Artificial Intelligence (AI) at Emory

This book continues a series of Springer publications devoted to the emerging field of Integrated Artificial Intelligence and Machine Learning with Visual Knowledge Discovery and Visual Analytics that combine advances in both fields. Artificial Intelligence and Machine Learning face long-standing challenges of explainability and interpretability that underpin trust.  Such attributes are fundamental to both decision-making and knowledge discovery.  Models are approximations and, at best, interpretations of reality that are transposed to algorithmic form.   A visual explanation paradigm is critically important to address such challenges, as current studies demonstrate in salience analysis in deep learning for images and texts.  Visualization means are generally effective for discovering and explaining high-dimensional patterns in all high-dimensional data, while preserving data properties and relations in visualizations is challenging.  Recent developments, such as in General Line Coordinates, open new opportunities to address such challenges. This book contains extended papers presented in 2021 and 2022 at the International Conference on Information Visualization (IV) on AI and Visual Analytics, with 18 chapters from international collaborators.  The book builds on the previous volume, published in 2022 in the Studies in Computational Intelligence. The current book focuses on the following themes: knowledge discovery with lossless visualizations, AI/ML through visual knowledge discovery with visual analytics case studies application, and visual knowledge discovery in text mining and natural language processing. The intended audience for this collection includes but is not limited to developers of emerging AI/machine learning and visualization applications, scientists, practitioners, and research students.  It has multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery, visual analytics, and text and natural language processing.  The book provides case examples for future directions in this domain.  New researchers find inspiration to join the profession of the field of AI/machine learning through a visualization lens.  

Artificial Intelligence and Machine Learning for Women's Health Issues discusses the applications, challenges, and solutions that machine learning can bring to women's health challenges. The book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning which enhance the future healthcare system. This book's primary focus is on women's health issues and machine learning's role in providing solutions to these challenges, providing novel ideas for feasible implementation. It also provides an early-stage analysis for early diagnosis of women's health issues.

This book discusses the implications of artificial intelligence (AI) on post-COVID-19 international relations. With the decline and fall of U.S. global leadership and the emergence of new powerful actors, as hastened by the global pandemic, new arms are now used in new forms of wars with new players. The balance of power swings between geostrategic interests and those linked to the global governance of virtual space and the race to technological sovereignty. Chapters focus on the challenges imposed by these changes on different parts of the international system--law, governance, diplomacy, international psychological security--and articulate new strategies and ethical policies as possible solutions. The volume is interdisciplinary and will appeal to researchers, students, and professionals across fields interested in the ethics of AI in the international system.

The hidden costs of artificial intelligence--from natural resources and labor to privacy, equality, and freedom   "This study argues that [artificial intelligence] is neither artificial nor particularly intelligent. . . . A fascinating history of the data on which machine-learning systems are trained."--New Yorker   "A valuable corrective to much of the hype surrounding AI and a useful instruction manual for the future."--John Thornhill, Financial Times   "It's a masterpiece, and I haven't been able to stop thinking about it."--Karen Hao, senior editor, MIT Tech Review   What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? Drawing on more than a decade of research, award‑winning scholar Kate Crawford reveals how AI is a technology of extraction: from the minerals drawn from the earth to the labor pulled from low-wage information workers to the data taken from every action and expression.   Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequity. Rather than taking a narrow focus on code and algorithms, Crawford offers us a material and political perspective on what it takes to make AI and how it centralizes power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world.

This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts - that in some cases have caused loss of life - and develop models that are fair, transparent, safe, secure, and robust. The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers.  What You Will Learn Build AI/ML models using Responsible AI frameworks and processes Document information on your datasets and improve data quality Measure fairness metrics in ML models Identify harms and risks per task and run safety evaluations on ML models Create transparent AI/ML models Develop Responsible AI principles and organizational guidelines Who This Book Is For AI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms

This second edition provides new and updated methods and techniques for identification of drug target, binding sites prediction, high- throughput virtual screening, lead discovery and optimization, conformational sampling, prediction of pharmacokinetic properties using computer-based methodologies. Chapters also focus on the application of the latest artificial intelligence technologies for computer aided drug discovery. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary methods, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols.   Authoritative and cutting-edge, Computational Drug Discovery and Design, Second Edition aims to effectively utilize computational methodologies in discovery and design of novel drugs.

The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.

