This research offers a concise analysis of leisure science, leisure studies, and the concept of leisure space-time. It explores the interdisciplinary nature of leisure science, drawing from psychology, sociology, and economics. The research article of corresponding report writing on desk examines leisure studies that contribute to understanding individual and societal leisure behaviors, motivations, and benefits. Additionally, it delves into the notion of leisure space-time, investigating the design and utilization of spaces for leisure activities. This research provides valuable insights into the (...) multifaceted realm of leisure, shedding light on its significance in contemporary society. (shrink)

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by (...) social effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence. (shrink)

OpenAI’s Chat Generative Pre-training Transformer (ChatGPT), Google’s Bard and other generative artificial intelligence (GenAI) technologies can greatly enhance the capability of healthcare profess...

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)

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)

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...

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)

In recent years, state-of-the-art artificial intelligence systems have started to show signs of what might be seen as human level intelligence. More specifically, large language models such as OpenAI’s GPT-3, and more recently Google’s PaLM and DeepMind’s GATO, are performing amazing feats involving the generation of texts. However, it is acknowledged by many researchers that contemporary language models, and more generally, learning systems, still lack important capabilities, such as understanding, reasoning and the ability to employ knowledge of the world (...) and common sense in order to reach or at least advance toward general intelligence. Some believe that scaling will eventually bring about these capabilities; others think that a different architecture is needed. In this paper, we focus on the latter, with the purpose of integrating a theoretical–philosophical conception of understanding as knowledge of dependence relations, with the high-level requirements and engineering design of a robust AI system, which integrates machine learning and symbolic components. (shrink)

As if 2020 were not a peculiar enough year, its fifth month has seen the relatively quiet publication of a preprint describing the most powerful Natural Language Processing (NLP) system to date, GPT-3 (Generative Pre-trained Transformer-3), by Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial Beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this Artificial Intelligence can comprehend (...) prompts in natural language, on virtually any topic, and generate relevant, original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public as of the time of this writing. This paper presents select examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. We use these examples to raise specific, fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. Our goal is to instigate not just a sense of urgency, but of a present tardiness on the part of the academic community in addressing these questions. (shrink)

The arrival of image-making generative artificial intelligence (AI) programs has been met with a broad rebuke: to many, it feels inherently wrong to regard images made using generative AI programs as artworks. I am skeptical of this sentiment, and in what follows I aim to demonstrate why. I suspect AI generated images can be considered artworks; more specifically, that generative AI programs are, in many cases, just another tool artists can use to realize their creative intent. I begin with an (...) overview of how generative AI programs, like OpenAI’s DALL-E 2, work. Then, leveraging work by Claire Anscomb, I argue that generative AI programs are a new technique of automatic image-making that affords creative agency to its users, thereby qualifying the images they create as artworks. Finally, I show many of the objections brought against AI artworks—including accusations of plagiarism and artistic devaluation—are due to the social backdrop in which we currently find them, rather than the technology itself. In the end, I aim to open the door to further aesthetic debate concerning AI generated images and art. (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)

As if 2020 was not a peculiar enough year, its fifth month saw the relatively quiet publication of a preprint describing the most powerful natural language processing (NLP) system to date—GPT-3 (Generative Pre-trained Transformer-3)—created by the Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this artificial intelligence can comprehend prompts (...) in natural language, on virtually any topic, and generate relevant original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public. This paper presents examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. I use these examples to raise specific fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. The goal is to instigate a sense of urgency, as well as a sense of present tardiness on the part of the academic community in addressing these questions. (shrink)

This article sets in with the question whether current or foreseeable transformer-based large language models (LLMs), such as the ones powering OpenAI’s ChatGPT, could be language users in a way comparable to humans. It answers the question negatively, presenting the following argument. Apart from niche uses, to use language means to act. But LLMs are unable to act because they lack intentions. This, in turn, is because they are the wrong kind of being: agents with intentions need to be (...) autonomous organisms while LLMs are heteronomous mechanisms. To conclude, the article argues, based on structural aspects of transformer-based LLMs, that these LLMs have taken a first step away from mechanistic artificiality to autonomous self-constitution, which means that these models are (slowly) moving into a direction that someday might result in non-human, but equally non-artificial agents, thus subverting the time-honored Kantian distinction between organism and mechanism. (shrink)

