Desert power for the AI era

_ Artificial intelligence (AI) is increasingly being employed to assist in the development of materials, including metal-organic frameworks (MOFs), to develop carbon capture technologies. MOFs are modular materials made up of three building blocks: inorganic nodes such as zinc or copper; organic nodes; and organic linkers made up of carbon, oxygen, and other elements. By changing the relative positions and configurations of the building blocks, the potential combinations for creation of unique MOFs are countless. The idea is to create a porous carbon dioxide “trap” to capture carbon from the air. The structure created by the building blocks can be thought of simplistically as a scaffolding with joints (linkers) that functions to absorb carbon. In a recent paper, researchers from the US Department of Energy Argonne National Laboratory, The University of Illinois Chicago (UIC), University of Illinois, Northwestern University, and TotalEnergies described their use of generative AI to identify “good carbon absorbers” among “billions and billions of possibilities.” Although early work on developing MOFs began in the 1990s, the power of AI modeling can generate new models with desired properties such as optimal selectivity and capacity without the laborious, reiterative experimental and computational efforts once required. In a press release, Argonne said, “… The team was able to quickly assemble, building block by building block, over 120,000 new MOF candidates within 30 minutes” on a supercomputer at the Argonne Leadership Computing Facility. “The race for capturing carbon hinges on finding needles in a haystack, and trial and error is too slow. You have billions and billions of possibilities, and then you must narrow down to candidates that are good carbon absorbers,” said paper coauthor Santanu Chaudhuri, professor of civil, materials, and environmental engineering at UIC and director of manufacturing science and engineering at Argonne, in a news release. “With this project, we have taken the first significant step towards closing that gap by using generative AI.” Coauthor Eliu Huerta, an Argonne computational scientist who helped lead the study, said, “The traditional methods have typically involved experimental synthesis and computational modeling with molecular dynamics simulations. But trying to survey the vast MOF landscape in this way is just impractical. “We wanted to add new flavors to the MOFs that we were designing. We needed new ingredients for the AI recipe,” Huerta said. The AI framework, called GHP-MOFassemble, screened the newly built 120,000 MOFs in 40 minutes to identify nearly 79,000 with valid bonds (joints). Those were screened to identify 19,000 with valid chemistry within 205 minutes. From this group, 364 MOFs were identified in 50 minutes with CO2 capacity higher than a selected value. From assembly to selection of MOFs, GHP-MOFassemble completed the analysis within 5 hours and 7 minutes. Powering the AI Future It’s ironic that processing of massive volumes of data on supercomputers for emissions-related research, such as AI simulations, could contribute to the problem to be solved because of the electricity required. Supercomputers use the computing power of multiple interconnected processing cores, which require an immense amount of energy—and depending on the source of the electricity generation, also contribute to emissions. In a November 2023 commentary, International Energy Agency (IEA) analysts estimated that AI uses more energy than other forms of computing. Training a single model uses more electricity than 100 US homes consume in an entire year. In 2022, Google reported that machine learning accounted for about 15% of its total energy use over the prior 3 years. In a January report, IEA estimated that Google could see a tenfold increase in their electricity demand if AI is fully implemented in its search engine. The average electricity demand of a typical search is 0.3 Wh. OpenAI’s ChatGPT requires 2.9 Wh per request. Considering 9 billion searches per day, nearly 10 TWh of additional electricity would be required in a year. Google and other tech companies are shifting their data operations around the world daily or hourly to tap into excess renewable energy production. Google uses its “carbon-intelligent” platform to analyze day-ahead predictions of how much a given grid will be relying on carbon-intensive energy. It then shifts computing globally in favor of regions where more carbon-free electricity is available. As AI advances in modeling complexity with a greater number of parameters, there is a growing emphasis on sustainability and optimizing power consumption, including developing energy-efficient algorithms, hardware, and data center management strategies. For Further Reading A Generative Artificial Intelligence Framework Based on a Molecular Diffusion Model for the Design of Metal-Organic Frameworks for Carbon Capture by H. Park, X. Yan, and R. Zhu, et al. Communications Chemistry. Why AI and Energy Are the New Power Couple by V. Rozite, J. Miller, and S. Oh, International Energy Agency. Electricity 2024. International Energy Agency. We Now Do More Computing Where There’s Cleaner Energy by R. Koningstein, Google Research.

