Postphenomenology, Phronesis, and the Physician: Cancer Care in Radiogenomic Artificial Intelligence Theranostics.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

THE MYTH OF ARTIFICIAL INTELLIGENCE: Why Computers Can't Think the Way We Do by Erik J. Larson. Cambridge, MA: Belknap Press, 2021. 312 pages. Hardcover; $29.95. ISBN: 9780674983519. *The Myth of Artificial Intelligence (AI) offers a technical and philosophical introduction to AI with an emphasis on AI's limitations. Larson, a computer scientist and tech entrepreneur, keeps his central claim modest: true general AI is neither inevitable nor imminent, and if it is possible, it will require fundamentally new approaches. It is an easy read, combining references to fiction, history, and science. It lays out a bird's eye view of the origins and ideas behind current AI methods, focusing on general AI, a category of AI that would need to learn and engage with a wide variety of problems. *Separated into three parts, The Myth of AI begins with the history and algorithmic logic of AI, largely through the lens of the Turing test. Larson argues that we are not near the singularity (superintelligent computers able to create ever more intelligent machines) and that, in fact, the basic premise of the singularity is flawed. *The second part discusses inference. AI falls short of human intelligence because it can work with hard rules, but cannot make the guesses necessary to formulate new ones or handle uncertain rules. In attempts at the Turing test, AI can throw data at the problem but will always lack understanding. Achieving the understanding necessary for true intelligence will require an approach fundamentally different from recent advances made in AI, which are only effective for narrow AI (a category of AI for solving specialized problems) and not general AI. *The final, and relatively brief, part examines AI in science. According to Larson's assessment, new scientific research relies heavily on newly available computation power and big data in order to use narrow AI to its full extent. Larson claims that this approach will hinder development of new theories. He also claims that this leads to treating scientists as if they were computers as well, which causes overvaluing the system of science above people. He criticizes "swarm science," which he describes as a large group of scientists approaching one problem with a variety of projects, emphasizing this collaboration over the individuals. Instead, he claims, we need our culture to continue to emphasize individual discovery and intelligence, as it is the key to innovation. *Through the discussions of the history, philosophy, and logic of AI in the first two parts of the book, Larson disentangles the hype of AI from what is actually possible with current technology. Even as he sheds light on the gap between the singularity prediction and what machine learning is truly capable of, he emphasizes the significance of the myth. "The myth is an emotional lighthouse by which we navigate the AI topic" (p. 76). The stories we tell through predictions and science fiction define AI in the public eye and set the goals for AI research. *Our underlying philosophy matters as much as the current state of AI research, when we consider the social role of AI and what we predict for our future. In the development of AI, we must define intelligence and explore what it means to be human. While this is not a book with overtly religious claims, it does acknowledge the spiritual claims inherent in discussions of personhood. It also frames technoscience as replacing philosophy and religion and as the oversimplified understanding of humanity and the precursor to expectations of the singularity. *Beyond the stated goal of disenchanting the reader of the inevitability of AI, the book highlights the significance of stories to both society and science and emphasizes the importance of understanding for both humans and AI. We need to understand not only the technical aspects of the technology we build but also the philosophy that defines our goals. *While I found the first two sections of the book to be an engaging and accurate discussion of the tension between the science and hopes of AI, I had concerns about the warnings of "swarm science" in the third. Larson is placing a strong emphasis on individual genius in science; however, science has never been a truly independent endeavor. Many times in history, from evolution to DNA, multiple teams of scientists independently made the same discoveries at nearly the same time, based on previously published work. Though these discoveries were not inevitable, they built upon other research and relied on collaboration at least as much as individual genius. Larson focuses on a particular neuroscience project and makes some valid criticisms, but then he generalizes his observations to all of science in ways that I do not believe to be accurate. His argument that all of science is moving away from theory toward shallow observations is not as obvious as he claims, nor is it supported by the evidence offered in the book. *As a counterexample, the research that resulted in the COVID-19 vaccine could be considered "swarm science" and was effective. Large amounts of funding were very suddenly directed to many scientists for one goal: understand and prevent the coronavirus. Due to both new funding and established research, we developed and approved multiple vaccines in one year. I was not convinced of several of Larson's generalizations in this third section. Tension between celebrating collaboration and individual genius will persist. However, it appears that there is more collaboration in science today. This is likely due to a variety of reasons, including a scientific community connected by the internet and more contributors receiving appropriate credit for their work. *The Myth of AI is a broad view of AI that should prove valuable and comprehensible to readers with or without a technical background. The first two sections offer a clear explanation and history of AI, and the third offers food for thought on how the process of science has been shaped by advances in AI and computer technology. The first sections would be a good introduction to someone not familiar with AI or looking to think about the philosophy of AI and I would recommend the book for these sections. *While the book avoids religious claims, the philosophical discussions of what it means to "understand" and the level of trust we place in AI are essential questions for Christians working in technology-related disciplines. The Myth of AI presents a jumping-off point for much deeper reflection about using AI responsibly and what it means to be human. *Reviewed by Elizabeth Koning, graduate student in the Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801.

