Top 22 best about ai books

1. AI 2041: Ten Visions for Our Future by Kai-Fu Lee, Chen Qiufan

A detailed exploration awaits in Kai-Fu Lee, Chen Qiufan's 'AI 2041: Ten Visions for Our Future', where important concepts are laid out in an accessible yet informative way. The book, published by Crown Currency in 2021, uses speculative fiction alongside scientific analysis to predict the impact of artificial intelligence on society by 2041. Through ten captivating stories, the authors delve into how AI will influence various aspects of our lives, including healthcare, entertainment, and even our understanding of ourselves. They highlight both the potential benefits and challenges of AI, urging readers to consider its implications and prepare for a future deeply intertwined with this technology.

Who should read AI 2041: Ten Visions for Our Future?

• Technology enthusiasts
• Business leaders
• Policymakers
• Anyone interested in the future

• $9.96 for hardcover
• $14.99 for kindle

2. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford

Published by Yale University Press in 2020, 'Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence' by Kate Crawford analyzes books about ai, which is essential for understanding the broader story. Crawford reveals the hidden costs of artificial intelligence, exposing how this technology is not just about code and algorithms but also involves significant environmental, social, and political implications. The book highlights how AI is intertwined with power dynamics, perpetuating inequalities and fostering undemocratic governance. Through extensive research, Crawford argues that the pursuit of AI comes at a cost, demanding a critical examination of its impact on our planet and society.

Lesson learned

The book emphasizes that the development and deployment of AI comes with significant and often overlooked costs. It urges readers to recognize the social, environmental, and political implications of AI, urging a more responsible and ethical approach to its development and use to ensure a more equitable and sustainable future.

• $17.31 for hardcover

3. Artificial Intelligence: A Modern Approach by Stuart Russell, Peter Norvig

The work 'Artificial Intelligence: A Modern Approach' provokes thought by Stuart Russell and Peter Norvig, delving into the core ideas surrounding 'books about ai'. Published by Prentice Hall in 2003, this comprehensive text provides a foundational understanding of artificial intelligence. It explores the theory and practice of building intelligent agents, covering topics like knowledge representation, planning, learning, and reasoning. The book emphasizes a unified approach, organizing the material around the concept of rational decision-making, and showcasing real-world applications of AI.

Who should read Artificial Intelligence: A Modern Approach?

• Computer science students
• AI researchers
• Software developers
• Anyone interested in learning about AI

• $207.05 for hardcover

4. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Published by Basic Books in 2015, 'The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World' by Pedro Domingos explores the world of machine learning and the pursuit of a universal learning algorithm capable of unlocking any knowledge from data. The book dives into the research and development of these algorithms, highlighting their potential impact on various aspects of our lives, including business, science, and society. Domingos examines the concept of a "Master Algorithm" and its implications for the future, ultimately suggesting that this technology could revolutionize the way we understand and interact with the world.

Lesson learned

The book highlights the immense potential of machine learning to revolutionize our world, while simultaneously emphasizing the importance of understanding its implications and navigating the ethical considerations surrounding its development and application. It underscores the need for responsible development and implementation of AI to ensure it benefits humanity while mitigating potential risks.

• $23.91 for hardcover
• $13.99 for kindle

5. Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

Published by Oxford University Press in 2014, 'Superintelligence: Paths, Dangers, Strategies' by Nick Bostrom is a thought-provoking read that offers a thorough understanding of its key topics. The book explores the potential emergence of artificial superintelligence, a hypothetical scenario where machines surpass human intelligence. Bostrom analyzes the potential benefits and risks associated with this development, examining various paths to achieving superintelligence and the strategies needed to ensure its safe and beneficial integration into society. He emphasizes the importance of careful planning and control in the development of AI to ensure that its power is harnessed for the betterment of humanity.

Who should read Superintelligence: Paths, Dangers, Strategies?

• Technologists
• Philosophers
• Policymakers
• Anyone concerned about the future of humanity

6. Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark

Contributor: Rob Shapiro(Narrator)

'Life 3.0: Being Human in the Age of Artificial Intelligence' explores the potential impact of artificial intelligence on society, from its effect on crime, war, and jobs to its implications for humanity's very existence. Published in 2017 by Random House Audio Publishing Group, the book delves into the challenges and opportunities presented by advanced AI, prompting readers to consider the future they desire and how to ensure that AI benefits humanity. The book examines key questions about AI's role in the future, such as how to manage technological advancements responsibly, the potential consequences of superintelligence, and the ethical implications of AI development.