ImportanceInterest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.ObservationsWhile AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people’s daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model’s behavior. Prompts such as “Write this note for a specialist consultant” and “Write this note for the patient’s mother” will produce markedly different content.Conclusions and RelevanceFoundation models and generative AI represent a major revolution in AI’s capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

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 rise of artificial intelligence (AI) is transforming educational practices, particularly in assessment. While AI may support the students in idea generation and summarization of source materials, it also introduces challenges related to content validity, academic integrity, and the development of critical thinking skills. Educators need strategies to navigate these complexities and maintain rigorous, ethical assessments that promote higher order cognitive skills. This article provides practical guidance for educators on designing take-home assessments (e.g. research-based assignments) in the AI era. This guidance was developed through a collaborative, consensus-driven process involving a consortium of three educators with diverse academic backgrounds, career stages, and perspectives on AI in education. Members, holding experience in higher education across the United Kingdom, United States of America, Australia, and Middle East and North Africa regions, brought varied insights into AI's role in education. The team engaged in an iterative process of refining recommendations through biweekly virtual meetings and offline discussions. Four key recommendations are presented 1) codeveloping AI literacy among students and educators, 2) designing assessments that prioritize process over output, 3) validating learning through AI-free assessments, and 4) preparing students for AI-enhanced workplaces by developing AI communication skills and promoting human-AI collaboration. These strategies emphasize ethical AI use, personalized feedback, and creativity. By adopting these approaches, educators can balance the benefits and risks of AI in assessments, fostering authentic learning while preparing students for the challenges of an AI-driven world.NEW & NOTEWORTHY This paper presents a framework to effectively design take-home assessments in the generative artificial intelligence (AI) era with four key recommendations to navigate the challenges and opportunities posed by generative AI. From codeveloping AI literacy to fostering human-AI collaboration, the strategies empower educators to promote authentic learning, critical thinking, and ethical AI use. Adaptable to various contexts, these insights help prepare students for an AI-driven future while maintaining academic rigor and integrity.

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

Objectives The purpose of this study is to analyze topics of media reports regarding generative artificial intellignece and education. Methods For this purpose, topic model, one of the text mining techniques, was used. First, we collected newspaper article data on ‘Generative Artificial Intelligence and Education’ through the Big Kinds service and then performed pre-processing and morphological analysis. Based on this, 30 keywords were analyzed. By measuring the number of topics and deriving the top 10 words for each topic, 13 topics were presented and explained. Results The 13 topics presented through the research results are as follows. Researchers focus on education that fosters artificial intelligence leadership, Gyeonggi GPT and fair art education, digital finance professional training, problems and responses to digital democracy, reliability and ethical use of artificial intelligence, artificial intelligence used in creation in various fields, training programs between Gyeonggi-do and State University of New York, regional cooperation model for artificial intelligence convergence education, artificial intelligence edutech support project and core talent training, Metaverse Lab support project, strengthening employee artificial intelligence capabilities for use in local government administrative work, Busan Metropolitan Office of Education's digital program attracting attention from overseas and changes in cultural arts education methods in the era of artificial intelligence were presented. Conclusions Based on the topics presented in this study, we proposed ways to apply and activate generative artificial intelligence in education. The five measures are strengthening teacher capacity for generative artificial intelligence, establishing guidelines for the development and use of artificial intelligence, learner capacity to use generative artificial intelligence correctly, developing and disseminating generative artificial intelligence teaching cases, and based on spontaneity. This is support from the Artificial Intelligence Teacher Research Group.

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. 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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.

