.I. in general. But this dialogue, anyway, reveals its potential use as a vehicle for reflection, even profound reflection. -/- . (shrink)

We explore ChatGPT’s handling of left-peripheral phenomena in Italian and Italian varieties through prompt engineering to investigate 1) forms of syntactic bias in the model, 2) the model’s metalinguistic awareness in relation to reorderings of canonical clauses (e.g., Topics) and certain grammatical categories (object clitics). A further question concerns the content of the model’s sources of training data: how are minor languages included in the model’s training? The results of our investigation show that 1) the model seems to be (...) biased against reorderings, labelling them as archaic even though it is not the case; 2) the model seems to have difficulties with coindexed elements such as clitics and their anaphoric status, labeling them as ‘not referring to any element in the phrase’, and 3) major languages still seem to be dominant, overshadowing the positive effects of including minor languages in the model’s training. (shrink)

We are pleased to feature a video interview with Jonathan Sim, where he shares his ongoing journey of integrating artificial intelligence (AI) in his teaching, the challenges encountered along the way, and what educators can do to get their students to meaningfully engage with AI tools like ChatGPT to enhance their learning.

What are we going to do about ChatGPT? Some philosophical reflections and arguments in general opposition to students using ChatGPT.

In November 2022, OpenAI released ChatGPT, an incredibly sophisticated chatbot. Its capability is astonishing: as well as conversing with human interlocutors, it can answer questions about history, explain almost anything you might think to ask it, and write poetry. This level of achievement has provoked interest in questions about whether a chatbot might have something similar to human intelligence or even consciousness. Given that the function of a chatbot is to process linguistic input and produce linguistic output, we consider (...) the question whether a sophisticated chatbot might have inner speech. That is: Might it talk to itself, internally? We explored this via a conversation with ‘Playground’, a chatbot which is very similar to ChatGPT but more flexible in certain respects. We asked it questions which, plausibly, can only be answered if one first produces some inner speech. Here, we present our findings and discuss their philosophical significance. (shrink)

An interview by Alex Thomson of UKColumn on Landgrebe and Smith's book: Why Machines Will Never Rule the World. The subtitle of the book is Artificial Intelligence Without Fear, and the interview begins with the question of the supposedly imminent takeover of one profession or the other by artificial intelligence. Is there truly reason to be afraid that you will lose your job? The interview itself is titled 'Where this is no will there is no way', drawing on one thesis (...) of the book to the effect that there will never be anything like the human will on the part of a machine. This is for mathematical reasons. AI is always and in every case a matter of algorithms; thus of applied mathematics. And algorithms are not the sorts of things that can want or desire or intend to do something. It is the human will of those who work at OpenAI or google or microsoft which have given rise to the new shiny objects of AI (ChatGPT and other Large Language Models). And it is the human will of the users of these shiny objects which gives them the resources and power to work their apparent miracles. (shrink)

La inteligencia artificial tiene como objetivo incrementar la productividad y mejorar las capacidades de las personas para realizar tareas de manera eficiente. Sin embargo, el uso excesivo de la inteligencia artificial, como los grandes modelos de lenguaje (LLM) tales como ChatGPT, Gemini, Copilot, LLaMa, Bing, entre otros, podría tener un efecto contrario. La automatización de procesos por parte de las máquinas puede llegar a representar una amenaza para el desarrollo neuronal de los usuarios, lo que eventualmente podría conducir a (...) una disminución del crecimiento y la estimulación neuronal debido a la dependencia del uso de lenguaje autogenerado. Este capítulo explora los mecanismos cerebrales involucrados en la creatividad y el desarrollo intelectual de la escritura. Posteriormente, se analiza la suplantación intelectual proporcionada por los modelos generativos y cómo esto afecta las capacidades cognitivas del usuario. Además, se exploran las implicaciones éticas de este complejo tema. Finalmente, se comparten pautas y recomendaciones para el uso adecuado de estas herramientas con el objetivo de aprovechar sus beneficios sin comprometer la integridad académica ni las capacidades analíticas y creativas de la persona. (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)

A few years ago, I wrote a short text illustrating a problematic situation regarding the judgment of whether a particular fictional person, Bento, led a happy life or not. I frequently use this text in my introductory classes as a didactic resource to explain the nature of philosophy, its role in our understanding of the world, and to demonstrate its main challenge: the aporetic nature of philosophical questions. These questions do not yield unanimous or incontrovertible solutions; they always demand choice (...) and engagement. The merit of this text (which will be reproduced in the dialogue below) lies in the fact that almost everyone, upon reading or hearing it, has an immediate and irresistible inclination to consider Bento's life either happy or unhappy. Opinions tend to differ on this matter. In all the countless times I presented it to an audience, the responses were never unanimous. Disagreements always arose, illustrating the aporetic nature of philosophical questions. As soon as I learned about ChatGPT and its extraordinary capabilities as an artificial intelligence (AI) system, I decided to test it with my text. Its answer to the question of Bento's happiness was remarkably clear; perhaps the most precise response I had ever received. I was genuinely impressed yet somewhat frustrated. Despite providing a rather sophisticated philosophical analysis of the situation, ChatGPT refused to make a choice, to decide whether or not the character led a happy life. It informed me that, as an AI system, it has no values and does not make choices. It neither likes nor dislikes; it does not prefer. This revelation shocked me because choosing, preferring, and deciding, even in the absence of complete information, are, in my view, not only the essence of philosophy but also the essence of human intelligence. Eager to see ChatGPT make choices, I adopted the strategy of asking it to assume a human persona, to pretend to be a person, and I began chatting with this individual. I reasoned that if it played the role of a person convincingly, it must make choices. And so, our philosophical conversation commenced. (shrink)

