Dynamical systems foundations for neuromorphic intelligence

During the summer of 2008 in Washington, DC, the Cognitive Science Society celebratedthe 30th anniversary of its seminal 1979 conference in San Diego. The 2008 conferenceorganizers—Bradley Love, Ken McRae, and Vladimir Sloutsky—commissioned a sympo-sium to celebrate the occasion. In discussing possibilities, we agreed that the symposiumshould not simply address the Society’s origins and subsequent history, but that it shouldfocus on contributions from the disciplines and theoretical perspectives central to CognitiveScience, along with their future directions.We originally settled on five disciplines and five theoretical perspectives, and then weinvited 10 active established researchers to address them at the conference. To accommo-date these 10 speakers, two symposia were presented, one on disciplines and one onperspectives. Each speaker was asked to address: (a) What was your discipline⁄perspectivelike at the time of the 1979 conference? (b) How has the discipline⁄perspective changedover the past 30 years to what it is today? (c) How do you foresee the discipline⁄perspectivechanging in the next 30 years?Because of time constraints, we could not include all disciplines and perspectives centralto Cognitive Science. Fortunately, however, we were able to remedy this limitation byasking additional researchers to contribute articles here. The resulting collection of articlescovers disciplines and perspectives that have been central to Cognitive Science for the past30 years and that are likely to be central for the next 30 years and beyond. Specifically, thedisciplines (and the authors addressing them) include the following:Psychology (Dedre Gentner)Artificial Intelligence (Kenneth D. Forbus)Philosophy (William Bechtel)Linguistics (Elissa L. Newport)

This paper addresses the formation of discrete representations from continuous processes by intelligent systems of varying natures. It explores a range of philosophical approaches—such as monism, sensationalism, dualism, physicalism, behaviorism, cybernetics, and semiotics—that present different interpretations of how intelligence and consciousness transform continuous phenomena into discrete forms. The limitations of existing theories in articulating the mechanisms underlying this transformation are critically examined. By incorporating the concept of discretization, which is traditionally associated with digital signal processing, into interdisciplinary research on artificial intelligence (AI) and cognitive sciences, the paper provides a novel perspective on the functions of both natural and artificial intelligence. Discretization emerges as a pivotal function of AI, enabling the conversion of continuous processes into discrete representations. An analogy is drawn between the discretization occurring in human consciousness and the mechanisms employed in artificial intelligence, underscoring the significance of forming discrete representations in the realms of perception and intellectual information processing. A discussion of the Heisenberg uncertainty principle within the context of discretization reveals how this process can lead to information loss and uncertainty within artificial intelligence systems. The concept of discretization has the potential to immensely enrich our understanding of information processing in human consciousness, elucidating how unique sensations and qualities of the perceived world are constructed, and how these processes relate to notions of self-organization and strange loops, as proposed by Douglas Hofstadter. Furthermore, the exploration of qualia contributes to the argument that qualia—which represent a collection of conscious, subjective experiences—must inherently include a discretization function as a fundamental aspect of organizing subjective perceptions. If this proposition holds true, then the philosophical zombie presented in Chalmers's thought experiment becomes conceptually untenable, rendering his criticisms of physicalism and materialism less convincing. This research offers new avenues for exploring the interaction between continuous and discrete phenomena, thereby proposing directions for future investigations in the philosophy of consciousness and the cognitive sciences.

Brain-inspired cognitive systems (BCSs) are an emerging field of cybernetics, cognitive science, and system science. BCSs study not only the intelligence science foundations of artificial intelligence (AI) and cognitive systems, but also formal models of the brain embodied by computational intelligence. This article presents the brain and intelligence science foundations of BCS toward hybrid intelligent systems and the symbiotic intelligence of humanity. It explores the transdisciplinary theoretical foundations of system, brain, intelligence, knowledge, cybernetic, and cognitive sciences toward the next generation of knowledge processors beyond classic data processors for autonomous computing systems. A BCS provides an overarching platform for cognitive cybernetics, humanity, and systems to enable emerging hybrid societies shared by humans and intelligent machines.

