On the Origin of Objects

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 presentation concerns some idea of what could be done, in the author's view, to help make Wang's cognitive informatics a powerful and viable source of tools and techniques for solving various real life problems. First, we give a brief account of cognitive informatics meant as a multidisciplinary field within informatics, or computer science, that is based on results of cognitive and information sciences, and which deals with human information processing mechanisms and processes and their decision theoretic, engineering, etc. applications in broadly perceived computing. We focus on its purpose, i.e. to develop and implement technologies to facilitate and extend the information acquisition, comprehension and processing capacity of humans. Emphasis is on underlying processes in the brain. However, we advocate an extended approach in which though the very cognitive informatics is the foundation, as those processes in the brain are crucial, some sort of an “outer” cognitive informatics is needed which explicitly makes reference not what proceeds “internally” in the brain, because we do not “see” this, but “externally”, i.e. what people can see, judge, evaluate, etc., and what is clearly a result of cognitive information specific processes in the brain. This line of reasoning is in line with the very essence of comprehension, memorizing, learning, choice and decision making, satisfaction with partial truth, allowing for not perfect solutions, etc. dealt with using tools and techniques derived from many areas like psychology, behavioral science, neuroscience, artificial intelligence, linguistics, neuroeconomics etc. In our case, we will concentrate on some cognitive informatics type elements that mostly have been inspired by psychology and behavioral sciences, as our problem is inherently related to human judgments and perceptions, but we will mentioned some inspirations from neuroscience, notably along the lines of neuroeconomics. Cognitive informatics constitutes a foundation of its related new field, cognitive computing, which is basically a new direction in broadly perceived intelligent computing and systems that synergistically combines results from many areas, e.g., information science, computational sciences, computer science, artificial and computational intelligence, cybernetics, systems science, cognitive science, (neuro)psychology, brain science, linguistics, etc. to just mention a few. We try to show on an example of a dynamic systems modeling, more specifically scenario based regional development planning, that cognitive computing can provide new conceptual and implementation vistas. Basically, we consider a region that is characterized by 7 life quality indicators related to economic, social, environmental, etc. qualities, which evolve over some planning horizon due to some investments, mostly by some regional or governmental agencies. There are some scenarios of investment levels over the planning horizon, meant for the development of the particular life quality indexes, and some desired levels of these indexes, both objective, i.e. set by authorities, and subjective, i.e. perceived by the inhabitant groups. As a result of a particular investment scenario, the life quality indexes evolve over the planning horizon, and their temporal evolution is evaluated by the authorities and inhabitants. This evaluation has both an objective, i.e. against the “officially” set thresholds, and subjective, i.e. as perceived by various humans and their groups. Basically, we employ Kacprzyk's fuzzy dynamic programming based approach to the modeling and planning/programming of sustainable regional development, with soft constraints and goals, but we advocated a more sophisticated assessment of variability, stability, balancedness of consecutive investments. In this process we try to develop evaluation measures, and then the optimization type model using concepts that can be effectively and efficiently handled by cognitive computing, notably the inclusion of the so called decision making and behavioral biases, biases in probability and belief, social biases, memory errors, etc. Moreover, we strongly reflect the so called status quo and minimal change biases. By using many results from social sciences, psychology, behavioral economics, neuroeconomics, etc. on human judgments and human centric evaluations, we augment a traditional purely effectiveness and efficiency oriented analysis by a more sophisticated analysis of effects of variability of temporal evolution of some life quality indicators on the human perception of its goodness. The model presented, which has been employed for years as part of large mathematical modeling projects for sustainable regional development in many regions in Asia and Europe, is illustrated on an example with scenario analysis for a rural region plagued by social and economic difficulties in which subsidies should properly be distributed over time to obtain a best overall socioeconomic effect. In this talk we present the model in a different perspective, based first on the basic Wang's cognitive informatics and its Wang and Ruhe's decision making application, and then based on new, more comprehensive cognitive computing. We show that this provides a novel insight.

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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This book applies the concepts and methods of psychoanalysis to the study of artificial intelligence (AI) and human–AI interaction. It develops a new, more fruitful approach for applying psychoanalysis to AI and machine behavior. It appeals to a broad range of scholars: philosophers working on psychoanalysis, technology, AI ethics, and cognitive sciences, psychoanalysts, psychologists, and computer scientists. The book is divided into four parts. The first part (Chapter 1) analyzes the concept of "machine behavior." The second part (Chapter 2) develops a reinterpretation of some fundamental Freudian and Lacanian concepts through Bruno Latour’s actor-network theory. The third part (Chapters 3 and 4) focuses on the nature and structure of the algorithmic unconscious. The author claims that the unconscious roots of AI lie in a form of projective identification, i.e., an emotional and imaginative exchange between humans and machines. In the fourth part of the book (Chapter 5), the author advances the thesis that neuropsychoanalysis and the affective neurosciences can provide a new paradigm for research on artificial general intelligence. The Algorithmic Unconscious explores a completely new approach to AI, which can also be defined as a form of "therapy." Analyzing the projective identification processes that take place in groups of professional programmers and designers, as well as the "hidden" features of AI (errors, noise information, biases, etc.), represents an important tool to enable a healthy and positive relationship between humans and AI. Psychoanalysis is used as a critical space for reflection, innovation, and progress.

