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The AI Textbook: Your Guide to Navigating the World of Artificial Intelligence
The world of artificial intelligence (AI) is rapidly evolving, impacting nearly every facet of our lives. From self-driving cars to personalized medicine, AI's influence is undeniable. But understanding this complex field can feel overwhelming, especially without the right resources. This comprehensive guide serves as your ultimate "AI textbook," providing a clear, concise, and accessible overview of key AI concepts, applications, and future implications. We'll demystify complex terminology, explore various AI approaches, and equip you with the knowledge to navigate this exciting and transformative technological landscape.
Understanding the Fundamentals of AI
Before diving into specific applications, it’s crucial to grasp the core concepts underpinning AI. This section will lay the groundwork for a deeper understanding of the field.
What is Artificial Intelligence?
At its core, AI refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. The goal is to create systems capable of performing tasks that typically require human intelligence.
Types of AI: A Deep Dive
The field of AI encompasses diverse approaches. We can broadly categorize AI systems as:
Narrow or Weak AI: Designed to perform a specific task, like playing chess or recommending products. This is the most common type of AI currently in use.
General or Strong AI: Hypothetical AI with human-level intelligence, capable of performing any intellectual task that a human can. This type of AI doesn't yet exist.
Super AI: Hypothetical AI surpassing human intelligence in all aspects. This remains purely speculative.
Key Concepts in AI: Machine Learning and Deep Learning
Two crucial concepts within AI are machine learning (ML) and deep learning (DL):
Machine Learning: A subset of AI where systems learn from data without explicit programming. Algorithms identify patterns, make predictions, and improve their accuracy over time.
Deep Learning: A more advanced form of ML that utilizes artificial neural networks with multiple layers (hence "deep") to analyze data and extract complex features. Deep learning powers many advanced AI applications, such as image recognition and natural language processing.
Applications of AI Across Industries
AI's impact stretches across various sectors, revolutionizing how we work, live, and interact with the world.
AI in Healthcare: Diagnosis and Treatment
AI is transforming healthcare through improved diagnostic accuracy, personalized medicine, and drug discovery. Algorithms analyze medical images, predict patient outcomes, and assist in developing new treatments.
AI in Finance: Fraud Detection and Algorithmic Trading
In finance, AI powers fraud detection systems, optimizes investment strategies through algorithmic trading, and provides personalized financial advice.
AI in Transportation: Autonomous Vehicles and Traffic Optimization
Self-driving cars, traffic management systems, and route optimization algorithms are just a few examples of AI's transformative impact on transportation.
AI in Customer Service: Chatbots and Virtual Assistants
AI-powered chatbots and virtual assistants provide 24/7 customer support, automate routine tasks, and personalize customer experiences.
The Future of AI: Challenges and Opportunities
While AI offers immense potential, it also presents challenges. Ethical considerations, job displacement concerns, and the potential for bias in algorithms require careful attention. However, ongoing research and development promise to address these challenges and unlock even more transformative applications in the years to come.
Ethical Considerations in AI Development
Developing ethical guidelines and regulations for AI is crucial to ensure responsible innovation and prevent misuse.
The Impact of AI on the Job Market
The automation potential of AI raises concerns about job displacement. However, it also creates new opportunities in AI-related fields.
Addressing Bias and Fairness in AI Algorithms
Addressing bias in AI algorithms is essential to ensure fairness and prevent discrimination. This requires careful data selection and algorithm design.
Conclusion
This "AI textbook" has provided a foundational understanding of artificial intelligence, covering its core concepts, applications, and future implications. While the field is complex and constantly evolving, this guide serves as a valuable starting point for anyone seeking to navigate the exciting world of AI. Further exploration into specific areas of interest will deepen your understanding and empower you to participate in this transformative technological revolution.
FAQs
Q1: What is the difference between AI, Machine Learning, and Deep Learning?
A1: AI is the broad concept of machines mimicking human intelligence. Machine learning is a subset of AI where systems learn from data without explicit programming. Deep learning is a more advanced form of machine learning using artificial neural networks with multiple layers.
Q2: Is AI dangerous?
A2: AI itself isn't inherently dangerous. However, like any powerful technology, it can be misused. Ethical considerations and responsible development are crucial to mitigate potential risks.
Q3: What are some good resources for learning more about AI?