Awards

• Los Angeles Times Book Prize (2017)
• Goodreads Choice Award (2017)

• $24.95 for hardcover
• $3.99 for kindle

7. How the Mind Works by Steven Pinker

An insightful look at 'books about ai', 'How the Mind Works' by Steven Pinker offers readers a comprehensive perspective on this topic. Published in 1999 by W. W. Norton Company, the book explores the workings of the human mind, examining its evolution, cognitive functions, and the role of natural selection in shaping human behavior. Pinker challenges conventional thinking about the mind and emotions, suggesting that the mind is a complex information-processing system and that human nature is shaped by evolutionary forces. The book delves into the mechanisms behind perception, thought, language, and social interaction, providing insights into the nature of consciousness and intelligence.

Who should read How the Mind Works?

• Psychologists
• Cognitive Scientists
• Students of Philosophy
• Anyone interested in the nature of human thought

Lesson learned

'How the Mind Works' suggests that the human mind is a product of evolution, shaped by natural selection to solve problems and enhance survival. Understanding this evolutionary basis provides insight into our cognitive abilities, motivations, and behaviors, ultimately fostering a deeper appreciation for the intricate workings of the human mind.

8. Interpretable Machine Learning by Christoph Molnar

A detailed exploration awaits in Christoph Molnar's 'Interpretable Machine Learning', where important concepts are laid out in an accessible yet informative way. This book addresses the challenge of making machine learning models and their decisions transparent. It begins by exploring the fundamental concepts of interpretability and then delves into simple, interpretable models like decision trees, decision rules, and linear regression. Later chapters focus on model-agnostic techniques for interpreting black box models, including feature importance, accumulated local effects, and explaining individual predictions with Shapley values and LIME. Each interpretation method is explained in detail, highlighting its strengths and weaknesses, and providing guidance on its application and interpretation. The book ultimately aims to equip readers with the knowledge and tools to select and effectively apply the most suitable interpretation method for their machine learning projects.

Who should read Interpretable Machine Learning?

• Data scientists
• Machine learning practitioners
• Researchers
• Students in data science or machine learning

• $29.99 for kindle

9. Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville

Published in 2016 by The MIT Press, 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville provides a comprehensive introduction to the field of deep learning. The book covers a wide range of topics, from fundamental mathematical and conceptual background to practical applications and cutting-edge research perspectives. It delves into the core concepts of linear algebra, probability theory, information theory, and numerical computation, as well as the essential techniques used in industry, including feedforward networks, convolutional networks, and sequence modeling. The authors explore applications in natural language processing, speech recognition, computer vision, and more, and delve into theoretical topics like representation learning, probabilistic models, and deep generative models. 'Deep Learning' is designed for undergraduate and graduate students, researchers, and software engineers seeking to understand and apply this powerful technology.

Who should read Deep Learning?

• Data scientists
• Machine learning engineers
• Researchers
• Students in computer science or related fields

• $76.39 - $95.00 for hardcover

10. Neural Networks and Deep Learning by Michael Nielsen

An insightful look at 'books about ai', 'Neural Networks and Deep Learning' by Michael Nielsen offers readers a comprehensive perspective on this topic. This freely available online resource provides a thorough exploration of neural networks and deep learning, two powerful techniques revolutionizing fields like image recognition, speech recognition, and natural language processing. The book delves into the core concepts of neural networks, their biological inspiration, and their ability to learn from observational data. It then dives into deep learning, a set of techniques for optimizing learning in these networks. 'Neural Networks and Deep Learning' is designed to be accessible to a wide audience, aiming to empower readers with a strong foundation in these critical areas of artificial intelligence.

11. The Hundred-Page Machine Learning Book by Andriy Burkov

Published by Andriy Burkov in 2019, 'The Hundred-Page Machine Learning Book' by Andriy Burkov is a thought-provoking read that offers a thorough understanding of its key topics. This book provides a concise and comprehensive overview of modern machine learning, covering a broad range of topics in just one hundred pages. It aims to equip readers with a strong foundation in the field within a week of dedicated reading. 'The Hundred-Page Machine Learning Book' is accompanied by a continuously updated wiki that expands on certain chapters with supplementary information, code snippets, further reading materials, and other relevant resources. To ensure accessibility, the book is available in various formats and pricing options, including Kindle, hardcover, paperback, EPUB, and PDF. The author also encourages readers to download chapters for free before committing to a purchase.