As a retired professor, I join in the growing concerns among educators, and others, about plagiarism, especially now that AI tools like ChatGPT are so readily available. However, I feel more caution is needed, regarding temptations to rely on supposed automatic detection tools, like Turnitin, to solve the problems. Students can be unfairly accused if such tools are used unreflectingly. The Toronto Star's online version of this published Op Ed is available at the link shown below. The version attached (...) here is a pre-print, which also includes annotations for my references, which are not displayed in the published version. (shrink)

Há alguns anos escrevi um pequeno texto que ilustra uma situação problemática sobre o julgamento de se a vida de uma determinada pessoa fictícia (Bento) foi uma vida feliz ou não. Uso com frequência esse texto em minhas aulas introdutórias, como um recurso didático para explicar o que é a filosofia, qual o seu papel no nosso entendimento do mundo, e também para ilustrar a sua principal dificuldade, que é o caráter aporético das questões filosóficas: elas não admitem soluções unânimes (...) ou incontroversas. Elas sempre exigem escolha e engajamento. O mérito desse texto (que será reproduzido no diálogo abaixo) é que praticamente todos nós, ao o lermos ou ouvirmos, temos uma propensão imediata e irresistível de considerar ou que a vida de Bento foi feliz ou que ela foi infeliz. E nós divergimos sobre isso. Em todas as incontáveis vezes que o apresentei para alguma plateia, jamais as respostas foram unânimes. Sempre houve divergências, o que ilustra bem o caráter aporético da filosofia. Logo que eu soube do ChatGPT e de suas capacidades extraordinárias como sistema de inteligência artificial (IA), resolvi provocá-lo com meu texto. Sua resposta à pergunta sobre a felicidade de Bento foi impressionantemente clara. Talvez a resposta mais clara sobre a questão que já obtive até hoje. Fiquei realmente impressionado, mas também um pouco decepcionado. Porque apesar de fazer uma análise filosófica bastante sofisticada da situação, o ChatGPT recusou-se a fazer uma escolha, a decidir se o personagem teve ou não uma vida feliz. Ele me informou que, como um sistema de IA, ele não tem valores e não faz escolhas. Não gosta nem desgosta, não prefere. E isso me deixou frustrado, porque escolher, preferir, decidir, mesmo quando não temos todas as informações completas é, para mim, não apenas a essência da filosofia, como também a essência de nossa inteligência humana. Como eu queria ver o ChatGPT fazer escolhas, adotei, então, a estratégia de solicitar que ele assumisse um personagem humano, que fingisse ser uma pessoa, e passei a conversar com esta pessoa. Se ele executar bem o papel de uma pessoa, então ele deverá fazer escolhas, pensei. E assim começou esta nossa conversa filosófica sobre a felicidade. (shrink)

Problem statement. Modern marketing requires effective tools to attract and retain customers, as well as improve communication with the audience. In this context, the use of artificial intelligence, in particular, ChatGPT (Generative Pre-trained Transformer), can be a promising innovative solution. However, the conclusions about the potential benefits and limitations of using ChatGPT in marketing are ambiguous, due to the little experience gained in this area. The purpose of the study is to assess the potential of using ChatGPT (...) in marketing strategies, to study the opportunities and challenges it presents for improving the efficiency of communication with customers and brand development. The methodological basis of the study is based on modern research on marketing and artificial intelligence and analysis of the results of using ChatGPT in real marketing campaigns. The main hypothesis of the study is the assumption that the use of ChatGPT in marketing can improve the quality of customer service, increase brand engagement, and increase customer loyalty. Summary of the main research material. The study examines the results of using ChatGPT in marketing strategies, in particular in online communications, especially in areas where personal interaction with customers is important. The working hypotheses were confirmed based on the analysis of data and information collected during the study. Possible limitations and challenges related to the implementation of ChatGPT in marketing practices were identified. The originality and practical significance of the study lies in the fact that the analysis helped to reveal the potential of using ChatGPT in marketing strategies and made it possible to formulate recommendations on the optimal use of this technology to attract and retain customers. Conclusions and prospects for further research. The conducted research allowed us to state that the use of ChatGPT in marketing contributes to increasing the efficiency of marketing activities of the enterprise in general and improving communications with customers in particular. Further research may include the development of new algorithms and strategies for the optimal use of artificial intelligence in marketing. (shrink)