Named entity recognition (NER) is a very relevant task for text information retrieval in natural language processing (NLP) problems. Most recent state-of-the-art NER methods require humans to annotate and provide useful data for model training. However, using human power to identify, circumscribe and label entities manually can be very expensive in terms of time, money, and effort. This paper investigates the use of prompt-based language models (OpenAI’s GPT-3) and weak supervision in the legal domain. We apply both strategies as (...) alternative approaches to the traditional human-based annotation method, relying on computer power instead human effort for labeling, and subsequently compare model performance between computer and human-generated data. We also introduce combinations of all three mentioned methods (prompt-based, weak supervision, and human annotation), aiming to find ways to maintain high model efficiency and low annotation costs. We showed that, despite human labeling still maintaining better overall performance results, the alternative strategies and their combinations presented themselves as valid options, displaying positive results and similar model scores at lower costs. Final results demonstrate preservation of human-trained models scores averaging 74.0% for GPT-3, 95.6% for weak supervision, 90.7% for GPT + weak supervision combination, and 83.9% for GPT + 30% human-labeling combination. (shrink)

The advent of ChatGPT by OpenAI has prompted extensive discourse on its potential implications for science and higher education. While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice. To investigate this further, we conducted a Delphi study involving 72 researchers specializing in AI and digitization. The study focused on applications and limitations of LLMs, their effects on the (...) science system, ethical and legal considerations, and the required competencies for their effective use. Our findings highlight the transformative potential of LLMs in science, particularly in administrative, creative, and analytical tasks. However, risks related to bias, misinformation, and quality assurance need to be addressed through proactive regulation and science education. This research contributes to informed discussions on the impact of generative AI in science and helps identify areas for future action. (shrink)

Information we use to structure our lives is increasingly stored digitally, rather than in biomemory. (Just think: if your online calendar went down, would you know where you are supposed be and at what time next week?) Likewise, with breakthroughs such as those from Google DeepMind and OpenAI, discoveries at the frontiers of knowledge are increasingly due to machine learning (often, applied to massive datasets, extracted from a fast-growing datasphere) rather than to brainbound cognition. It’s hard to deny that (...) knowledge retention and production is becoming increasingly – in various ways – digitised. -/- Digital Knowledge: A Philosophical Investigation is the first book to squarely and rigorously investigate digital knowledge: what it is, how to make sense of it in connection with received theories of knowledge, and where it is going. Key questions J Adam Carter examines along the way are the following: -/- How is mere digital information converted into reliable digital knowledge? To what extent can digital knowledge be vindicated against sceptical challenges, and in what ways might digital knowledge stand distinctively subject to defeat? What is the epistemically optimal way for us to decide which tasks to outsource entirely to intelligent machines, and to what extent is further outsourcing appropriate (or not) to verify the results of that same outsourced cognition? Are there any ways in which the expansion of the datasphere threatens to make knowledge more, rather than easy, to come by? If so, are there any promising ways to safeguard, epistemically, against such threats? Using fascinating examples throughout, such as the recent chess match between Stockfish and Google’s AlphaZero, smartphones and personalisation, Digital Knowledge: A Philosophical Investigation is an ideal for researchers investigating this fascinating area of research at the intersection of traditional mainstream epistemology, the philosophy of cognitive science, the philosophy of technology, and computer science. (shrink)

