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****Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust AI.****Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we are led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer winning in games like Jeopardy and go does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules. These approaches are too narrow to achieve genuine intelligence. The world we live in is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Marcus and Davis show us what we need to first accomplish before we get there and argue that if we are wise along the way, we won't need to worry about a future of machine overlords. If we heed their advice, humanity can create an AI that we can trust in our homes, our cars, and our doctor's offices.__Reboot__provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of what we can achieve and how AI can make our lives better. Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a robust artificial intelligence that can make our lives better. “Finally, a book that tells us what AI is, what AI is not, and what AI could become if only we are ambitious and creative enough.” —Garry Kasparov, former world chess champion and author of Deep Thinking Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence. The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better. This book brings together the fields of robot spatial mapping and cognitive spatial mapping, which share some common core problems. One would reasonably expect some cross-fertilisations of research between the two areas to have occurred, and this has happened but only recently. There are signs that both fields have matured and that efforts to cross-fertilise are happening, but it is neither complete nor common yet. Robot spatial mapping, in this book, is about the problem of a robot computing a representation of its environment from data gathered by its sensors. This problem has been studied since the creation of the first autonomous mobile robot in the late nineteen-sixties. People and animals also compute a representation of their environment, which is commonly referred to as a cognitive map. Cognitive spatial mapping is about the problem of computing a cognitive map, and has been studied extensively by many researchers of disparate backgrounds. The book consists of three parts: Robot Mapping, Cognitive Mapping, and Cognitive Robot Mapping. The first part addresses a cross section of problems commonly found in robot mapping, such as uncertainty, localization, unstructured environments, and control architectures. It includes a comprehensive introduction to the famous SLAM problem. Part two presents works on cognitive mapping and discusses how the findings could benefit researchers interested in robot mapping. Spatial cognition is examined based on behaviour of humans and animals, and how spatial information is encoded in the brain. The third part presents implementations of cognitive mapping theories on mobile robots. It includes computational models of cognitive maps, such as hybrid metric-topological ones, absolute space representations, and biomimetic approaches. Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust artificial intelligence. Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence. The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better. The Human Brain, Wi Th Its Hundred Billion Or More Neurons, Is Both One Of The Most Complex Systems Known To Man And One Of The Most Important. The Last Decade Has Seen An Explosion Of Experimental Research On The Brain, But Little Theory Of Neural Networks Beyond The Study Of Electrical Properties Of Membranes And Small Neural Circuits. Nonetheless, A Number Of Workers In Japan, The United States And Elsewhere Have Begun To Contribute To A Theory Which Provides Techniques Of Mathematical Analysis And Computer Simulation To Explore Properties Of Neural Systems Containing Immense Numbers Of Neurons. Recently, It Has Been Gradually Recognized That Rather Independent Studies Of The Dynamics Of Pattern Recognition, Pattern Format::ion, Motor Control, Self-organization, Etc. , In Neural Systems Do In Fact Make Use Of Common Methods. We Find That A Competition And Cooperation Type Of Interaction Plays A Fundamental Role In Parallel Information Processing In The Brain. The Present Volume Brings Together 23 Papers Presented At A U. S. -japan Joint Seminar On Competition And Cooperation In Neural Nets Which Was Designed To Catalyze Better Integration Of Theory And Experiment In These Areas. It Was Held In Kyoto, Japan, February 15-19, 1982, Under The Joint Sponsorship Of The U. S. National Science Foundation And The Japan Society For The Promotion Of Science. Participants Included Brain Theorists, Neurophysiologists, Mathematicians, Computer Scientists, And Physicists. There Are Seven Papers From The U. S. Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust AI. Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we are led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer winning in games like Jeopardy and go does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules. These approaches are too narrow to achieve genuine intelligence. The world we live in is wildly complex and open-ended. How can we bridge this gap' What will the consequences be when we do' Marcus and Davis show us what we need to first accomplish before we get there and argue that if we are wise along the way, we won't need to worry about a future of machine overlords. If we heed their advice, humanity can create an AI that we can trust in our homes, our cars, and our doctor's offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of what we can achieve and how AI can make our lives better This book constitutes the refereed proceedings of the three confederated conferences CoopIS 2002, DOA 2002, and ODBASE 2002, held in Irvine, CA, USA, in October/November 2002. The 77 revised full papers and 10 posters presented were carefully reviewed and selected from a total of 291 submissions. The papers are organized in topical sections on interoperability, workflow, mobility, agents, peer-to-peer and ubiquitous, work process, business and transaction, infrastructure, query processing, quality issues, agents and middleware, cooperative systems, ORB enhancements, Web services, distributed object scalability and heterogeneity, dependability and security, reflection and reconfiguration, real-time scheduling, component-based applications, ontology languages, conceptual modeling, ontology management, ontology development and engineering, XML and data integration, and tools for the intelligent Web. Two Leaders In The [artificial Intelligence] Field Offer A Compelling Analysis Of The Current State Of The Art And Reveal The Steps We Must Take To Achieve A Truly Robust Artificial Intelligence. -- From Book Jacket. Mind The Gap -- What's At Stake -- Deep Learning, And Beyond -- If Computers Are So Smart, How Come They Can't Read? -- Where's Rosie? -- Insights From The Human Mind -- Common Sense, And The Path To Deep Understanding -- Trust. Gary Marcus And Ernest Davis. Includes Bibliographical References And Index. This Important Work Is An Attempt To Synthesize Two Areas That Need To Be Treated In Tandem. The Book Brings Together The Fields Of Robot Spatial Mapping And Cognitive Spatial Mapping, Which Share Some Common Core Problems. One Would Expect Some Cross-fertilization Of Research Between The Two Areas To Have Occurred, Yet This Has Begun Only Recently. There Are Now Signs That Some Synthesis Is Happening, So This Work Is A Timely One For Students And Engineers In Robotics.