The advent of Artificial Intelligence (AI) has led to profound changes in human interaction and cognitive functions, fundamentally transforming the way individuals engage with technology and with each other. AI technologies, including machine learning, natural language processing, and neural networks, have become pervasive in everyday life, shaping communication, learning, and decision-making processes. This article explores the evolving role of AI in shaping human cognition, emphasizing both its positive impacts, such as enhanced cognitive abilities, and the challenges it introduces, including the potential for cognitive dependency. The study draws on an extensive review of existing literature and relevant case studies to investigate the psychological effects of AI, highlighting its influence in key sectors such as education, healthcare, and social media. However, the increasing reliance on AI also presents significant concerns, particularly regarding cognitive dependency. As individuals become more dependent on AI for tasks like memory retention and decision-making, there is a risk of diminishing cognitive engagement and critical thinking abilities. Additionally, the integration of AI into social media raises questions about its impact on interpersonal relationships and mental health, particularly in terms of privacy, data security, and the formation of echo chambers. Ultimately, while AI offers substantial benefits in terms of efficiency and cognitive enhancement, its long-term implications for human cognitive capabilities and behavior warrant careful consideration and further research to address the psychological challenges it poses.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Response to M. Trengove & coll regarding "Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine".

Purpose This study aims to critically examine the socio-technical, economic and governance challenges emerging at the intersection of Generative artificial intelligence (AI) and Urban AI. By foregrounding the metaphor of “the moon and the ghetto” (Nelson, 1977, 2011), the issue invites contributions that interrogate the gap between technological capability and institutional justice. The purpose is to foster a multidisciplinary dialogue–spanning applied economics, public policy, AI ethics and urban governance – that can inform trustworthy, inclusive and democratically grounded AI practices. Contributors are encouraged to explore not just what GenAI can do, but for whom, how and with what consequences. Design/methodology/approach This study draws upon interdisciplinary literature from public policy, innovation studies, digital governance and urban sociology to frame the emerging governance challenges of Generative AI and Urban AI. It builds a conceptual foundation by synthesizing insights from comparative city case studies, innovation systems theory and normative policy frameworks. The approach is interpretive and exploratory, aiming to situate AI technologies within broader institutional, geopolitical and socio-economic contexts. The study invites contributions that adopt empirical, theoretical or practice-based methodologies addressing the governance of GenAI in cities and regions. Findings This study identifies a critical gap between the rapid technological advancements in Generative AI and the institutional readiness of public governance systems – particularly in urban contexts. It finds that current policy frameworks often prioritize efficiency and innovationism over democratic legitimacy, civic trust and inclusive design. Drawing on comparative global city experiences, it highlights the risk of reinforcing power asymmetries without robust accountability mechanisms. The analysis suggests that trustworthy AI is not a purely technical attribute but a political and institutional achievement, requiring participatory governance architectures and innovation systems grounded in public value and civic engagement. Research limitations/implications As an editorial introduction, this study does not present original empirical data but synthesizes key theoretical frameworks, case studies and policy debates to guide future research. Its analytical scope is conceptual and comparative, offering a foundation for submissions that further investigate Generative and Urban AI through empirical, normative and practice-based lenses. The limitations lie in its broad coverage and reliance on secondary sources. Nonetheless, it provides an agenda-setting contribution by highlighting the urgent need for interdisciplinary research into how AI reshapes public governance, institutional legitimacy and urban democratic futures. Practical