The Buddhist Jātaka tells the tale of a hare lounging under a palm tree who becomes convinced the Earth is coming to an end when a ripe bael fruit falls on its head. Soon all the hares are running; other animals join them, forming a stampede of deer, boar, elk, buffalo, wild oxen, rhinoceros, tigers and elephants, loudly proclaiming the earth is ending.1 In the American retelling, the hare is ‘chicken little,’ and the exaggerated fear is that the sky is (...) falling. The story offers a cautionary tale for considering the trend towards calamity thinking in artificial intelligence (AI). A growing chorus of tech leaders has warned that AI poses existential risk (X-Risk) that could result in the extinction of the human species, the collapse of civilisation, or a colossal decline in human potential and culture. In 2014, Hawking told the Washington Post that AI, ‘could spell the end of the human race’.2 Musk has repeatedly warned about AI’s perils, calling AI, ‘more dangerous than nukes’, recommending colonising Mars to ensure ‘a bolt-hole if AI goes rogue and turns on humanity’,3 and donating 10 million dollars to the Future of Life Institute to jumpstart research on AI’s X-Risk. Gates has stated, ‘I am in the camp…concerned about super intelligence…I agree with … Musk and some others on this and don’t understand why some people are not concerned’.4 Altman, CEO of OpenAI, has fretted that technologies OpenAI was building could endanger humanity—‘even destroying the world as we know it’.5 This paper offers a critical appraisal of the rise of calamity thinking in the scholarly AI ethics literature. It cautions against viewing X-Risk in isolation and highlights ethical considerations sidelined when X-Risk takes centre stage. Section I introduces a working definition of X-Risk, considers its likelihood and …. (shrink)

We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, as well (...) as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld. (shrink)

Can large language models be trained to produce philosophical texts that are difficult to distinguish from texts produced by human philosophers? To address this question, we fine-tuned OpenAI's GPT-3 with the works of philosopher Daniel C. Dennett as additional training data. To explore the Dennett model, we asked the real Dennett ten philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry-picking. We recruited 425 participants to distinguish Dennett's answer (...) from the four machine-generated answers. Experts on Dennett's work (N = 25) succeeded 51% of the time, above the chance rate of 20% but short of our hypothesized rate of 80% correct. For two of the ten questions, the language model produced at least one answer that experts selected more frequently than Dennett's own answer. Philosophy blog readers (N = 302) performed similarly to the experts, while ordinary research participants (N = 98) were near chance distinguishing GPT-3's responses from those of an "actual human philosopher". (shrink)

Artificial Intelligence (AI) applied to the legal domain is gaining ground. AI is argued to be particularly helpful with labour-intensive activities and repetitive tasks. Amongst the various AI solutions, ChatGPT has gathered momentum and its acclaimed advantages are, amongst others, document generation and contract review. This paper wishes to assess the effectiveness of two chatbots in contract drafting. To this aim, ChatGPT (by OpenAI) and Gemini (by Google) are prompted to write two supply contracts each, the first one written (...) in English according to the laws of England and Wales, and the other one still written in English, but according to the laws of the Republic of Italy. The paper finds that each contract pair generated by the AI solutions is identical, thus disregarding legal-system specificities and language conventions. Therefore, the drafts present a number of fallacies, either because they are incomplete, or because they include clauses that are redundant or inapplicable in a given legal system. The paper claims that human intervention and supervision are necessary to tackle the shortcomings generated by AI, at least as far as contract generation is concerned and with regard to the two chatbots addressed. (shrink)