A3: Online courses (Coursera, edX), university-level programs, and reputable online publications are excellent resources for in-depth learning.
Q4: What kind of jobs are available in the AI field?
A4: The AI field offers a wide range of roles, including data scientists, machine learning engineers, AI ethicists, and AI researchers.
Q5: How can I contribute to the development of ethical AI?
A5: Engage in discussions about AI ethics, support research on responsible AI development, and advocate for policies that promote fairness and transparency in AI systems.
| ai textbook: The AI Book Ivana Bartoletti, Anne Leslie, Shân M. Millie, 2020-06-29 Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important |
| ai textbook: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
| ai textbook: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| ai textbook: The Essence of Artificial Intelligence Alison Cawsey, 1998 A concise, practical introduction to artificial intelligence, this title starts with the fundamentals of knowledge representation, inference, expert systems, natural language processing, machine learning, neural networks, agents, robots, and much more. Examples and algorithms are presented throughout, and the book includes a complete glossary. |
| ai textbook: Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016-11-10 An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. |
| ai textbook: The Singularity Is Nearer Ray Kurzweil, 2024-06-25 The noted inventor and futurist’s successor to his landmark book The Singularity Is Near explores how technology will transform the human race in the decades to come Since it was first published in 2005, Ray Kurzweil’s The Singularity Is Near and its vision of an exponential future have spawned a worldwide movement. Kurzweil's predictions about technological advancements have largely come true, with concepts like AI, intelligent machines, and biotechnology now widely familiar to the public. In this entirely new book Ray Kurzweil brings a fresh perspective to advances toward the Singularity—assessing his 1999 prediction that AI will reach human level intelligence by 2029 and examining the exponential growth of technology—that, in the near future, will expand human intelligence a millionfold and change human life forever. Among the topics he discusses are rebuilding the world, atom by atom with devices like nanobots; radical life extension beyond the current age limit of 120; reinventing intelligence by connecting our brains to the cloud; how exponential technologies are propelling innovation forward in all industries and improving all aspects of our well-being such as declining poverty and violence; and the growth of renewable energy and 3-D printing. He also considers the potential perils of biotechnology, nanotechnology, and artificial intelligence, including such topics of current controversy as how AI will impact employment and the safety of autonomous cars, and After Life technology, which aims to virtually revive deceased individuals through a combination of their data and DNA. The culmination of six decades of research on artificial intelligence, The Singularity Is Nearer is Ray Kurzweil’s crowning contribution to the story of this science and the revolution that is to come. |
| ai textbook: Artificial Intelligence and Games Georgios N. Yannakakis, Julian Togelius, 2018-02-17 This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading. |
| ai textbook: Artificial Intelligence for a Better Future Bernd Carsten Stahl, 2021-03-17 This open access book proposes a novel approach to Artificial Intelligence (AI) ethics. AI offers many advantages: better and faster medical diagnoses, improved business processes and efficiency, and the automation of boring work. But undesirable and ethically problematic consequences are possible too: biases and discrimination, breaches of privacy and security, and societal distortions such as unemployment, economic exploitation and weakened democratic processes. There is even a prospect, ultimately, of super-intelligent machines replacing humans. The key question, then, is: how can we benefit from AI while addressing its ethical problems? This book presents an innovative answer to the question by presenting a different perspective on AI and its ethical consequences. Instead of looking at individual AI techniques, applications or ethical issues, we can understand AI as a system of ecosystems, consisting of numerous interdependent technologies, applications and stakeholders. Developing this idea, the book explores how AI ecosystems can be shaped to foster human flourishing. Drawing on rich empirical insights and detailed conceptual analysis, it suggests practical measures to ensure that AI is used to make the world a better place. |