In his book “Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence”, Matt Carter presents a comprehensive exploration of the philosophical questions surrounding artificial intelligence (AI). Carter argues that the development of AI is not merely a technological challenge but fundamentally a philosophical one. He delves into key issues like the nature of mental states, the limits of introspection, the implications of memory decay, and the functionalist framework that allows for the possibility of AI. Carter contrasts functionalism with reductive materialism, highlighting how the former accommodates the concept of artificial minds. He also emphasizes the significance of computationalism, which combines functionalism with computational theory to provide a robust explanation for mental processes. The book further discusses formal systems, the role of register machines in computation, and the inherent challenges in AI research, particularly in developing natural languag processing systems. Carter’s work underscores the limitations of current computational models of cognition while remaining hopeful that advances in neuroscience may lead to stronger AI systems in the future. This book provides critical insights for researchers in AI and cognitive science, motivating further inquiry into the philosophical foundations of artificial intelligence and its practical implications.

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

The article explores the social and educational context of the rapid development of artificial intelligence (AI) and examines its growing impact on contemporary society and modern education systems. The study outlines the evolution of AI from a theoretical concept to a widely applied technological solution that supports automation, data analysis, and decision-making processes across multiple sectors. Particular attention is given to the role of AI in transforming communication practices, especially through the use of conversational agents such as chatbots, which simulate human interaction and enhance user experience in digital environments. The paper analyzes the conceptual foundations of artificial intelligence, including its interdisciplinary nature, drawing from computer science, cognitive science, psychology, and philosophy. It also presents key areas of AI application, such as machine learning, natural language processing, computer vision, and expert systems, emphasizing their relevance in both educational and professional contexts. The author highlights the increasing integration of AI tools into educational processes, where they contribute to personalized learning, accelerated knowledge acquisition, and improved access to information resources. A significant part of the study is devoted to the practical use of AI systems, particularly ChatGPT and similar platforms, in supporting research activities. These tools facilitate literature searches, data processing, and problem-solving, thereby enhancing the efficiency of academic work. At the same time, the article addresses the limitations of AI-generated content, including the risk of superficial accuracy and the necessity of critical evaluation by users. The research also considers broader social implications of AI implementation, including its influence on the labor market, where automation may lead to the transformation or replacement of certain professions. Concerns related to ethical, psychological, and sociological aspects are discussed, highlighting the need for responsible and balanced integration of AI technologies. In conclusion, the article emphasizes that while artificial intelligence offers significant opportunities for innovation and development, it also requires a thoughtful and critical approach to its use in education and society. The formation of digital and analytical competencies becomes essential for effective interaction with AI-driven systems, ensuring that technological progress contributes to sustainable and human-centered development.

Why a new approach is needed in the quest for general artificial intelligence. Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems that can replicate human thought processes. Before we know it, computers will become so intelligent that humans will be lucky to be kept as pets. And yet, although artificial intelligence has become increasingly sophisticated—with such achievements as driverless cars and humanless chess-playing—computer science has not yet created general artificial intelligence. In Algorithms Are Not Enough, Herbert Roitblat explains how artificial general intelligence may be possible and why a robopocalypse is neither imminent nor likely. Existing artificial intelligence, Roitblat shows, has been limited to solving path problems, in which the entire problem consists of navigating a path of choices—finding specific solutions to well-structured problems. Human problem-solving, on the other hand, includes problems that consist of ill-structured situations, including the design of problem-solving paths themselves. These are insight problems, and insight is an essential part of intelligence that has not been addressed by computer science. Roitblat draws on cognitive science, including psychology, philosophy, and history, to identify the essential features of intelligence needed to achieve general artificial intelligence. Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes.

More than the 70 years since its emergence in the early 1950s, artificial intelligence (AI) is performing cognitive tasks traditionally considered the unique province of humans. This progress did not occur in a vacuum. AI emerged against a rich background of technologies from computer science and ideas about intelligence and learning from philosophy, psychology, logic, game theory, and cognitive science. We sketch out the enabling technologies for AI. They include search, reasoning, neural networks, natural language processing, signal processing and computer graphics, programming and conventional software engineering, human-computer interaction, communications, and specialized hardware that provides supercomputing power. Beyond these technologies is the notion of Artificial General Intelligence that has or exceeds the capabilities of the human brain. Currently this is completely aspirational and is not expected to be possible before 2025, if ever. Artificial Intelligence is based on a variety of technologies, none of which seek to emulate human intelligence.