Artificial intelligence is widely seen as the most important technology of our time and has attracted a huge amount of interest regarding not only its technological capabilities but also its economic and ethical impact. It has, however, been less discussed with regard to the book world and publishing. AI is applicable to publishing at multiple levels, all of them significant. It could change the business-processing aspects of publishing; but, even more importantly, the latest advances in machine learning from the likes (...) of Google and OpenAI demonstrate a powerful capacity to translate and even compose text. Publishing and publishing studies alike need to grapple with the implications of a world where machines as well as humans can produce the core product. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Editor’s NoteJessica HeybachThis final installation of Education and Culture’s special theme issue on Dewey, Data, and Technology coincides with what feels like a technological paradigm shift. As I sat down to write this editor’s note, a former student forwarded me Stephen Marche’s December 6, 2022 piece in The Atlantic titled “The College Essay is Dead” wherein he offers a critique of the humanities as dependent on traditional forms of (...) assignments and assessment (i.e. writing), and their historically persistent inability to sufficiently respond to technology and be in conversation with the technologists. Ultimately Marche argues that the humanists and technologists need each other now more than ever with the development of naturallanguage processing. In November 2022, OpenAI released ChatGPT (GPT stands for generative pre-trained transformer), a large language model chatbot capable of answering any question posed by a user. From “What was John Dewey’s theory of experience?” to chocolate cake recipes, ChatGPT offers users answers to their questions by predicting text (similar to autocorrect). The chatbot can answer fairly sophisticated questions, translate text, and write code, all of which previously used to require a human curator to achieve. Interestingly, ChatGPT was trained through a model of learning reflective of what B.F. Skinner (1938) proposed in The Behavior of Organisms: An Experimental Analysis.1 Specifically, to train ChatGPT, creators utilized reinforcement learning and a system of rewards: “a human ‘labeler’ assigns rewards to various outputs the model generates by ranking them from the best to the worst. Thus, ChatGPT is trained by incorporating human intuition—specifically in terms of preferences in verbal combinations—and learning from it.”2Like all educational technologies, ChatGPT has been met with polarizing reactions—you either love it or hate it. Yet without much debate, educators and educational consultants immediately absorbed the technology as an educational good and started proselytizing. With little regard as to how the technology works, who created it, or why it was created, many educators have become early adopters. What is most curious about educators’ response is how many are not alarmed by the inaccuracy of the text produced by ChatGPT (imagine if calculators gave us wrong answers), or the issues of privacy inherent in becoming a user of AI (imagine if the overhead projector sold our behavioral data to other corporate entities), or the notion of academic integrity and thought itself in the face of AI (imagine if the blackboard became a coauthor). Rather, our addiction to novelty and technological fantasies is on display in this technological paradigm shift. Most universities now have webpages filled with links to resources and have filled their professional development calendars with AI-related webinars related to revising [End Page 1] assignments and assessments and creating syllabus policies for AI-produced materials.Although there is much to debate and decades of scholarship will interrogate ChatGPT in all disciplines, for the purposes of this editor’s note I want to pause and focus on the reactive early adopters of ChatGPT. As a regular observer of the educational Twitter-verse, I’m struck by how quickly this technology has been championed by school leaders and classroom educators. Since educators are users only, rather than the designers of said technology, ChatGPT in the classroom can be seen as the ultimate form of outsourcing available to date. I want to emphasize that ChatGPT is different than the calculator, overhead projector, or blackboard, as it is part of a culture already in motion that is interested in the destruction of the teaching profession writ large. Of course, Dewey would caution us against either/or thinking and the fatalism embedded in my own early reaction to ChatGPT, but I implore educators to consider ChatGPT in light of recent political attacks in P-20 educational settings and the increasing legislative moves to destroy progressive and reconstructive educational aims of education.One educational consultant declared just one month after its release: “#ChatGPT is here to stay and efforts against it are futile.” This consultant goes on to list its usefulness in the following tasks: “generating lesson plans, creating quizzes, providing feedback on student writing, summarizing and paraphrasing texts, personalized learning, grading student work, generating... (shrink)

Goldschmidt’s evocation of Leviticus 19:18 in Contradiction Set Free accomplishes heavy lifting within the distinction of the dialogic from the dialectic. Analogized to a necessary recognition of each particular and unique fulfillment of the immediate command to “love your neighbor as yourself,” dialogue is temporalized within an already near, yet not ever complete, messianic infinite. As an ongoing, active and unfinished composition of unique “nows,” dialogue’s structure is likewise epistemically distinct from the structure of dialectical synthesis. How might this distinction’s (...) lens of Leviticus 19:18 illumine opportunities—and obstacles—for “dialogue” between humans and artificial systems? Does examining the specific case of OpenAI’s ChatGPT under the lens of Goldschmidt’s text suggest that certain questions about generative AI learning capacity might actually be questions of time? (shrink)

Artificial Intelligence (AI) tools and digital infrastructures are rapidly advancing. In early November 2023, OpenAI unveiled ChatGPT-4 Turbo, an enhanced ChatGPT model. This next generation model...

John Lennox published 2084 in 2020, several years prior to the unveiling of OpenAI’s ChatGPT to the world in November 2022. ChatGPT and its rivals such as Google’s Gemini displayed astonishing capa...