Practical and straightforward solutions to everyday sales challenges In The Innovative Seller: Keeping Pace In An AI and Customer-Centric World, veteran sales leader and trainer Jake Dunlap delivers an expert playbook for sales that offers out-of-the-box and creative answers for the problems and questions that salespeople face every day. Fun and motivational, the book walks you through effective strategies for dealing with common challenges, like LinkedIn prospecting, sales transparency, cold calling, and others. The author has included a comprehensive tactical appendix, so you can easily identify and locate the exact solution you need when you encounter a specific problem. You'll also find: Proven, grounded, and actionable techniques you can apply immediately to improve your sales performance Instructive stories and anecdotes drawn from Dunlap's decades of sales and sales training experience Insightful discussions of how the typical sales process and model has changed over the years and how to adapt to the new realities of the discipline An engaging and eye-opening resource for early- and mid-career sales professionals, as well as business development and customer success practitioners, The Innovative Seller will also prove invaluable to managers and executives at quickly growing companies who seek to optimize their firms' sales processes and results.

Artificial intelligence models are being used to make labor and delivery safer for mothers and newborns. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. This is a critical area of study as maternal and infant health are indispensable for a healthy society. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning considers the recent advances, challenges, and best practices of artificial intelligence and machine learning in relation to pregnancy complications. Covering key topics such as pregnancy complications, wearable sensors, and healthcare technologies, this premier reference source is ideal for nurses, doctors, computer scientists, medical professionals, industry professionals, researchers, academicians, scholars, instructors, and students.

Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem: A Humanity Worth Saving is the first comprehensive book showing how trustworthy AI can revolutionize decolonized global public health. It explains how it works as an ecosystem and how it can be fixed to equitably empower us all to solve the defining crises of our era, from poverty to pandemics, climate to conflicts, debt to divisions. It is written from the first-hand perspective of the world's first triple doctorate trained physician-data scientist and ethicist who has cared for more than 10,000 patients and authored 5 AI textbooks and more than 400 scientific and ethics papers. This essential resource integrates science, political economics, and ethics to unite our unique cultures, belief systems, institutions, and governments. In doing so, it is meant to give humanity a fighting chance against shared existential threats through cooperation and managed strategic competition for integral sustainable development. Taking seriously diverse voices, perspectives, and insights from the Global North and the Global South, this book uses concrete examples backed up by clear explanations to elucidate the current failures, emerging successes, and societal trends of global public health. It shows how a small number of powerful governments and corporations--amid digitalization, deglobalization, and demographic shifts--dominate global health, and how we can re-engineer a better future for it both societally and technologically. The book spans health breakthroughs in federated data architectures, machine learning, deep learning, swarm learning, quantum computing, blockchain, agile data governance and solidarity, value blocks (of democracies and autocracies), adaptive value supply chains, social networks, pandemics, health financing, universal health coverage, public-private partnerships, healthcare system design, precision agriculture, clean energy, human security, and multicultural global ethics. This book therefore is meant to provide a clear, coherent, and actionable guide equipping students, practitioners, researchers, policymakers, and leaders in digital technology, public health, healthcare, health policy, public policy, political economics, and ethics to generate the solutions that will define humanity's next era--while recovering what that humanity means, and why it is worth saving.

Futurists speculate that we are heading towards a 'singularity,' where AI will outsmart human beings, and humanity will coalesce into a single, ever-expanding mind for which data is everything. The idea mirrors conceptions of God as everything, singular, and all-knowing. But is this idea of the singularity, or God, good for humanity? Oneness has its attractions. But what space does it leave for individuality and difference? In this book, British-Jewish theologian, Harris Bor, explores these questions by applying approaches to oneness and difference found in the thought of philosophers, Benedict Spinoza (1632-1677) and Martin Heidegger (1889-1976), to the challenges of religious belief and practice in the era of AI. What emerges is a dynamic religion of the everyday capable of balancing all aspects of being, while holding tight to a God who is both singular and wholly other, and which urges us, above all, to stay human.

Explores the transformative impact of artificial intelligence (AI) on the healthcare industry AI Doctor: The Rise of Artificial Intelligence in Healthcare provides a timely and authoritative overview of the current impact and future potential of AI technology in healthcare.