implications This editorial offers a structured framework for policymakers, urban planners, technologists and public administrators to critically assess the governance of Generative and Urban AI systems. By highlighting international case studies and conceptual tools – such as public algorithmic infrastructures, civic trust frameworks and anticipatory governance – the article underscores the importance of institutional design, regulatory foresight and civic engagement. It invites practitioners to shift from techno-solutionist approaches toward inclusive, democratic and place-based AI governance. The reflections aim to support the development of trustworthy AI policies that are grounded in legitimacy, accountability and societal needs, particularly in urban and regional contexts. Social implications The editorial underscores that Generative and Urban AI systems are not socially neutral but carry significant implications for equity, representation and democratic legitimacy. These technologies risk reinforcing existing social hierarchies and systemic biases if not governed inclusively. This study calls for reimagining trust not as a technical feature but as a relational, contested dynamic between institutions and citizens. It encourages submissions that examine how AI reshapes the urban social contract, affects marginalized communities and challenges existing civic infrastructures. The goal is to promote AI governance frameworks that are pluralistic, just and reflective of diverse societal values and lived experiences. Originality/value This editorial offers a timely and conceptually grounded intervention into the emerging field of Urban AI and Generative AI governance. By framing the challenges through Richard R. Nelson’s metaphor of The Moon and the Ghetto, this study foregrounds the gap between technical capabilities and enduring societal injustices. The contribution lies in its interdisciplinary synthesis – bridging innovation systems, AI ethics, public policy and urban governance. It introduces a critical framework for assessing “trustworthy AI” not as a technical goal but as a democratic achievement and encourages research that is policy-relevant, equity-oriented and attuned to the institutional realities of AI in cities.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

The purpose of the article is to define prospects and limitations of artificial intelligence as a core of in the new industrial revolution. The definition of the concept of AI in the scientific community remains the subject of heated debate. At the same time, in the regulatory and legal plane, a trend is being formed towards unification of the concept of AI. Based on the analysis conducted and literary sources, the following prospects for AI can be identified on theoretical and practical levels. On theoretical level: (1) alienation of tacit knowledge from the individual (employee and entrepreneur); (2) optimization of the planning system; (3) revision of the socialist-calculation debate; (4) decreasing information asymmetry. On practical level: (1) formation of new products and markets; (2) increasing labor and capital productivity; (3) massive creation of new jobs; (4) optimization of business processes; (5) opportunity for rapid growth for small businesses and startups. Limitations: (1) long-term structural unemployment; (2) inflated expectations from AI and, as a consequence, the possible formation of a speculative bubble in the global stock market; (3) energy consumption of AI; (4) outdated pre-AI corporate culture and regulatory environment. Further improvement of AI (including the transition from AI to AGI) and the expansion of its use can make a significant contribution to solving problems related to economic calculation and minimizing information asymmetry, and therefore optimizing transaction costs in the economy. AI, certainly acting as a locally useful tool at the level of individual enterprises and organizations, causes the acceleration of attracting funds to the stock market, which can lead to the formation of a bubble on global level. If this bubble bursts, expectations about the economic efficiency of AI will be revised, and some AI-related companies will experience significant margin reductions (perhaps losses and bankruptcies). But this, in turn, will initiate the next stage of AI development, will accelerate its transition from the current narrow specialization to the creation of full-fledged general artificial intelligence (artificial general intelligence), which has a greater potential to change the economy at all levels. As a result, AI will become established as the core of the new industrial revolution.