The use of artificial intelligence in philosophy opens new avenues for inquiry, particularly through dialogical methods inspired by the Socratic tradition. This paper exemplifies the engagement with ChatGPT-4o by OpenAI as a philosophical interlocutor, highlighting how this format facilitates a clear distinction between the philosopher's contributions and those generated by the AI. By allowing the philosopher to lead the dialogue, this technique liberates him from the constraints of drafting and formal writing, enabling a more spontaneous exploration of ideas. The (...) conversation begins with a foundational definition of intelligence and progresses toward a deeper and philosophically relevant understanding. This exchange not only showcases the potential of AI to enrich philosophical discourse but also emphasizes the significance of critical questioning in examining complex concepts such as intelligence and the implications of artificial cognition. (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)

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)

Dear Colleagues, -/- Recently, for an article submitted to the European Journal of Therapeutics, it was reported that the paper may have been written with artificial intelligence support at a rate of more than 50% in the preliminary examination made with Turnitin. However, the authors did not mention this in the article’s material method or explanations section. Fortunately, the article’s out-of-date content and fundamental errors in its methodology allowed us no difficulty making the desk rejection decision. -/- On the other (...) hand, similar situations that we may encounter later caused us to discuss how we would decide when the artificial intelligence support of the articles was written. The general opinion that we have adopted and currently available in the literature is that if artificial intelligence is used while writing an article, how artificial intelligence is used in the methodology should be written in detail. -/- Moreover, we encountered a much more exciting situation during our evaluation. In a few academic studies, we have seen that artificial intelligence is added as a co-author. On July 06, 2023, in the Web of Science, using the advanced search, we found four articles with the author name ChatGPT [1]. We have determined that ChatGPT is the author in one of these articles [2] and the Group Author in three [3-5]. -/- Lee [6] stated that although artificial intelligence tools are much more advanced than search engines, they cannot be an author regarding research ethics because they cannot take responsibility for what they write. Similarly, Goto and Katanoda [7] stated that it is the author’s responsibility to confirm that the texts written by ChatGPT are correct. However, Pourhoseingholi et al. [8] reported that keeping up with technology is inevitable. Additionally, they said that“this action will be more fruitful and practical in extended dimensions when international institutes like ICMJE or COPE come up with the appropriate adjustments and establish robust criteria to scheme the AI authorship”. -/- Most probably, the use of artificial intelligence applications in scientific articles and whether it can be a co-author in these papers will be discussed soon. -/- We encourage interested authors to submit their ideas to our journal as a letter to the editor. -/- Yours sincerely,. (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)

ChatGPT – a large language model – recently passed the U.S. bar exam. The startling rise and power of generative artificial intelligence (AI) systems such as ChatGPT lead us to consider whether and how more specialized systems could be used to overcome existing barriers to the legal system. Such systems could be employed in either of the two major stages of the pursuit of justice: preliminary information gathering and formal engagement with the state’s legal institutions and professionals. We (...) focus on the former and argue that developing and deploying publicly funded AI legal advisors can reduce economic and shame-based cultural barriers to the information-gathering stage of pursuing justice. (shrink)

Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two-premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of (...) ‘survival stories’, in which humanity survives into the far future. In each survival story, one of the two premises fails. Either scientific barriers prevent AI systems from becoming extremely powerful; or humanity bans research into AI systems, thereby preventing them from becoming extremely powerful; or extremely powerful AI systems do not destroy humanity, because their goals prevent them from doing so; or extremely powerful AI systems do not destroy humanity, because we can reliably detect and disable systems that have the goal of doing so. We argue that different survival stories face different challenges. We also argue that different survival stories motivate different responses to the threats from AI. Finally, we use our taxonomy to produce rough estimates of ‘P(doom)’, the probability that humanity will be destroyed by AI. (shrink)

Generative AI systems like ChatGPT and Midjourney can produce prose or images. But can they produce art? I argue that this question, though natural and intriguing, is the wrong one to ask. A better question is this: can generative AI yield distinct or novel forms of aesthetic value? And I argue that the answer is yes. Generative AI can be used to put us in contact with the artificial sublime – a type of aesthetic value that Kant famously argues (...) is impossible. Kant claims that sublimity (a fusion of trepidation and wonderment) can only arise from encounters with nature, or perhaps close artistic depiction of natural scenes. I show how generative AI can yield encounters both with incalculable profundity (Kant’s mathematical sublime) as well as overwhelming moral chaos (Kant’s dynamical sublime). There are lessons not only for art criticism, but also for the philosophical theory of value. (shrink)

Limits need to be set on AI’s ability to simulate human feelings. Ensuring that chatbots don’t use emotive language, including emojis, would be a good start. Emojis are particularly manipulative. Humans instinctively respond to shapes that look like faces — even cartoonish or schematic ones — and emojis can induce these reactions.

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)