Understand the real power of AI and and its ability to shape the future for the better. AI For Social Good: Using Artificial Intelligence to Save the World bridges the gap between the current state of reality and the incredible potential of AI to change the world. From humanitarian and environmental concerns to advances in art and science, every area of life stands poised to make a quantum leap into the future. The problem? Too few of us really understand how AI works and how to integrate it into our policies and projects. In this book, Rahul Dodhia, Deputy Director of Microsoft's AI for Good Research Lab, offers a nontechnical exploration of artificial intelligence tools--how they're built, what they can and can't do, and the raw material that teaches them what they "know." Readers will also find an inventory of common challenges they might face when integrating AI into their work. You'll also read more on: The potential for AI to solve longstanding issues and improve lives Learn how you can tap into the power of AI, regardless of the size of your organization Gain an understanding of how AI works and how to communicate with AI scientists to create new solutions Understand the real risks of implementing AI and how to avoid potential pitfalls Real-life examples and stories that demonstrate how teams of AI specialists, project managers, and subject matter experts can achieve remarkable products. Written for anyone who is curious about AI, and especially useful for policymakers, project managers, and leaders who work alongside AI, AI For Social Good provides discussions of how AI scientists create artificially intelligent systems, and how AI can be used ethically (or unethically) to transform society. You'll also find a discussion of how governments can become more flexible, helping regulations keep up with the fast pace of change in technology.

The overriding question discussed in this Element can be stated simply as: can computers create art? This Element presents an overview of the controversies raised by various answers to this question. A major difficulty is that the technology is developing rapidly, and there are still many uncertainties and knowledge gaps as to what is possible today and in the near future. But a number of controversial issues are identified and discussed. The position taken on controversial issues will depend on assumptions made about the technology, about the nature and location of consciousness, about art and creativity. Therefore, a number of hypothetical answers are outlined, related to the assumptions made.

This book explores, from a high level, the parallels between the evolution of humans and the evolution of machines. The book reviews practical questions about the future of AI but also engages in philosophical discussions about what machine intelligence could mean for the human experience. The book focuses on what is intelligence and what separates intelligent species from non-so-intelligent ones. It concludes this section with the description of true nature of human intelligence can be. We discuss how we looked at machines few hundred years back and how their definition and the expectations from them has changed over time. We will consider when and how machines became intelligent and then explore in depth he latest developments in artificial intelligence with explanation of deep learning technology and humanlike chat interface provided with products like ChatGPT. We will define both human intelligence and artificial intelligence and the distinction between the two. In the third and final section of the book, we will focus on near- and longer-term futures with widespread use of machine intelligence, making the whole ambient environment that we will live in intelligent How is this going to change human lives, and what parts of human life will be encroached with machines and their intelligence? We will explore how the job market will look with some jobs being taken by machines, and if this is overall a positive or negative change. What You Will Learn How human intelligence is connected with artificial intelligence as well as the differences How AI is going to change our lives in the coming years, decades and centuries An explanation of deep learning technology and humanlike chat interface provided with products like ChatGPT Who This Book is For Readers looking to contextualize the evolution of artificial intelligence in human history

As artificial intelligence (AI) becomes more and more woven into our everyday lives--and underpins so much of the infrastructure we rely on--the ethical, security, and privacy implications require a critical approach that draws not simply on the programming and algorithmic foundations of the technology.   Bringing together legal studies, philosophy, cybersecurity, and academic literature, Beyond the Algorithm examines these complex issues with a comprehensive, easy-to-understand analysis and overview. The book explores the ethical challenges that professionals--and, increasingly, users--are encountering as AI becomes not just a promise of the future, but a powerful tool of the present.   · An overview of the history and development of AI, from the earliest pioneers in machine learning to current applications and how it might shape the future · Introduction to AI models and implementations, as well as examples of emerging AI trends · Examination of vulnerabilities, including insight into potential real-world threats, and best practices for ensuring a safe AI deployment · Discussion of how to balance accountability, privacy, and ethics with regulatory and legislative concerns with advancing AI technology · A critical perspective on regulatory obligations, and repercussions, of AI with copyright protection, patent rights, and other intellectual property dilemmas · An academic resource and guide for the evolving technical and intellectual challenges of AI · Leading figures in the field bring to life the ethical issues associated with AI through in-depth analysis and case studies in this comprehensive examination.

A Biologist's Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist's perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms.