This study aims to analyze the perception of Islamic Religious Education students at the State Islamic University of North Sumatra towards the use of Artificial Intelligence as a learning resource. This study uses a qualitative method with a phenomenological type. Data collection techniques used are questionnaires, observations, interviews, and documentation. Furthermore, the data was analyzed using the Miles and Huberman model combined with ATLAS.ti software. The results of this study indicate that; 1) The information provided by Artificial Intelligence is not completely accurate because there are deficiencies in the depth of explanation and the references listed are not completely valid; 2) AI provides ease of access and use in the learning process; 3) The use of AI is considered efficient in terms of time and cost; and 4) Student ethics in using Artificial Intelligence as a learning resource is not yet entirely good, although it shows a positive direction and growing ethical awareness. This study suggests that in the future, Artificial Intelligence developers need to connect Artificial Intelligence systems with scientific repositories, and that their use must be accompanied by strengthening digital literacy and critical attitudes from users.

Artificial intelligence is defined as the totality of systems and programs that imitate human intelligence and can eventually surpass this intelligence over time. The rapid development of these technologies has raised various ethical debates such as moral responsibility, privacy, bias, respect for human rights, and social impacts. This study examines the technical infrastructure of artificial intelligence, the differences between weak and strong artificial intelligence, ethical issues, and theological dimensions in detail, providing a comprehensive perspective on the role of artificial intelligence in human life and the problems it brings. The historical development of artificial intelligence has been shaped by the contributions of various disciplines such as mathematical logic, cognitive science, philosophy, and engineering. From the ancient Greek philosophers to the present day, thoughts on artificial intelligence have raised deep philosophical questions such as human nature, consciousness, and responsibility. The algorithms developed by Alan Turing have contributed to the modern shaping of artificial intelligence and have put forward the first models to assess whether machines have human-like intelligence, such as the “Turing Test”. The study first analyzes the technical infrastructure of artificial intelligence in detail and discusses the current limits and potential of the technology through the distinction between weak and strong artificial intelligence. Weak artificial intelligence includes systems designed to perform specific tasks and do not exhibit general intelligence outside of those tasks, while strong artificial intelligence refers to systems with human-like general intelligence and flexible thinking capacity. Most of the widely used artificial intelligence applications today fall into the category of weak artificial intelligence. However, the development of strong artificial intelligence brings various ethical and theological consequences for humanity. The ethical issues of artificial intelligence include fundamental topics such as autonomy, responsibility, transparency, fairness, and privacy. The decision-making processes of autonomous systems raise serious ethical questions at the societal level. Especially autonomous weapons and artificial intelligence-managed justice systems raise concerns in terms of human rights and individual freedoms. In this context, the ethical framework of artificial intelligence has deep impacts on the future of humanity and human-machine interaction, not just limited to technological boundaries. From a theological perspective, the ability of artificial intelligence to imitate the human mind and creative processes raises deep theological issues such as the creativity of God, the place of human beings in the universe, and consciousness. The questions of whether artificial intelligence systems can gain consciousness and whether these conscious systems can have a spiritual status have led to new debates in theology and philosophy. The ethical principles of artificial intelligence are shaped around principles such as transparency, accountability, autonomy, human control, and data management. In conclusion, determining the ethical and theological principles that need to be considered in the development and application of artificial intelligence is critical for the future of humanity. A comprehensive examination of the ethical and theological dimensions of artificial intelligence technologies is necessary to understand and manage the social impacts of this technology. This study emphasizes the necessity of an interdisciplinary approach for the development of artificial intelligence in harmony with social values and for the benefit of humanity. The study provides an important theoretical framework for future research by shedding light on the complex ethical and theological issues arising from the development and widespread use of artificial intelligence.

Differential calculus is a vital subject in many STEM programs, yet students often find abstract concepts like limits, derivatives, and the connection between rates of change challenging. The recent growth of artificial intelligence (AI) and intelligent tutoring systems (ITS) provides opportunities to customize calculus teaching, offer immediate feedback, and support learners outside traditional classrooms. This review compiles research from 2019 to 2025 on the use of AI—especially ITS—in college-level differential calculus. It explores technological bases, effectiveness, limitations, ethical issues, and future directions. Studies from North America, Asia, Europe, and Latin America are included to give a global view. While evidence suggests AI tutors can boost engagement and help students grasp concepts better, issues remain regarding accuracy, fairness, teacher involvement, and data privacy. Suggestions are provided for thoughtfully incorporating AI into differential calculus education.