The study essentially aims to analyze the changes occurring within the practice of dakwah as a form of communicating Islamic messages in the era of digital technology and artificial intelligence. The practice of dakwah must retain its prophetic communication dimension even as it adjusts to new communication formats brought about by the digital era and artificial intelligence. This study employs a qualitative approach with a descriptive-analytical method. This method was chosen for its capacity to provide a profound understanding of complex phenomena within their contexts, aligned with the research objective to analyze the changes in communication patterns and their effects on dakwah practices. Data is gathered through documentation and observation of dakwah practices across various platforms and digital media. The study indicates that the shifts in dakwah communication patterns in the digital era and the advancement of artificial intelligence have significant and intricate impacts on the comprehensive communication of Islamic prophetic messages. Key points include: (1) Changes in the meaning and practice of dakwah depict a shift in focus from the spiritual and moral aspects to the dissemination of Islamic messages through technological platforms; (2) Changes in dakwah communication formats and platforms reflect the adaptation of preachers to digital trends and audience preferences; (3) The influence of social media in dakwah practices opens broad avenues for disseminating Islamic messages; (4) Artificial intelligence (AI) has notably contributed to presenting dakwah content to the public; (5) Normative dynamics in digital-era dakwah communication reveal the challenges in maintaining a balance between popularity and the integrity of religious teachings; (6) Ethical challenges and controversies in digital dakwah underscore the need to uphold moral and ethical values in religious communication; and (7) Education and supervision emerge as pivotal in addressing the challenges of digital dakwah. Content creators, preachers, and society at large must be equipped with a proper understanding of technology usage aligned with scholarly and ethical responsibility, ensuring that the conveyance of religious messages remains accurate, substantial, and consistent with Islamic values.

The continued rapid growth of data, along with advances in Artificial Intelligence (AI) to extract knowledge from such data, is reshaping the computing ecosystem landscape. With AI becoming an essential part of almost every end-user application, our current computing platforms are facing several challenges. The data-intensive nature of current AI models requires minimizing data movement. Furthermore, interactive intelligent datacenter-scale services require scalable and real-time solutions to provide a compelling user experience. Finally, algorithmic innovations in AI demand a flexible and programmable computing platform that can keep up with this rapidly changing field. We believe that these trends and their accompanying challenges present tremendous opportunities for FPGAs. FPGAs are a natural substrate to provide a programmable, near-data, real-time, and scalable platform for AI analytics. FPGAs are already embedded in several places where data flows throughout the computing ecosystem (e.g., smart network/storage, near image/audio sensors). Intel FPGAs are System-in-Package (SiP), scalable with 2.5D chiplets. They are also scalable at datacenter-scale as reconfigurable cloud, enabling real-time AI services. Using overlays, FPGAs can be programmed through software without needing long-running RTL synthesis. With further innovations, and leveraging their existing strengths, FPGAs can leap forward to realize their true potentials in AI analytics. In this talk, we first discuss the current trends in AI and big data. We then present trends in FPGA and opportunities for FPGAs in the era of AI and big data. Finally, we highlight selected research efforts to seize some of these opportunities: (1) 2.5D SiP integration of FPGA and AI chiplets to improve the performance and efficiency of AI workloads, and (2) AI overlay for FPGA to facilitate software-level programmability and compilation-speed.