AI appears to disrupt key private law doctrines, and threatens to undermine some of the principal rights protected by private law. The social changes prompted by AI may also generate significant new challenges for private law. It is thus likely that AI will lead to new developments in private law. This Cambridge Handbook is the first dedicated treatment of the interface between AI and private law, and the challenges that AI poses for private law. This Handbook brings together a global team of private law experts and computer scientists to deal with this problem, and to examine the interface between private law and AI, which includes issues such as whether existing private law can address the challenges of AI and whether and how private law needs to be reformed to reduce the risks of AI while retaining its benefits.

DIFFUSIONS IN ARCHITECTURE A guide to diffusion models and their impact on design, with insight on how this novel artificial intelligence technology may disrupt the industry Diffusions in Architecture: Artificial Intelligence and Image Generators delves into the impact of Generative AI models and their effect on architecture design and aesthetics. The book presents an in-depth analysis of how these new technologies are revolutionizing the field of architecture and changing the way architects approach their work. The architects presented in the book focus on the application of specific AI techniques and tools used in generative design, such as Diffusion models, Dall-E2, Stable Diffusion, and MidJourney. It discusses how these techniques can generate synthetic images that are both realistic and imaginative, creating new possibilities for architectural design and aesthetics. Twenty-two leading designers and theorists offer their insights, providing disciplinary depth by covering the full impact of these learning tools on architecture.

Digital Directions: Artificial Intelligence Pathways for Higher Education describes how Artificial Intelligence (AI) is currently being used in six different institutions of higher education including both community colleges and universities. The chapters include the impacts of Artificial Intelligence on education, technical programs, student affairs, agriculture, health care and religion. The book provides the reader an opportunity to understand just how Artificial Intelligence will become part of higher education over a twenty-eight-year time period from 2022. This book attempts to convince those in higher education that disruption and phenomenal change is heading directly for them.

An informed general readership, including students and interested non-specialists. Academics from business, sociology, politics, management, science and technology studies and emerging fields such as critical algorithm studies will find it a stimulating and entertaining read. Reflective practitioners who are beginning to question the potential social impacts of their work (see current explosion of tech worker activism).

This book claims that artificial intelligence (AI) may affect our freedom at work, in our daily life, and in the political sphere. The author provides a philosophical framework to help make sense of and govern the ethical and political impact of AI in these domains. AI presents great opportunities and risks, raising the question of how to reap its potential benefits without endangering basic human and societal values. The author identifies three major risks for human freedom. First, AI offers employers new forms of control of the workforce, opening the door to new forms of domination and exploitation. Second, it may reduce our capacity to remain in control of and responsible for our decisions and actions, thereby affecting our free will and moral responsibility. Third, it may increase the power of governments and tech companies to steer the political debate, thereby affecting the possibility of a free and inclusive political participation. The author claims that it is still possible to promote human freedom in our interactions with AI. This requires designing AI systems that help promote workers' freedom, strengthen human control and responsibility, and foster a free, active, and inclusive democratic participation. Human Freedom in the Age of AI will be of interest to scholars and graduate students working on the ethics of technology, philosophy of technology, political philosophy, design, and artificial intelligence.

Clear away the fog surrounding AI in education--and regain your peace of mind Among teachers, there is a cloud of rumors, confusion, and fear surrounding the rise of artificial intelligence. AI and the Future of Education is a timely response to this general state of panic, showing you that AI is a tool to leverage, not a threat to teaching and learning. By understanding what AI is, what it does, and how it can be used to enhance education, you can let go of anxiety and uncertainty, and learn to embrace artificial intelligence. It's true that, along with tremendous opportunities, AI presents some challenges for the field of education. In this book, Priten Shah, a Harvard M.Ed. with a robust background in educational innovation, helps you face these challenges head on, so you can gain the knowledge and skills you need to use AI effectively in your classroom. Thanks to this thorough consideration of ethical considerations and practical approaches, you can develop your own strategy for leveraging AI in administrative tasks, lesson design, professional development, and beyond. Understand what AI and machine learning are, and learn about new developments like ChatGPT Discover strategies for engaging students more fully using AI Automate administrative tasks, grading and feedback, and assessments Use AI in innovative ways to promote higher-order thinking skills Examine ethical considerations of AI, including the achievement gap, privacy concerns, and bias For K-12 educators, as well as leaders and policymakers who want to understand the role of technology in education, AI and the Future of Education is a valuable resource that can change AI from an unknown entity to an indispensable tool.

The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.

Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.