We are glad to introduce you that the 2021 3rd International Conference on Artificial Intelligence Technologies and Applications (ICAITA 2021) was successfully held on September 10-12, 2021. In light of worldwide travel restriction and the impact of COVID-19, ICAITA 2021 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out ICAITA 2021 via online platform according to the original schedule instead of postponing it.ICAITA 2021 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence Technologies and Applications to a common forum. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence Technologies and Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence Technologies and Applications and related areas.This scientific event brings together more than 100 national and international researchers in artificial intelligence technologies and applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.We were pleased to invite three distinguished experts to present their insightful speeches. Our first keynote speaker, Prof. Yau Kok Lim, from Sunway University, Malaysia. His research interests include Applied artificial intelligence, 5G networks, Cognitiveradio networks, Routing and clustering, Trust and reputation, Intelligent transportation system. And then we had Prof. Peter Sincak, from Technical University of Kosice, Slovakia. His research includes Artificial Intelligence and Intelligent Systems. Lastly, we were glad to invite Chinthaka Premachandra, from Shibaura Institute of Technology, Sri Lanka. His research interests include Artificial Intelligence, image processing and robotics. In the last part of the conference, all participants were invited to join in a WeChat group to discuss and explore the academic issues after the presentations. The online discussion was lasted for about 30-60 minutes. The first two parts were conducted via online collaboration tool, Zoom, while the online discussion was carried out through instant communication tool, WeChat. The online platform enabled all participants to join this grand academic event from their own home.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Artificial Intelligence Applications & Technologies, Computing and the Mind, Foundations of Artificial Intelligence and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Committee of ICAITA 2021List of titles Committee member, General Conference Chair, Technical Program Committee Chair, Academic Committee Chair, Technical Program Committee Member, Academic Committee Member are available in this Pdf.

On the Origin of Objects is the culmination of Brian Cantwell Smith's decade-long investigation into the philosophical and metaphysical foundations of computation, artificial intelligence, and cognitive science. Based on a sustained critique of the formal tradition that underlies the reigning views, he presents an argument for an embedded, participatory, "irreductionist," metaphysical alternative. Smith seeks nothing less than to revise our understanding not only of the machines we build but also of the world with which they interact. On the Origin of Objects is the culmination of Brian Cantwell Smith's decade-long investigation into the philosophical and metaphysical foundations of computation, artificial intelligence, and cognitive science. Based on a sustained critique of the formal tradition that underlies the reigning views, he presents an argument for an embedded, participatory, "irreductionist," metaphysical alternative. Smith seeks nothing less than to revise our understanding not only of the machines we build but also of the world with which they interact. Smith's ambitious project begins as a search for a comprehensive theory of computation, able to do empirical justice to practice and conceptual justice to the computational theory of mind. A rigorous commitment to these two criteria ultimately leads him to recommend a radical overhaul of our traditional conception of metaphysics. Everything that exists—objects, properties, life, practice—lies Smith claims in the "middle distance," an intermediate realm of partial engagement with and partial separation from, the enveloping world. Patterns of separation and engagement are taken to underlie a single notion unifying representation and ontology: that of subjects' "registration" of the world around them. Along the way, Smith offers many fascinating ideas: the distinction between particularity and individuality, the methodological notion of an "inscription error," an argument that there are no individuals within physics, various deconstructions of the type-instance distinction, an analysis of formality as overly disconnected ("discreteness run amok"), a conception of the boundaries of objects as properties of unruly interactions between objects and subjects, an argument for the theoretical centrality of reference preservation, and a theatrical, acrobatic metaphor for the contortions involved in the preservation of reference and resultant stabilization of objects. Sidebars and diagrams throughout the book help clarify and guide Smith's highly original and compelling argument. Bradford Books imprint

In the talk I will discuss how research on humanoid robots, cognition and brain sciences can be seen as parts of a multidisciplinary, coordinated effort aimed at advancing knowledge on the foundation of human intelligence and at developing new, human-centered technologies. The rationale stems from the observation that developing human-like intelligence in artificial systems with human-like morphology (humanoids) requires to address the same questions cognitive neuroscientists are asking through experimental investigations. Conversely understanding human intelligence from all its multifaceted perspective can take advantage of the realistic simulation allowed by the physical implementation of hardware models. Within this framework I will present results of projects ongoing at the Department of Robotics, Brain and Cognitive Sciences of IIT in the areas of humanoid cognition, robotic rehabilitation and motor learning, multimodal sensory integration and brain machine interface.