| ai textbook: AI and Humanity Illah Reza Nourbakhsh, Jennifer Keating, 2020-03-10 An examination of the implications for society of rapidly advancing artificial intelligence systems, combining a humanities perspective with technical analysis; includes exercises and discussion questions. AI and Humanity provides an analytical framing and a common language for understanding the effects of technological advances in artificial intelligence on society. Coauthored by a computer scientist and a scholar of literature and cultural studies, it is unique in combining a humanities perspective with technical analysis, using the tools of literary explication to examine the societal impact of AI systems. It explores the historical development of these technologies, moving from the apparently benign Roomba to the considerably more sinister semi-autonomous weapon system Harpy. The book is driven by an exploration of the cultural and etymological roots of a series of keywords relevant to both AI and society. Works examined range from Narrative of the Life of Frederick Douglass, given a close reading for its themes of literacy and agency, to Simon Head's critique of the effects of surveillance and automation on the Amazon labor force in Mindless. Originally developed as a textbook for an interdisciplinary humanities-science course at Carnegie Mellon, AI & Humanity offers discussion questions, exercises (including journal writing and concept mapping), and reading lists. A companion website provides updated resources and a portal to a video archive of interviews with AI scientists, sociologists, literary theorists, and others. |
| ai textbook: A Citizen's Guide to Artificial Intelligence John Zerilli, 2021-02-23 A concise but informative overview of AI ethics and policy. Artificial intelligence, or AI for short, has generated a staggering amount of hype in the past several years. Is it the game-changer it's been cracked up to be? If so, how is it changing the game? How is it likely to affect us as customers, tenants, aspiring home-owners, students, educators, patients, clients, prison inmates, members of ethnic and sexual minorities, voters in liberal democracies? This book offers a concise overview of moral, political, legal and economic implications of AI. It covers the basics of AI's latest permutation, machine learning, and considers issues including transparency, bias, liability, privacy, and regulation. |
| ai textbook: Interpretable Machine Learning Christoph Molnar, 2020 This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. |
| ai textbook: Artificial Intelligence David L. Poole, Alan K. Mackworth, 2017-09-25 Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains. |
| ai textbook: Introduction to Artificial Intelligence Wolfgang Ertel, 2018-01-18 This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning. Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW). Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material. |
| ai textbook: Probabilistic Machine Learning Kevin P. Murphy, 2022-03-01 A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach. |
| ai textbook: Human Compatible Stuart Russell, 2019-10-08 The most important book on AI this year. --The Guardian Mr. Russell's exciting book goes deep, while sparkling with dry witticisms. --The Wall Street Journal The most important book I have read in quite some time (Daniel Kahneman); A must-read (Max Tegmark); The book we've all been waiting for (Sam Harris) A leading artificial intelligence researcher lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machines In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable. In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage. If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial. |
| ai textbook: Artificial Intelligence and the Law Jan De Bruyne, 2021-01-18 Artificial intelligence (AI) is becoming increasingly more prevalent in our daily social and professional lives. Although AI systems and robots bring many benefits, they present several challenges as well. The autonomous and opaque nature of AI systems implies that their commercialisation will affect the legal and regulatory framework.0In this comprehensive book, scholars critically examine how AI systems may impact Belgian law. It contains contributions on consumer protection, contract law, liability, data protection, procedural law, insurance, health, intellectual property, arbitration, lethal autonomous weapons, tax law, employment law, ethics,?While specific topics of Belgian private and public law are thoroughly addressed, the book also provides a general overview of a number of regulatory and ethical AI evolutions and tendencies in the European Union. Therefore, it is a must-read for legal scholars, practitioners and government officials as well as for anyone with an interest in law and AI. |
| ai textbook: Edge AI Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen, 2020-08-31 As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing. |
| ai textbook: Linguistics for the Age of AI Marjorie Mcshane, Sergei Nirenburg, 2021-03-02 A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language. |
| ai textbook: AI 2041 Kai-Fu Lee, Chen Qiufan, 2024-03-05 How will AI change our world within twenty years? A pioneering technologist and acclaimed writer team up for a “dazzling” (The New York Times) look at the future that “brims with intriguing insights” (Financial Times). This edition includes a new foreword by Kai-Fu Lee. A BEST BOOK OF THE YEAR: The Wall Street Journal, The Washington Post, Financial Times Long before the advent of ChatGPT, Kai-Fu Lee and Chen Qiufan understood the enormous potential of artificial intelligence to transform our daily lives. But even as the world wakes up to the power of AI, many of us still fail to grasp the big picture. Chatbots and large language models are only the beginning. In this “inspired collaboration” (The Wall Street Journal), Lee and Chen join forces to imagine our world in 2041 and how it will be shaped by AI. In ten gripping, globe-spanning short stories and accompanying commentary, their book introduces readers to an array of eye-opening settings and characters grappling with the new abundance and potential harms of AI technologies like deep learning, mixed reality, robotics, artificial general intelligence, and autonomous weapons. |