Purpose Generative Artificial Intelligence (GenAI) is rapidly transforming business strategy, innovation processes and governance practices. While prior research has focused primarily on technological implementation and performance outcomes, limited attention has been paid to how senior executives interpret, frame and adapt to GenAI as a strategic and ethical phenomenon. This study aims to explore executives’ sensemaking and adaptive responses to GenAI and to develop a conceptual model that captures this process. Design/methodology/approach The study adopts a qualitative exploratory design based on semi-structured interviews with 15 senior executives from diverse industries in Greece. Data were analyzed using thematic analysis to identify recurring patterns in executives’ perceptions, decision rationales and ethical considerations related to GenAI adoption and integration. Findings The analysis reveals four interrelated dimensions shaping executive adaptation: strategic integration and decision-making, business value and innovation, human–AI collaboration and workforce transformation and ethics, governance and adoption barriers. Cross-thematic synthesis indicates that executives perceive GenAI as a decision-support and innovation amplifier rather than an autonomous decision-maker. These findings inform an Emergent Integrative Model of Executive Adaptation to GenAI, conceptualized as a dynamic cycle comprising strategic sensemaking (Curate), operational experimentation (Create) and ethical consolidation (Consolidate). Research limitations/implications Several executives cited technical and resource limitations as obstacles to effective GenAI implementation. Challenges include legacy systems, lack of skilled artificial intelligence (AI) engineers and limited integration between AI platforms and enterprise software. Practical implications This model advances understanding of executive cognition and adaptive intelligence in the AI era, positioning leadership as a process of continuous learning, sensemaking and ethical stewardship. Practically, the research offers a roadmap for organizations and policymakers for aligning GenAI-driven innovation with responsible governance and leadership development. Social implications By highlighting the role of executive stewardship, the study underscores how ethical leadership in GenAI adoption influences public trust, workforce well-being and organizational legitimacy. Originality/value Existing research on AI in business has predominantly focused on technological implementation, efficiency gains and economic outcomes. Studies emphasize measurable benefits such as productivity enhancement, improved decision-making speed and customer experience optimization. However, fewer studies explore how executive cognition and strategic reasoning shape the trajectory of AI adoption – particularly regarding GenAI technologies that introduce new forms of creative automation. The study advances leadership and sensemaking research by shifting the focus from GenAI adoption outcomes to executive cognition and ethical stewardship. It offers a novel integrative model that explains how strategy, innovation and governance co-evolve in the GenAI era.

Purpose- In the contemporary business landscape, we are witnessing the rapid development of Artificial Intelligence (AI), which is fundamentally reshaping organizational practices. These developments mark what can be described as the "Era of AI", a significant milestone in technological history. While AI offers benefits, it also presents critical challenges, particularly concerning its adoption and the adaptation processes within organizations. Despite the swift evolution of AI technologies, research on their practical applications in organizational settings remains scarce and underdeveloped. This gap highlights a promising area for further exploration. In alignment with the literature, it can be argued that organizations with higher AI adoption rates tend to achieve better innovation outcomes, which suggests a need to revisit and potentially expand the Technology-Organization-Environment (TOE) paradigm. Originally developed to explain technological adoption/embracement, the TOE framework may not capture the complexities introduced by AI. This study aims to explore whether an expanded TOE paradigm is necessary to better address the contemporary dynamics of AI adoption. Methodology- This research investigates the historical development and consolidation of AI within organizations, using the TOE paradigm as a foundational theoretical look. The study examines whether the existing TOE model sufficiently explains AI adoption or whether it requires augmentation to remain relevant in the age of generative AI. Findings- Literature review findings indicate that the traditional TOE framework exhibits limitations when applied to AI adoption. To address these gaps, another study was found in the literature that proposes the inclusion of a human factor—transforming the TOE into a TOEH (Technology-Organization-Environment-Human) model. In our research we would like to integrate critical thinking (CT) skills under Human Factor, as organizations increasingly seek employees who can critically assess and effectively utilize outputs from generative AI (GenAI) tools. The ability to make intelligent and ethical decisions in the context of AI is now a vital competency. Conclusion- The proposed TOEH framework offers a more well-rounded approach to discovering AI adoption within organizations. By incorporating the human element, particularly critical thinking skills, organizations can better prepare to embrace AI in an ethical, effective, and innovative manner.