This volume provides an interdisciplinary collection of essays from leaders in various fields addressing the current and future challenges arising from the implementation of AI in brain and mental health. Artificial Intelligence (AI) has the potential to transform health care and improve biomedical research. While the potential of AI in brain and mental health is tremendous, its ethical, regulatory and social impacts have not been assessed in a comprehensive and systemic way. The volume is structured according to three main sections, each of them focusing on different types of AI technologies. Part 1, Big Data and Automated Learning: Scientific and Ethical Considerations, specifically addresses issues arising from the use of AI software, especially machine learning, in the clinical context or for therapeutic applications. Part 2, AI for Digital Mental Health and Assistive Robotics: Philosophical and Regulatory Challenges, examines philosophical, ethical and regulatory issues arising from the use of an array of technologies beyond the clinical context. In the final section of the volume, Part 3 entitled AI in Neuroscience and Neurotechnology: Ethical, Social and Policy Issues, contributions examine some of the implications of AI in neuroscience and neurotechnology and the regulatory gaps or ambiguities that could potentially hamper the responsible development and implementation of AI solutions in brain and mental health. In light of its comprehensiveness and multi-disciplinary character, this book marks an important milestone in the public understanding of the ethics of AI in brain and mental health and provides a useful resource for any future investigation in this crucial and rapidly evolving area of AI application.The book is of interest to a wide audience in neuroethics, robotics, computer science, neuroscience, psychiatry and mental health.

Big Data, Big Design provides designers with the tools they need to harness the potential of machine learning and put it to use for good through thoughtful, human-centered, intentional design. Enter the world of Machine Learning (ML) and Artificial Intelligence (AI) through a design lens in this thoughtful handbook of practical skills, technical knowledge, interviews, essays, and theory, written specifically for designers. Gain an understanding of the design opportunities and design biases that arise when using predictive algorithms. Learn how to place design principles and cultural context at the heart of AI and ML through real-life case studies and examples. This portable, accessible guide will give beginners and more advanced AI and ML users the confidence to make reasoned, thoughtful decisions when implementing ML design solutions.

Twenty-first century life is increasingly governed by artificial intelligence (AI) technologies such as machine learning, big data analysis, facial recognition, and robotics. For decades, an ideology of apocalyptic progress and cosmic transformation has accompanied the advancement of AI in the United States; that vision is intimately connected to transhumanism, the idea that humanity can transcend its limits, even mortality, using technology. Based on contributions from science and science fiction, advocates of such apocalyptic AI suggest that the world will soon see godlike machine intelligence and that human beings will upload their minds into immortal machine bodies. The arrival of this ideology in India raises questions about how global cultures can contribute to AI technology and our beliefs about AI. These beliefs have gained a foothold in Indian visions of AI, but they have not been accepted uncritically; rather, Indian scientists and futurists revise the transhumanist vision and illustrate how traditional Hindu values can add to the global perspective. By describing the arrival and reconfiguration of transhumanist ideas in India, this book reveals how the nexus of religion and technology contributes to public life and our modern self-understanding while suggesting that the apocalyptic approach to AI should be tempered by other visions. By tracing the movement of apocalyptic AI into India and exploring Indian efforts to redefine those transhumanist aspirations, Futures of Artificial Intelligence opens the door for rethinking our global approach to AI and advocates for technologies and visions of technology that advance human flourishing.

Providing the most comprehensive source available, this book surveys the state of the art in artificial intelligence (AI) as it relates to architecture. This book is organized in four parts: theoretical foundations, tools and techniques, AI in research, and AI in architectural practice. It provides a framework for the issues surrounding AI and offers a variety of perspectives. It contains 24 consistently illustrated contributions examining seminal work on AI from around the world, including the United States, Europe, and Asia. It articulates current theoretical and practical methods, offers critical views on tools and techniques, and suggests future directions for meaningful uses of AI technology. Architects and educators who are concerned with the advent of AI and its ramifications for the design industry will find this book an essential reference.

What is intelligence? How did it begin and evolve to human intelligence? Does a high level of biological intelligence require a complex brain? Can man-made machines be truly intelligent? Is AI fundamentally different from human intelligence? In Birth of Intelligence, distinguished neuroscientist Daeyeol Lee tackles these pressing fundamental issues. To better prepare for future society and its technology, including how the use of AI will impact our lives, it is essential to understand the biological root and limits of human intelligence. After systematically reviewing biological and computational underpinnings of decision making and intelligent behaviors, Birth of Intelligence proposes that true intelligence requires life.