| ai textbook: AI Ethics Mark Coeckelbergh, 2020-04-07 This overview of the ethical issues raised by artificial intelligence moves beyond hype and nightmare scenarios to address concrete questions—offering a compelling, necessary read for our ChatGPT era. Artificial intelligence powers Google’s search engine, enables Facebook to target advertising, and allows Alexa and Siri to do their jobs. AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, AI Ethics goes beyond the usual hype and nightmare scenarios to address concrete questions. Mark Coeckelbergh describes influential AI narratives, ranging from Frankenstein’s monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. He offers an overview of important ethical issues, including privacy concerns, responsibility and the delegation of decision making, transparency, and bias as it arises at all stages of data science processes. He also considers the future of work in an AI economy. Finally, he analyzes a range of policy proposals and discusses challenges for policymakers. He argues for ethical practices that embed values in design, translate democratic values into practices and include a vision of the good life and the good society. |
| ai textbook: Artificial Intelligence in Behavioral and Mental Health Care David D. Luxton, 2015-09-10 Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. - Summarizes AI advances for use in mental health practice - Includes advances in AI based decision-making and consultation - Describes AI applications for assessment and treatment - Details AI advances in robots for clinical settings - Provides empirical data on clinical efficacy - Explores practical issues of use in clinical settings |
| ai textbook: The AI Advantage Thomas H. Davenport, 2019-08-06 Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage. In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM's Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the “moonshot” (curing cancer, or synthesizing all investment knowledge); look for the “low-hanging fruit” to make your company more efficient. Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed—important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (“analytics on steroids”), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI. A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review. |
| ai textbook: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| ai textbook: Artificial Intelligence for All Chakraborty Utpal, 2020-02-18 Artificial Intelligence, the Revolutionary Transformation that no one can escape KEY FEATURES Provides perfect 'playground' for enterprises and institutions globally to develop Artificial Intelligence solutionsThe world has achieved an enormous amount of technological advancement and skyrocketing progress in mass Digitization, Data Science, and FinTechThe gist of the golden era of AI and FinTechAI-powered autonomous vehicles are undoubtedly the future. Autonomous vehicles are the dawn of a whole new lifestyleUsing Artificial Intelligence to redefine their products, processes and strategies Providing banking and financial services to the customers through a variety of digital channelsA preliminary guide for enterprises and businesses to revisit their AI strategy DESCRIPTION The book 'Artificial Intelligence for All' is a snapshot of AI applications in different industries, society, and everyday life. The book is written considering possibilities AI can bring in the Indian context and considering Indian industries and economy at the center stage. The book starts with describing the race for the supremacy of different countries in the field of Artificial Intelligence that has already taken a great momentum and how AI has managed to influence even mainstream politics and the world leaders. In the subsequent chapters, the book brings in AI applications primarily in the Banking and Finance sectors like Financial Crime detection using AI, Credit Risk Assessment, AI-powered conversational banking, Predictive Analytics, and recommendations in Banking and Finance. In few of the chapters, it goes deep into Machine Learning, Deep Learning, Neural Network and analogy with the human brain for readers who wants to go deeper into the subject, at the same time the content and explanations remain very simple for non-technical readers.How AI is powering the self-driving autonomous vehicles and its implication in the society, job, and the world economy, and it's transforming the world of home automation, will be another area of interest in the book. A full chapter is dedicated for CIOs and CTOs to consider AI top in their priority list. Applications of AI in Sports are going to be interesting for sports lovers as well as professionals working in the Sports and Computer Games domain.The book also gives special emphasis on Conversational AI like Virtual Assistances and ChatBots and their utility in different sectors. A chapter dedicated for healthcare and medicine provides a complete overview of AI applications in the field and how it's transforming clinical imaging, personalized medicines, drug discovery, and predictions and forecasting health-related events and many more.Cognitive Cyber Security using AI and Machine Learning would be an area of interest for the readers in the field of Cyber Security. The chapter talks about various modern cognitive cybersecurity tools and techniques to fight with the ever-evolving cybercrime space. 