BackgroundOnline health information seeking is undergoing a major shift with the advent of artificial intelligence (AI)–powered technologies such as voice assistants and large language models (LLMs). While existing health information–seeking behavior models have long explained how people find and evaluate health information, less is known about how users engage with these newer tools, particularly tools that provide “one” answer rather than the resources to investigate a number of different sources.ObjectiveThis study aimed to explore how people use and perceive AI- and voice-assisted technologies when searching for health information and to evaluate whether these tools are reshaping traditional patterns of health information seeking and credibility assessment.MethodsWe conducted in-depth qualitative research with 27 participants (ages 19-80 years) using a think-aloud protocol. Participants searched for health information across 3 platforms—Google, ChatGPT, and Alexa—while verbalizing their thought processes. Prompts included both a standardized hypothetical scenario and a personally relevant health query. Sessions were transcribed and analyzed using reflexive thematic analysis to identify patterns in search behavior, perceptions of trust and utility, and differences across platforms and user demographics.ResultsParticipants integrated AI tools into their broader search routines rather than using them in isolation. ChatGPT was valued for its clarity, speed, and ability to generate keywords or summarize complex topics, even by users skeptical of its accuracy. Trust and utility did not always align; participants often used ChatGPT despite concerns about sourcing and bias. Google’s AI Overviews were met with caution—participants frequently skipped them to review traditional search results. Alexa was viewed as convenient but limited, particularly for in-depth health queries. Platform choice was influenced by the seriousness of the health issue, context of use, and prior experience. One-third of participants were multilingual, and they identified challenges with voice recognition, cultural relevance, and data provenance. Overall, users exhibited sophisticated “mix-and-match” behaviors, drawing on multiple tools depending on context, urgency, and familiarity.ConclusionsThe findings suggest the need for additional research into the ways in which search behavior in the era of AI- and voice-assisted technologies is becoming more dynamic and context-driven. While the sample size is small, participants in this study selectively engaged with AI- and voice-assisted tools based on perceived usefulness, not just trustworthiness, challenging assumptions that credibility is the primary driver of technology adoption. Findings highlight the need for digital health literacy efforts that help users evaluate both the capabilities and limitations of emerging tools. Given the rapid evolution of search technologies, longitudinal studies and real-time observation methods are essential for understanding how AI continues to reshape health information seeking.

The meteoric rise in generative AI has created both opportunities and ethical challenges for the mental health disciplines, namely in clinical mental health counseling, psychology, psychiatry, and social work. While these disciplines have been grounded in well-established ethical principles such as autonomy, beneficence, justice, fidelity, and confidentiality, the exponential ubiquity of AI in society has rendered mental health professionals unsure as to how to navigate ethical decision making in the AI era. The author proposes a preliminary ethical framework which synthesizes the code of ethics of the American Counseling Association (ACA), the American Psychological Association (APA), the American Medical Association (AMA), and the National Association of Social Workers (NASW), which is then organized around five pillars: (i) autonomy and informed consent; (ii) beneficence and non-malfeasance; (iii) confidentiality, privacy, and transparency; (iv) justice, fairness and inclusiveness; and (v) fidelity, professional integrity, and accountability. These pillars are juxtaposed with AI ethical guidelines developed by multinational organizations, governmental and non-governmental entities, and technology corporations. The resulting integrated ethical framework provides a practical cogent structure that mental health professionals can use when navigating this uncharted terrain. A case study based on the proposed ethical framework and strategies that clinical mental professionals can consider prior to incorporating AI into their clinical repertoire are offered. Limitations of the framework and its implications for future research are addressed.

Artificial intelligence (AI) has revolutionized urology, offering transformative advancements in diagnostics, treatment planning and patient care. This update highlights AI applications in diagnostic imaging, benign urological conditions, uro-oncology, urologic surgeries and patient monitoring. AI algorithms including machine learning (ML) and deep learning (DL) enhance non-oncological applications include predicting ureteral stone passage (accuracy: 85%) and optimizing benign prostatic hyperplasia (BPH) management. In uro-oncology, AI predicts biochemical recurrence post-prostatectomy (accuracy: 95%) and stratifies renal cell carcinoma aggressiveness and prostate cancer detection via MRI analysis (AUC: 0.95-0.99) and improve bladder cancer diagnosis through cystoscopic image classification (sensitivity: 89.7%). Robotic surgery benefits from AI-driven precision in procedures like robot-assisted radical prostatectomy (RARP). AI enables early detection of minimal residual disease and recurrence through analysis of urinary 'liquid biopsies', while AI-based 'computational biopsy' predicts genomic markers and clinical risk scores directly from H&E-stained slides, reducing costs and turnaround time. Ethical challenges including algorithmic bias and data privacy necessitate robust governance frameworks. Future innovations, such as artificial general intelligence (AGI), federated learning, promise personalized care and autonomously diagnoses and prescribes treatments of urologic illnesses but require interdisciplinary collaborations. This review underscores AI's potential to improve outcomes while addressing limitations in data diversity and clinical integration.