This dialogue is from an early scene in the 2014 film Ex Machina, in which Nathan has invited Caleb to determine whether Nathan has succeeded in creating artificial intelligence.1 The achievement of powerful artificial general intelligence has long held a grip on our imagination not only for its exciting as well as worrisome possibilities, but also for its suggestion of a new, uncharted era for humanity. In opening his 2021 BBC Reith Lectures, titled "Living with Artificial Intelligence," Stuart Russell states that "the eventual emergence of general-purpose artificial intelligence [will be] the biggest event in human history."2Over the last decade, a rapid succession of impressive results has brought wider public attention to the possibilities of powerful artificial intelligence. In machine vision, researchers demonstrated systems that could recognize objects as well as, if not better than, humans in some situations. Then came the games. Complex games of strategy have long been associated with superior intelligence, and so when AI systems beat the best human players at chess, Atari games, Go, shogi, StarCraft, and Dota, the world took notice. It was not just that Als beat humans (although that was astounding when it first happened), but the escalating progression of how they did it: initially by learning from expert human play, then from self-play, then by teaching themselves the principles of the games from the ground up, eventually yielding single systems that could learn, play, and win at several structurally different games, hinting at the possibility of generally intelligent systems.3Speech recognition and natural language processing have also seen rapid and headline-grabbing advances. Most impressive has been the emergence recently of large language models capable of generating human-like outputs. Progress in language is of particular significance given the role language has always played in human notions of intelligence, reasoning, and understanding. While the advances mentioned thus far may seem abstract, those in driverless cars and robots have been more tangible given their embodied and often biomorphic forms. Demonstrations of such embodied systems exhibiting increasingly complex and autonomous behaviors in our physical world have captured public attention.Also in the headlines have been results in various branches of science in which AI and its related techniques have been used as tools to advance research from materials and environmental sciences to high energy physics and astronomy.4 A few highlights, such as the spectacular results on the fifty-year-old protein-folding problem by AlphaFold, suggest the possibility that AI could soon help tackle science's hardest problems, such as in health and the life sciences.5While the headlines tend to feature results and demonstrations of a future to come, AI and its associated technologies are already here and pervade our daily lives more than many realize. Examples include recommendation systems, search, language translators - now covering more than one hundred languages - facial recognition, speech to text (and back), digital assistants, chatbots for customer service, fraud detection, decision support systems, energy management systems, and tools for scientific research, to name a few. In all these examples and others, AI-related techniques have become components of other software and hardware systems as methods for learning from and incorporating messy real-world inputs into inferences, predictions, and, in some cases, actions. As director of the Future of Humanity Institute at the University of Oxford, Nick Bostrom noted back in 2006, "A lot of cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."6As the scope, use, and usefulness of these systems have grown for individual users, researchers in various fields, companies and other types of organizations, and governments, so too have concerns when the systems have not worked well (such as bias in facial recognition systems), or have been misused (as in deepfakes), or have resulted in harms to some (in predicting crime, for example), or have been associated with accidents (such as fatalities from self-driving cars).7Dædalus last devoted a volume to the topic of artificial intelligence in 1988, with contributions from several of the founders of the field, among others. Much of that issue was concerned with questions of whether research in AI was making progress, of whether AI was at a turning point, and of its foundations, mathematical, technical, and philosophical-with much disagreement. However, in that volume there was also a recognition, or perhaps a rediscovery, of an alternative path toward AI - the connectionist learning approach and the notion of neural nets-and a burgeoning optimism for this approach's potential. Since the 1960s, the learning approach had been relegated to the fringes in favor of the symbolic formalism for representing the world, our knowledge of it, and how machines can reason about it. Yet no essay captured some of the mood at the time better than Hilary Putnam's "Much Ado About Not Very Much." Putnam questioned the Dædalus issue itself: "Why a whole issue of Dædalus? Why don't we wait until AI achieves something and then have an issue?" He concluded:This volume of Dædalus is indeed the first since 1988 to be devoted to artificial intelligence. This volume does not rehash the same debates; much else has happened since, mostly as a result of the success of the machine learning approach that was being rediscovered and reimagined, as discussed in the 1988 volume. This issue aims to capture where we are in AI's development and how its growing uses impact society. The themes and concerns herein are colored by my own involvement with AI. Besides the television, films, and books that I grew up with, my interest in AI began in earnest in 1989 when, as an undergraduate at the University of Zimbabwe, I undertook a research project to model and train a neural network.9 I went on to do research on AI and robotics at Oxford. Over the years, I have been involved with researchers in academia and labs developing AI systems, studying AI's impact on the economy, tracking AI's progress, and working with