'Journey of a Digital Traveler' describes how AI is transforming the travel and tourism industry.The book also includes top 100 business use cases which illustrate possible applications in various fields. WHAT WILL YOU LEARN This book is for both technical and non-technical readers, a cutting edge technology like Artificial Intelligence is simplified for all and a genuine effort has been made to democratize it as much as possible. The book will provide insights into the real applications of AI in different industries like health care and medicine, banking and finance, manufacturing, retail, sports, and many more, including how it's transforming our life which probably many of us are not even aware of. And most importantly how a country like India can be benefitted by embracing this groundbreaking technology and the huge opportunities and economic impact that AI can bring. Also, you will get to know how different countries like USA, CHINA, UK, EUROPE, RUSSIA, including INDIA is already in the race of being AI Superpower; because AI is the future and whoever becomes the leader in AI will become the ruler of the world. WHO THIS BOOK IS FOR This book is useful for AI Professionals, Data Scientists......The content of the book is for both Technical and Non Technical readers who wants to know the applications of AI in different industries.No prior technical or programming experience is required to understand this book.This book can be used as a hand book for Data Scientist and Business SMEs who are in the process of identifying different use cases of Artificial Intelligence in their respective domains. TABLE OF CONTENTS1. Super Powers of AI - The Leaders and the Contenders 2. AI - The Core Fabric for NextGen Banking 3. How an AI Framework can be a Game-Changer in Your AI Journey 4. Artificial Neural Networks 5. The Next Wave of Automation will Transform our Living Experience 6. Self-Driving Cars - Socio Economic Impact of Autonomous Vehicles 7. How Artificial Intelligence is Transforming the BFSI Sector 438. AI Now is a Race Among Startups and Tech Giants 9. AI in the top of priorities for CIOs and CTOs10. AI in Sports11. How a Country can be Transformed Using Artificial Intelligence12. Don't Underestimate the Power of an AI Chatbot 13. Industry Adoption of Cognitive and Artificial Intelligence 14. Artificial Intelligence - The Biggest Disruptor in the BFSI Industry15. AI in Healthcare16. AI in Cyber Security - Cognitive Cyber Defense17. Be Aware of Cyber Threat 18. AI Revolution in India - National Strategy for AI19. AI in Tour and Travels - Journey of a Digital Traveler 20. Top 100 Business Use Cases of Artificial Intelligence21. T Impact of Modern Automation on Employment |
| ai textbook: Trustworthy AI Beena Ammanath, 2022-03-15 An essential resource on artificial intelligence ethics for business leaders In Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI. Filled with deep insights and actionable steps for enabling trust across the entire AI lifecycle, the book presents: In-depth investigations of the key characteristics of trustworthy AI, including transparency, fairness, reliability, privacy, safety, robustness, and more A close look at the potential pitfalls, challenges, and stakeholder concerns that impact trust in AI application Best practices, mechanisms, and governance considerations for embedding AI ethics in business processes and decision making Written to inform executives, managers, and other business leaders, Trustworthy AI breaks new ground as an essential resource for all organizations using AI. |
| ai textbook: AI for the Sustainable Development Goals Henrik Skaug Sætra, 2022-02-23 What is artificial intelligence? What are the Sustainable Development Goals (SDGs)? How does AI affect the SDGs? Artificial Intelligence has a real impact on our lives and on our environment, and the Sustainable Development Goals enable us to evaluate these impacts in a systematic manner. This book shows that doing so requires us to understand the context of AI – the infrastructure it is built on, who develops it, who owns it, who has access to it, who uses it, and what it is used for – rather than relying on an isolationist theory of technology. By doing so, we can analyze not only the direct effects of AI on sustainability, but also the indirect – or second-order – effects. AI for the Sustainable Development Goals shows how AI potentially affects all SDGs – both positively and negatively. |
| ai textbook: Artificial Intelligence for Human Computer Interaction: A Modern Approach Yang Li, Otmar Hilliges, 2021-11-04 This edited book explores the many interesting questions that lie at the intersection between AI and HCI. It covers a comprehensive set of perspectives, methods and projects that present the challenges and opportunities that modern AI methods bring to HCI researchers and practitioners. The chapters take a clear departure from traditional HCI methods and leverage data-driven and deep learning methods to tackle HCI problems that were previously challenging or impossible to address. It starts with addressing classic HCI topics, including human behaviour modeling and input, and then dedicates a section to data and tools, two technical pillars of modern AI methods. These chapters exemplify how state-of-the-art deep learning methods infuse new directions and allow researchers to tackle long standing and newly emerging HCI problems alike. Artificial Intelligence for Human Computer Interaction: A Modern Approach concludes with a section on Specific Domains which covers a set of emerging HCI areas where modern AI methods start to show real impact, such as personalized medical, design, and UI automation. |