It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement. To learn autonomously and in a cumulative fashion is one of the hallmarks of intelligence, and we know that higher mammals engage in exploratory activities that are not directed to pursue goals of immediate relevance for survival and reproduction but are instead driven by intrinsic motivations such as curiosity, interest in novel stimuli or surprising events, and interest in learning new behaviours. The adaptive value of such intrinsically motivated activities lies in the fact that they allow the cumulative acquisition of knowledge and skills that can be used later to accomplish tness-enhancing goals. Intrinsic motivations continue during adulthood, and in humans they underlie lifelong learning, artistic creativity, and scientific discovery, while they are also the basis for processes that strongly affect human well-being, such as the sense of competence, self-determination, and self-esteem. This book has two aims: to present the state of the art in research on intrinsically motivated learning, and to identify the related scientific and technological open challenges and most promising research directions. The book introduces the concept of intrinsic motivation in artificial systems, reviews the relevant literature, offers insights from the neural and behavioural sciences, and presents novel tools for research. The book is organized into six parts: the chapters in Part I give general overviews on the concept of intrinsic motivations, their function, and possible mechanisms for implementing them; Parts II, III, and IV focus on three classes of intrinsic motivation mechanisms, those based on predictors, on novelty, and on competence; Part V discusses mechanisms that are complementary to intrinsic motivations; and Part VI introduces tools and experimental frameworks for investigating intrinsic motivations.The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots. The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots.

Encouragement of European labor mobility is one of the key challenges in the 21st century. The article “E-learning in ICT and Agriculture” has as main objective the description of the results obtained in the LaProf project and the processes that lead to their development. LaProf (www.laprof.eu) project has responded to this challenge by developing computer-mediated multilingual language learning exercises for specific purposes and the overall concept of migration process. LaProf was a multiliteral project that aimed at promoting language awareness to immigrating workforces in two particular sectors, ICT and agriculture. The main goal was to provide free access to language learning resources that would help candidate immigrants get more familiarized with the terminology and cultural issues in their sectors, through developing and disseminating a number of language learning exercises. The main idea of the project was to encourage ICT teachers living in Estonia (and Baltics in general) to learn Finnish and give them assistance in an overall immigration process to Finland by increasing their knowledge about working environment and culture of the target country. Accordingly, LaProf aimed to teach Greek and cultural issues to agricultural specialists living in Romania, who want to move and work in Greece. Significant attention was given to encouraging the learning of under-representing European languages (Finnish and Greek) as foreign languages in order to help European citizens from Estonia and Romania to understand better the working environment and culture of the targeted countries (i.e. Finland and Greece). This objective is in accordance with one of the European Label national priorities: foreign languages as preparation for the work market, language skills increasing the possibility of obtaining a better job, at national and even international level. In addition, the instructions of LaProf language learning exercises are translated into widely spoken EU languages (English and French) as well as into Hungarian, Romanian, Estonian and Russian, which are notably less widely used and taught languages in Europe. To reinforce the acquisition of language and cultural competencies by its targeted user groups, as well as to raise awareness for the targeted languages, LaProf developed and promoted language learning methodologies and resources that motivate the particular categories of language learners, in order to enhance their capacity for language learning. As the main output 656 interactive language learning exercises were developed for its clearly defined user groups. A series of piloting tests were applied to a specified target group, the final outputs being thus optimized to the maximum. The targeted learning resources are focused on language learning of the targeted languages, but also reflect the embedded cultural context of the destination countries and sectors. The following key results were achieved: • A language learning framework outlining the background, topics, working culture, and relevant terminology of the targeted sectors and destination countries; • A variety of multilingual language learning exercises (translated and adapted in English, French, Romanian, Hungarian, Estonian, and Russian) are publicly available and accessible online; • Additional learning resources such as Learner’s Guide, Teacher’s Guide, Manual of Tools, WebQuest containing the background knowledge that learners should have before taking the language learning exercises, culture-aware resources that will facilitate their preparation for immigration in the destination countries, as well as pedagogical and technical guidelines for the language teachers; • Two online platforms: (1) the LaProf Web portal and (2) the LaProf Wiki page through which interested users are able to easily search, identify, retrieve and use language learning exercises in a digital format. These platforms contain also an online tool through which all producers of digital resources on language learning for the targeted communities are able to upload their resources, describe them with appropriate metadata in English and in their languages, and to make them publicly available via the LaProf Web portal for all interested users to find.