others in business, policy, and labor grappling with its opportunities and challenges for society.10The authors of the twenty-five essays in this volume range from AI scientists and technologists at the frontier of many of AI's developments to social scientists at the forefront of analyzing AI's impacts on society. The volume is organized into ten sections. Half of the sections are focused on AI's development, the other half on its intersections with various aspects of society. In addition to the diversity in their topics, expertise, and vantage points, the authors bring a range of views on the possibilities, benefits, and concerns for society. I am grateful to the authors for accepting my invitation to write these essays.Before proceeding further, it may be useful to say what we mean by artificial intelligence. The headlines and increasing pervasiveness of AI and its associated technologies have led to some conflation and confusion about what exactly counts as AI. This has not been helped by the current trend-among researchers in science and the humanities, startups, established companies, and even governments-to associate anything involving not only machine learning, but data science, algorithms, robots, and automation of all sorts with AI. This could simply reflect the hype now associated with AI, but it could also be an acknowledgment of the success of the current wave of AI and its related techniques and their wide-ranging use and usefulness. I think both are true; but it has not always been like this. In the period now referred to as the AI winter, during which progress in AI did not live up to expectations, there was a reticence to associate most of what we now call AI with AI.Two types of definitions are typically given for AI. The first are those that suggest that it is the ability to artificially do what intelligent beings, usually human, can do. For example, artificial intelligence is:The human abilities invoked in such definitions include visual perception, speech recognition, the capacity to reason, solve problems, discover meaning, generalize, and learn from experience. Definitions of this type are considered by some to be limiting in their human-centricity as to what counts as intelligence and in the benchmarks for success they set for the development of AI (more on this later). The second type of definitions try to be free of human-centricity and define an intelligent agent or system, whatever its origin, makeup, or method, as:This type of definition also suggests the pursuit of goals, which could be given to the system, self-generated, or learned.13 That both types of definitions are employed throughout this volume yields insights of its own.These definitional distinctions notwithstanding, the term AI, much to the chagrin of some in the field, has come to be what cognitive and computer scientist Marvin Minsky called a "suitcase word."14 It is packed variously, depending on who you ask, with approaches for achieving intelligence, including those based on logic, probability, information and control theory, neural networks, and various other learning, inference, and planning methods, as well as their instantiations in software, hardware, and, in the case of embodied intelligence, systems that can perceive, move, and manipulate objects.Three questions cut through the discussions in this volume: 1) Where are we in AI's development? 2) What opportunities and challenges does AI pose for society? 3) How much about AI is really about us?Notions of intelligent machines date all the way back to antiquity.15 Philosophers, too, among them Hobbes, Leibnitz, and Descartes, have been dreaming about AI for a long time; Daniel Dennett suggests that Descartes may have even anticipated the Turing Test.16 The idea of computation-based machine intelligence traces to Alan Turing's invention of the universal Turing machine in the 1930s, and to the ideas of several of his contemporaries in the mid-twentieth century. But the birth of artificial intelligence as we know it and the use of the term is generally attributed to the now famed Dartmouth summer workshop of 1956. The workshop was the result of a proposal for a two-month summer project by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon whereby "An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves."17In their respective contributions to this volume, "From So Simple a Beginning: Species of Artificial Intelligence" and "If We Succeed," and in different but complementary ways, Nigel Shadbolt and Stuart Russell chart the key ideas and developments in AI, its periods of excitement as well as the aforementioned AI winters. The current AI spring has been underway since the 1990s, with headline-grabbing breakthroughs appearing in rapid succession over the last ten years or so: a period that Jeffrey Dean describes in the title of his essay as a "golden decade," not only for the pace of AI development but also its use in a wide range of sectors of society, as well as areas of scientific research.18 This period is best characterized by the approach to achieve artificial intelligence through learning from experience, and by the success of neural networks, deep learning, and reinforcement learning, together with methods from probability theory, as ways for machines to learn.19A brief history may be useful here: In the 1950s, there were two dominant visions of how to achieve machine intelligence. One vision was to use computers to create a logic and symbolic representation of the world and our knowledge of it and, from there, create systems that could reason about the world, thus exhibiting intelligence akin to the mind. This vision was most espoused by Allen Newell and Hebert Simon, along with Marvin Minsky and others. Closely associated with it was the "heuristic search" approach that supposed intelligence was essentially a problem of exploring a space of possibilities for answers. The second vision was inspired by the brain, rather than the mind, and sought to achieve intelligence by learning. In what became known as the connectionist approach, units called perceptrons were connected in ways inspired by the connection of neurons in the brain. At the time, this approach was most