| ai textbook: The AI Marketing Canvas Raj Venkatesan, Jim Lecinski, 2021-05-18 This book offers a direct, actionable plan CMOs can use to map out initiatives that are properly sequenced and designed for success—regardless of where their marketing organization is in the process. The authors pose the following critical questions to marketers: (1) How should modern marketers be thinking about artificial intelligence and machine learning? and (2) How should marketers be developing a strategy and plan to implement AI into their marketing toolkit? The opening chapters provide marketing leaders with an overview of what exactly AI is and how is it different than traditional computer science approaches. Venkatesan and Lecinski, then, propose a best-practice, five-stage framework for implementing what they term the AI Marketing Canvas. Their approach is based on research and interviews they conducted with leading marketers, and offers many tangible examples of what brands are doing at each stage of the AI Marketing Canvas. By way of guidance, Venkatesan and Lecinski provide examples of brands—including Google, Lyft, Ancestry.com, and Coca-Cola—that have successfully woven AI into their marketing strategies. The book concludes with a discussion of important implications for marketing leaders—for your team and culture. |
| ai textbook: The Atlas of AI Kate Crawford, 2021-04-06 The hidden costs of artificial intelligence, from natural resources and labor to privacy and freedom What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind automated services, to the data AI collects from us. Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world. |
| ai textbook: Lithium-ion Batteries , 2019 This is the first machine-generated scientific book in chemistry published by Springer Nature. Serving as an innovative prototype defining the current status of the technology, it also provides an overview about the latest trends of lithium-ion batteries research. This book explores future ways of informing researchers and professionals. State-of-the-art computer algorithms were applied to: select relevant sources from Springer Nature publications, arrange these in a topical order, and provide succinct summaries of these articles. The result is a cross-corpora auto-summarization of current texts, organized by means of a similarity-based clustering routine in coherent chapters and sections. This book summarizes more than 150 research articles published from 2016 to 2018 and provides an informative and concise overview of recent research into anode and cathode materials as well as further aspects such as separators, polymer electrolytes, thermal behavior and modelling. With this prototype, Springer Nature has begun an innovative journey to explore the field of machine-generated content and to find answers to the manifold questions on this fascinating topic. Therefore it was intentionally decided not to manually polish or copy-edit any of the texts so as to highlight the current status and remaining boundaries of machine-generated content. Our goal is to initiate a broad discussion, together with the research community and domain experts, about the future opportunities, challenges and limitations of this technology.--Publisher's website. |
| ai textbook: Playing Smart Julian Togelius, 2019-01-15 THE FUTURE OF GAME DESIGN IN THE AGE OF AI: Can games measure intelligence? And how will artificial intelligence inform games of the future? In Playing Smart, Julian Togelius explores the connections between games and intelligence to offer a new vision of future games and game design. Video games already depend on AI. We use games to test AI algorithms, challenge our thinking, and better understand both natural and artificial intelligence. In the future, Togelius argues, game designers will be able to create smarter games that make us smarter in turn, applying advanced AI to help design games. In this book, he tells us how. Games are the past, present, and future of artificial intelligence. In 1948, Alan Turing, one of the founding fathers of computer science and artificial intelligence, handwrote a program for chess. Today we have IBM’s Deep Blue and DeepMind’s AlphaGo, and huge efforts go into developing AI that can play such arcade games as Pac-Man. Programmers continue to use games to test and develop AI, creating new benchmarks for AI while also challenging human assumptions and cognitive abilities. Game design is at heart a cognitive science, Togelius reminds us—when we play or design a game, we plan, think spatially, make predictions, move, and assess ourselves and our performance. By studying how we play and design games, Togelius writes, we can better understand how humans and machines think. AI can do more for game design than providing a skillful opponent. We can harness it to build game-playing and game-designing AI agents, enabling a new generation of AI-augmented games. With AI, we can explore new frontiers in learning and play. |