Will the “representational paradigm” - that characterised Artificial Intelligence (AI) and Cognitive Science (CS) from their very birth - be eliminated in the 21st century? Will this paradigm be replaced by the new one based on dynamic systems, connectionism, situatedness, embodiedness, etc.? Will this be the end of the AI ambitious project? I do not think so. Challenges and attacks to AI and CS have been hard and radical in the last 15 years, however I believe that the next century will start with a renewed rush of AI and we will not assist to a paradigmatic revolution, with connectionism replacing cognitivism and symbolic models; emergentist, dynamic and evolutionary models eliminating reasoning on explicit representations and planning; neuroscience (plus phenomenology) eliminating cognitive processing; situatedness, reactivity, cultural constructivism eliminating general concepts, context independent abstractions, ideal-typical models. I claim that the major scientific challenge of the first part of the century will precisely be the construction of a new “synthetic” paradigm: a paradigm that puts together, in a principled and non-eclectic way, cognition and emergence, information processing and self-organisation, reactivity and intentionality, situatedness and planning, etc. [Cas98a].

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 &

Scientific machine learning is a burgeoning discipline which blends scientific computing and machine learning.Traditionally, scientific computing focuses on large-scale mechanistic models, usually differential equations, that are derived from scientific laws that simplified and explained phenomena.On the other hand, machine learning focuses on developing non-mechanistic data-driven models which require minimal knowledge and prior assumptions.The two sides have their pros and cons: differential equation models are great at extrapolating, the terms are explainable, and they can be fit with small data and few parameters.Machine learning models on the other hand require "big data" and lots of parameters but are not biased by the scientists ability to correctly identify valid laws and assumptions.However, the recent trend has been to merge the two disciplines, allowing explainable models that are data-driven, require less data than traditional machine learning, and utilize the knowledge encapsulated in centuries of scientific literature.The promise is to fuse a priori domain knowledge which doesn't fit into a "dataset", allow this knowledge to specify a general structure that prevents overfitting, reduces the number of parameters, and promotes extrapolatability, while still utilizing machine learning techniques to learn specific unknown terms in the model.This has started to be used for outcomes like automated hypothesis generation and accelerated scientific simulation.The purpose of this blog post is to introduce the reader to the tools of scientific machine learning, identify how they come together, and showcase the existing open source tools which can help one get started.We will be focusing on differentiable programming frameworks in the major languages for scientific machine learning: C++, Fortran, Julia, MATLAB, Python, and R.We will be comparing two important aspects: efficiency and composability.Efficiency will be taken in the context of scientific machine learning: by now most tools are well-optimized for the giant neural networks found in traditional machine learning, but, as will be discussed here, that does not necessarily make them efficient when deployed inside of differential equation solvers or when mixed with probabilistic programming tools.Additionally, composability is a key aspect of scientific machine learning since our toolkit is not ML in isolation.Our goal is not to do machine learning as seen in a machine learning conference (classification, NLP, etc.), and it's not to do traditional machine learning as applied to scientific data.Instead, we are putting ML models and techniques into the heart of scientific simulation tools to accelerate and enhance them.Our neural networks need to fully integrate with tools that simulate satellites and robotics simulators.They need to integrate with the packages that we use in our scientific work for verifying numerical accuracy, tracking units, estimating uncertainty, and much more.We need our neural networks to play nicely with existing packages for delay

Focusing on the global trend of artificial intelligence (AI) in language learning, this survey-based study explored the practices and perceptions of Japanese English as a foreign language students (EFL) toward ChatGPT for second language (L2) learning. A mixed-method research design was utilized to achieve the study’s aims, with data being collected from three universities in Japan. The technology acceptance model-based survey was administered in the fall of 2023 and a total of 521 EFL students fully completed it. Quantitative analysis related to the students’ practices revealed that less than 25% of the respondents had used ChatGPT in their English studies, with formal language learning being more common than informal L2 learning outside of English coursework. Summarizing information written in the English language and translation were the top reported uses of ChatGPT for L2 English learning. According to the Likert scale responses, the L2 students’ perceived usefulness, perceived ease of use, and behavioral intention to use ChatGPT for English learning were positive. Content analysis of the qualitative data indicated contrasting findings, namely, while the students believed the AI chatbot could enhance their L2 learning, they were also concerned that it could hinder their language learning if overly relied upon. These results indicate that although a growing number of L2 learners are using ChatGPT and perceive it to be a useful resource for language learning, they are also aware of the drawbacks it poses to the language learning process.