associated with Frank Rosenblatt. While there was initial excitement about both visions, the first came to dominate, and did so for decades, with some successes, including so-called expert systems.Not only did this approach benefit from championing by its advocates and plentiful funding, it came with the suggested weight of a long intellectual tradition-exemplified by Descartes, Boole, Frege, Russell, and Church, among others-that sought to manipulate symbols and to formalize and axiomatize knowledge and reasoning. It was only in the late 1980s that interest began to grow again in the second vision, largely through the work of David Rumelhart, Geoffrey Hinton, James McClelland, and others. The history of these two visions and the associated philosophical ideas are discussed in Hubert Dreyfus and Stuart Dreyfus's 1988 Dædalus essay "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint."20 Since then, the approach to intelligence based on learning, the use of statistical methods, back-propagation, and training (supervised and unsupervised) has come to characterize the current dominant approach.Kevin Scott, in his essay "I Do Not Think It Means What You Think It Means: Artificial Intelligence, Cognitive Work & Scale," reminds us of the work of Ray Solomonoff and others linking information and probability theory with the idea of machines that can not only learn, but compress and potentially generalize what they learn, and the emerging realization of this in the systems now being built and those to come. The success of the machine learning approach has benefited from the boon in the availability of data to train the algorithms thanks to the growth in the use of the Internet and other applications and services. In research, the data explosion has been the result of new scientific instruments and observation platforms and data-generating breakthroughs, for example, in astronomy and in genomics. Equally important has been the co-evolution of the software and hardware used, especially chip architectures better suited to the parallel computations involved in data- and compute-intensive neural networks and other machine learning approaches, as Dean discusses.Several authors delve into progress in key subfields of AI.21 In their essay, "Searching for Computer Vision North Stars," Fei-Fei Li and Ranjay Krishna chart developments in machine vision and the creation of standard data sets such as ImageNet that could be used for benchmarking performance. In their respective essays "Human Language Understanding & Reasoning" and "The Curious Case of Commonsense Intelligence," Chris Manning and Yejin Choi discuss different eras and ideas in natural language processing, including the recent emergence of large language models comprising hundreds of billions of parameters and that use transformer architectures and self-supervised learning on vast amounts of data.22 The resulting pretrained models are impressive in their capacity to take natural language prompts for which they have not been trained specifically and generate human-like outputs, not only in natural language, but also images, software code, and more, as Mira Murati discusses and illustrates in "Language & Coding Creativity." Some have started to refer to these large language models as foundational models in that once they are trained, they are adaptable to a wide range of tasks and outputs.23 But despite their unexpected performance, these large language models are still early in their development and have many shortcomings and limitations that are highlighted in this volume and elsewhere, including by some of their developers.24In "The Machines from Our Future," Daniela Rus discusses the progress in robotic systems, including advances in the underlying technologies, as well as in their integrated design that enables them to operate in the physical world. She highlights the limitations in the "industrial" approaches used thus far and suggests new ways of conceptualizing robots that draw on insights from biological systems. In robotics, as in AI more generally, there has always been a tension as to whether to copy or simply draw inspiration from how humans and other biological organisms achieve intelligent behavior. Elsewhere, AI researcher Demis Hassabis and colleagues have explored how neuroscience and AI learn from and inspire each other, although so far more in one than the other, as and have the success of the current approaches to AI, there are still many shortcomings and as well as problems in It is useful to on one such as when AI does not as or or or that can to or when it on or information about the world, or when it has such as of all of which can to a of public shortcomings have captured the attention of the wider public and as well as among there is an on AI and In recent years, there has been a of to principles and approaches to AI, as well as involving and such as the on AI, that to best important has been the of with to and - in the and developing AI in both and as has been well in recent This is an important in its own but also with to the of the resulting AI and, in its intersections with more the other there are limitations and problems associated with the that AI is not capable of if could to more more or more general AI. In their Turing deep learning and Geoffrey took of where deep learning and highlighted its current such as the with In the case of natural language processing, Manning and Choi the challenges in and despite the of large language Elsewhere, and have the notion that large language models do anything learning, or In & of in a and discuss the problems in systems, the as how to reason about other their systems, and well as challenges in both and especially when the include both humans and Elsewhere, and others a useful of the problems in there is a growing among many that we do not have for the of AI systems, especially as they become more capable and the of use although AI and its related techniques are to be powerful tools for research in science, as examples in this volume and recent examples in which AI not only help results but also by design and become what some have AI to science and and to and challenges for the possibility that more