| ai textbook: General Video Game Artificial Intelligence Diego Pérez Liébana, Simon M. Lucas, Raluca D. Gaina, Julian Togelius, Ahmed Khalifa, Jialin Liu, 2019-10-09 Research on general video game playing aims at designing agents or content generators that can perform well in multiple video games, possibly without knowing the game in advance and with little to no specific domain knowledge. The general video game AI framework and competition propose a challenge in which researchers can test their favorite AI methods with a potentially infinite number of games created using the Video Game Description Language. The open-source framework has been used since 2014 for running a challenge. Competitors around the globe submit their best approaches that aim to generalize well across games. Additionally, the framework has been used in AI modules by many higher-education institutions as assignments, or as proposed projects for final year (undergraduate and Master's) students and Ph.D. candidates. The present book, written by the developers and organizers of the framework, presents the most interesting highlights of the research performed by the authors during these years in this domain. It showcases work on methods to play the games, generators of content, and video game optimization. It also outlines potential further work in an area that offers multiple research directions for the future. |
| ai textbook: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller, 2019-09-10 The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI. |
| ai textbook: Artificial Intelligence: Models, Algorithms and Applications Terje Solsvik Kristensen, 2021-05-31 Artificial Intelligence: Models, Algorithms and Applications presents focused information about applications of artificial intelligence (AI) in different areas to solve complex problems. The book presents 8 chapters that demonstrate AI based systems for vessel tracking, mental health assessment, radiology, instrumentation, business intelligence, education and criminology. The book concludes with a chapter on mathematical models of neural networks. The book serves as an introductory book about AI applications at undergraduate and graduate levels and as a reference for industry professionals working with AI based systems. |
| ai textbook: Cognitive Electronic Warfare: An Artificial Intelligence Approach Karen Haigh, Julia Andrusenko, 2021-07-31 This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization. |
| ai textbook: Foundations of Machine Learning, second edition Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 2018-12-25 A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. |
| ai textbook: AI Injected e-Learning Matthew Montebello, 2017-10-27 This book reviews a blend of artificial intelligence (AI) approaches that can take e-learning to the next level by adding value through customization. It investigates three methods: crowdsourcing via social networks; user profiling through machine learning techniques, and personal learning portfolios using learning analytics. Technology and education have drawn closer together over the years as they complement each other within the domain of e-learning, and different generations of online education reflect the evolution of new technologies as researcher and developers continuously seek to optimize the electronic medium to enhance the effectiveness of e-learning. Artificial intelligence (AI) for e-learning promises personalized online education through a combination of different intelligent techniques that are grounded in established learning theories while at the same time addressing a number of common e-learning issues. This book is intended for education technologists and e-learning researchers as well as for a general readership interested in the evolution of online education based on techniques like machine learning, crowdsourcing, and learner profiling that can be merged to characterize the future of personalized e-learning. |
| ai textbook: Artificial Intelligence in Surgery: Understanding the Role of AI in Surgical Practice Daniel A. Hashimoto, Guy Rosman, Ozanan R. Meireles, 2021-03-08 Build a solid foundation in surgical AI with this engaging, comprehensive guide for AI novices Machine learning, neural networks, and computer vision in surgical education, practice, and research will soon be de rigueur. Written for surgeons without a background in math or computer science, Artificial Intelligence in Surgery provides everything you need to evaluate new technologies and make the right decisions about bringing AI into your practice. Comprehensive and easy to understand, this first-of-its-kind resource illustrates the use of AI in surgery through real-life examples. It covers the issues most relevant to your practice, including: Neural Networks and Deep Learning Natural Language Processing Computer Vision Surgical Education and Simulation Preoperative Risk Stratification Intraoperative Video Analysis OR Black Box and Tracking of Intraoperative Events Artificial Intelligence and Robotic Surgery Natural Language Processing for Clinical Documentation Leveraging Artificial Intelligence in the EMR Ethical Implications of Artificial Intelligence in Surgery Artificial Intelligence and Health Policy Assessing Strengths and Weaknesses of Artificial Intelligence Research Finally, the appendix includes a detailed glossary of terms and important learning resources and techniques―all of which helps you interpret claims made by studies or companies using AI. |
| ai textbook: Fundamentals of Artificial Intelligence K.R. Chowdhary, 2020-04-04 Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI. |
| ai textbook: The Hundred-page Machine Learning Book Andriy Burkov, 2019 Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. |
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