Artificial Intelligence programming involves representing knowledge, using paradigms to manipulate the knowledge, and having a learning process modify both the knowledge and the paradigms. One could consider this process as building a model of how one thinks, i.e. how the brain operates at the cognitive psychology level [2]. Recently, cognitive scientists have developed a model of how one thinks at the neural level. This model is called the Parallel Distributed Processing (PDP) model of cognition and is described in the definitive work of Rumelhart and McClelland [1]. The idea that we can actually model the brain as an electrical network of neurons and then develop Artificial Intelligence in terms of the model is extremely attractive. The program has had some success, especially in the area of sensory perception and motor activity, but still has some problems to overcome before it can be said to be the ideal foundation for Artificial Intelligence. Much of the power of the PDP model derives from the learning algorithms. In this paper we consider a classification of learning algorithms that helps to organize the many developing techniques seen in the literature. We also discuss how the PDP model is changing the way we teach Artificial Intelligence. This is an important aspect of the PDP model, since the model has produced a number of new problem-solving techniques for Artificial Intelligence as well as holding out the promise of a better foundation for the basic theory of this field. If the PDP model fulfills its promise we would develop Artificial Intelligence programs that are really intelligent rather than programs that only appear to be intelligent.

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)

The Mindset of Cognitive Science.

The 2003 Benjamin Franklin Medal in computer and cognitive science presented to John McCarthy (Stanford California). John McCarthy's multiple contributions to the foundations of artificial intelligence and computer science

Blending and Choosing Within One Mind: Should Judgments Be Based on Exemplars, Rules, or Both? Stefan M. Herzog (herzog@mpib-berlin.mpg.de) Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94 14195 Berlin, Germany Bettina von Helversen (bettina.vonhelversen@unibas.ch) Department of Psychology, University of Basel, Missionsstrasse 62a 4055 Basel, Switzerland Abstract Accurate judgments and decisions are crucial for success in many areas of human life. The accuracy of a judgment or decision depends largely on the cognitive process applied. In research on judgment, decision making, and categorization, two kinds of cognitive processes have often been contrasted: exemplar-based processes, which use similarity to previously encountered items to make judgments, decisions, and categorizations, and rule-based processes, which use abstracted cue knowledge. Although most cognitive models of judgment and decision processes assume that people rely on both processes, they differ in whether they assume that one process is selected or that both processes are blended into a single response. The present research takes a functional perspective and investigates what kind of interaction between the two processes leads to accurate responses. Based on cross- validated simulations in real-world domains, it shows that blending rule- and exemplar-based processes generally leads to better judgments than does choosing between them, suggesting that the default strategy should be a blend of both processes, which is abandoned only when feedback justifies it. Keywords: accuracy; multiple-cue judgments; decision making; categorization; exemplar models; rules; cognitive models; mixtures of experts; simulation. Introduction Judging quantities, making decisions, and categorizing items are crucial elements of successful human behavior. A vast and diverse literature in cognitive science and judgment and decision making has investigated how people achieve these tasks (e.g., Ashby & Maddox, 2005; Gigerenzer, Hertwig, & Pachur, 2011; Kruschke, 2008; Payne, Bettman, & Johnson, 1993). The many different models and strategies proposed can be broadly classified into two categories with reference to the cognitive processes they assume: exemplar- based processes, which use similarity to previously encountered items to make judgments, decisions, and categorizations, and rule-based processes, which use abstracted cue knowledge (Hahn & Chater, 1998). Extensive research has compared the proposed models’ ability to describe human behavior. Furthermore, the performance of judgment and decision making strategies in predicting real-world criteria has been thoroughly investigated (e.g., Gigerenzer et al., 2011; Todd, Gigerenzer, & the ABC Research Group, 2012). To our knowledge, however, research in cognitive science and judgment and decision making has not previously investigated what kind of interaction between exemplar- and rule-based processes leads to accurate judgments, decisions, and categorizations: relying on just one of the two processes or using both? If both are considered, is it better to choose between them depending on the structure of the task, for instance (Rieskamp & Otto, 2006), or to blend them into a joint response? This paper presents first answers to these questions. A functional perspective on the interaction between exemplar- and rule based processes may be useful for at least three reasons. First, examining cognitive models’ ability to predict external real-world criteria goes a step further than comparing their ability to describe human behavior in idealized laboratory tasks, by adding a further evaluation criterion. If one class of cognitive models were superior to another in terms of predictive performance, this would make them more attractive as plausible models of human behavior (Chater & Oaksford, 1999). Second, many cognitive models are inspired by or share similarities with models from research fields interested in predictive performance (such as statistics, artificial intelligence, computer science, and machine learning; see e.g., Jakel, Scholkopf, & Wichmann, 2009; Marling, Sqalli, Rissland, Munoz-Avila, & Aha, 2002), and a functional perspective provides a common ground that serves to re-connect cognitive models with such fields. Third, knowledge of how to profit from the complementary strengths of the two processes could offer prescriptions for improving human judgment, decision making, and categorization by instructing decision makers on when and how to use the two processes. Models of Judgment, Decision Making, and Categorization There are two general approaches to modeling human cognition. First, single general-purpose models have been proposed (e.g., Lee & Cummins, 2004). For instance, judgment and categorization models assume either only exemplar-based (e.g., Juslin & Persson, 2002; Kruschke, 1992) or only rule-based processes (e.g., Ashby & Gott, 1988; Brehmer, 1994). Second, toolbox approaches have been proposed. These assume that people draw on multiple,