powerful AI could to new in science, as well as progress in some of challenges and has long been a key for many at the frontier of AI research to more capable the of each of AI, the of more general problems that to the possibility of more capable AI learning, reasoning, of and and of these and other problems that could to more capable systems the of whether current characterized by deep learning, the of and and more foundational and and reinforcement or whether different approaches are in such as cognitive agent approaches or or based on logic and probability theory, to name a few. whether and what of approaches be the AI is but many the current along with of and learning architectures have to their about the of the current approaches is associated with the of whether artificial general intelligence can be and if how and Artificial general intelligence is in to what is called that AI and for tasks and goals, such as The development of on the other aims for more powerful AI - at as powerful as is generally to problem or and, in some the capacity to and improve as well as set and its own and the of and when will be is a for most that its achievement have and as is often in and such as A through and The to Ex and it is or there is growing among many at the frontier of AI research that we for the possibility of powerful with to and and with humans, its and use, and the possibility that of could and that we these into how we approach the development of of the research and development, and in AI is of the AI and in its what Nigel Shadbolt the of AI. This is given the for useful and applications and the for in sectors of the However, a few have made the development of their the most of these are and each of which has demonstrated results of increasing still a long way from the most discussed impact of AI and automation is on and the future of This is not In in the of the excitement about AI and and concerns about their impact on a on and the was that such technologies were important for growth and and "the that but not Most recent of this including those I have been involved have and that over time, more are than are that it is the and the and the of will the In their essay AI & and John discuss these for work and further, in & the of & to discuss the with to and and as well as the opportunities that are especially in developing In "The Turing The & of Artificial Intelligence," discusses how the use of human benchmarks in the development of AI the of AI that rather than human He that the AI's development will take in this and resulting for will on the for companies, and a that the that more will be than too much from of the and does not far enough into the future and at what AI will be capable The for AI could from of that in the is and labor and ability to are and and until automation has mostly physical and but that AI will be on more cognitive and tasks based on and, if early examples are even tasks are not of the In other are now in the world machines that that learn and that their ability to do these is to a range of problems they can will be with the range to which the human has been This was and Allen Newell in that this time could be different usually two that new labor will in which will by other humans for their own even when machines may be capable of these as well as or even better than The other is that AI will create so much and all without the for human and the of will be to for when that will the that once the first time since his creation will be with his his to use his from how to the which science and interest will have for to live and and However, most researchers that we are not to a future in which the of will and that until then, there are other and that be in the labor now and in the such as and other and how humans work increasingly capable that and John and discuss in this are not the only of the by AI. Russell a of the potentially from artificial general intelligence, once a of or ten But even we to general-purpose AI, the opportunities for companies and, for the and growth as well as from AI and its related technologies are more than to pursuit and by companies and in the development, and use of AI. At the many the is it is generally that is a in AI, as by its growth in AI research, and as highlighted in several will have for companies and given the of such technologies as discussed by and others the may in the way of approaches to AI and (such as whether they are companies or as and have have the to to in AI. The role of AI in intelligence, systems, autonomous even and other of increasingly In &

How might rapid advances in artificial intelligence (AI) technologies affect the construction and application of military power? Despite the emerging importance of AI systems in defense modernization initiatives, there has been little empirical or theoretical study from the perspective of the international relations (IR) and security studies fields. This article addresses this shortcoming by describing AI developments and assessing the manner in which AI is likely to affect military organizations. We focus specifically on military power, as new methods and modes thereof will alter the constitution of security relationships around the world and affect the ability of states to bargain, signal, and influence in the twenty-first century. We argue that, though rapid adoption of AI technologies stands to transform states’ ways of war on a number of fronts, an AI revolution brings with it new forms of risk that must be reconciled with the widespread integration of algorithmic systems across military functions. Where new technology promises a transformation of the character of military power in some veins, it also complicates the cognitive aspects of decision-making and bureaucratic interactions in security institutions. The speed with which complex integrated AI systems enable entirely new modes of war also stands to detach human agency in a potentially destabilizing fashion from the conduct of warfare on several fronts. Preventing the negative externalities of these “ghosts in the machine” will involve significant efforts to educate decision makers, promote accountability, and restrain irresponsible employment of AI.

Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.

As advancements in artificial intelligence (AI) technology have opened new avenues for artistic expression and creation, human‐AI collaboration in creative activities has garnered increasing attention. Through semi‐structured interviews with 15 participants, we investigate the intrinsic and extrinsic factors that motivate laypeople's engagement with AI painting, as well as challenges and concerns faced by users. Our findings reveal that laypeople engage with AI painting for emotional needs like entertainment, aesthetics, surprise, and curiosity, personal utilitarian needs such as self‐expression and customization, and social interaction through sharing and communication. Despite the appeal of novelty and unpredictability they also encountered challenges related to technical and system functionality, personal and environmental factors, as well as concerns about algorithm bias, pornography misuse, employment risks, copyright disputes and ethical implications. Findings provide preliminary evidence of the potential and limitations of AI in democratizing creative activities, and offer implications for designing and developing of AI assistance tools.

This chapter presents a comprehensive literature review to identify the challenges and limitations of using generative artificial intelligence (GAI) in education. As a result of screening seven major citation databases, 476 studies were reached. Analysis was carried out on 25 studies selected according to the inclusion and exclusion criteria. Results showed that research on using GAI in education is mostly conducted at the higher education level. The number of studies focusing on lower levels of education is quite low. The challenges and limitations of artificial intelligence are more about general education rather than focusing on a specific discipline. ChatGPT was the most investigated GAI tool. The challenges and limitations of using GAI in education are grouped under five factors: ethics and safety; educational implementations; assessment and evaluation; equity and access; quality control and expertise.

How far have we come? Artificial intelligence for chest radiograph interpretation

As artificial intelligence (AI) is becoming more integrated into the field of healthcare, medical students need to learn foundational AI literacy. Yet, traditional, descriptive teaching methods of AI topics are often ineffective in engaging the learners. This article introduces a new application of cinema to teaching AI concepts in medical education. With meticulously chosen movie clips from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie, the students were introduced to the primary differences between artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI). This method triggered encouraging responses from students, with learners indicating greater conceptual clarity and heightened interest. Film as an emotive and visual medium not only makes difficult concepts easy to understand but also encourages curiosity, ethical consideration, and higher order thought. This pedagogic intervention demonstrates how narrative-based learning can make abstract AI systems more relatable and clinically relevant for future physicians. Beyond technical content, the method can offer opportunities to cultivate critical engagement with ethical and practical dimensions of AI in healthcare. Integrating film into AI instruction could bridge the gap between theoretical knowledge and clinical application, offering a compelling pathway to enrich medical education in a rapidly evolving digital age.NEW & NOTEWORTHY This article introduces a new learning strategy that employs film to instruct artificial intelligence (AI) principles in medical education. By introducing clips the from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie to clarify artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI), the approach converted passive learning into an emotionally evocative and intellectually stimulating experience. Students experienced enhanced comprehension and increased interest in artificial intelligence. This narrative-driven, visually oriented process promises to incorporate technical and ethical AI literacy into medical curricula with enduring relevance and impact.