• $31.94 - $40.01 for hardcover
• $24.95 for kindle

12. Machine Learning Yearning by Andrew Ng

Offering a fresh perspective, Andrew Ng delivers an expertly crafted narrative in 'Machine Learning Yearning'. This book focuses on the practical aspects of building successful machine learning systems, addressing the strategic decisions that arise throughout the process. It guides readers through essential considerations such as data collection, model selection, and dealing with discrepancies between training and test sets. The book aims to bridge the gap between theoretical knowledge and real-world implementation, providing a framework for making informed choices and developing more effective AI systems.

13. Artificial Intelligence and Machine Learning by Vinod Chandra S.S., Anand Hareendran S.

An insightful look at 'books about ai', 'Artificial Intelligence and Machine Learning' by Vinod Chandra S.S., Anand Hareendran S. offers readers a comprehensive perspective on this topic. Published by PHI Learning in 2014, this textbook caters to undergraduate and postgraduate students of computer science and engineering. It bridges the gap in understanding between the seemingly complex concepts of artificial intelligence and machine learning. This book emphasizes practical application with a large number of case studies, worked-out examples, and interactive explanations. It covers a broad range of topics, starting with the introduction to AI, moving on to heuristics, searching, and game playing, and then diving into the fundamentals of machine learning. It explores various types of learning, including supervised, unsupervised, reinforced, and statistical learning. The book's inclusion of algorithms, pseudocodes, and day-to-day examples makes it a valuable resource for students seeking to learn and implement AI and machine learning concepts.

14. Make Your Own Neural Network by Tariq Rashid

A detailed exploration awaits in Tariq Rashid's 'Make Your Own Neural Network', where important concepts are laid out in an accessible yet informative way. This book provides a step-by-step guide to understanding and building neural networks, focusing on demystifying the underlying mathematics and making the process approachable for a wide audience. The book begins with fundamental ideas, gradually building towards a comprehensive understanding of neural networks. It introduces the Python programming language and guides readers through the process of creating a neural network capable of recognizing handwritten digits. 'Make Your Own Neural Network' aims to make neural network development accessible to anyone, even those with limited mathematical background. The book includes practical examples, illustrations, and code that can be tested on a Raspberry Pi, making it a hands-on resource for exploring the world of AI and deep learning.

15. Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

In 'Human Compatible: Artificial Intelligence and the Problem of Control', Stuart Russell provides an engaging and well-structured narrative that presents essential information in a clear manner. Published by Viking in 2019, this book examines the potential risks and opportunities associated with the development of artificial intelligence (AI). Russell argues that achieving human-compatible AI, where machines align with our values and remain under our control, requires a fundamental shift in how AI systems are designed. He outlines a new approach to AI development, focusing on creating systems that are uncertain about human preferences and prioritize human goals over their own objectives. By emphasizing the importance of aligning AI with human values, Russell suggests a path toward a future where AI becomes a force for good, promoting human progress and prosperity.

• $28.00 for hardcover
• $4.99 for kindle

16. Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb

Published by Harvard Business Review Press in 2018, 'Prediction Machines: The Simple Economics of Artificial Intelligence' by Ajay Agrawal, Joshua Gans, and Avi Goldfarb offers a clear and practical framework for understanding the impact of artificial intelligence (AI) on business, policy, and society. The book argues that AI fundamentally reduces the cost of prediction, enabling more accurate and efficient decision-making in various aspects of life, from business operations to personal choices. By reframing AI as a tool for improving predictions, the authors provide a clear understanding of its potential benefits and challenges. They analyze how AI-driven predictions can enhance productivity, create new opportunities for business models, and reshape economic strategies. 'Prediction Machines' emphasizes that the economic impact of AI is profound, yet its underlying principles are surprisingly simple, making it accessible to a wide range of readers.

• $6.24 for hardcover
• $14.51 for kindle

17. The Fourth Industrial Revolution by Klaus Schwab

"'The Fourth Industrial Revolution' by Klaus Schwab delivers a well-researched analysis that guides readers through complex ideas with clarity and precision". Published by the World Economic Forum in 2016, this book explores the profound impact of the fourth industrial revolution, characterized by the convergence of physical, digital, and biological technologies. Schwab argues that this revolution is unlike any previous one in its scale, scope, and complexity, affecting all aspects of society, including governments, businesses, and individuals. He examines the key technologies driving this revolution, including artificial intelligence, nanotechnology, and biotechnology, and discusses their potential consequences. Schwab emphasizes the importance of shaping this revolution ethically and responsibly, ensuring that it benefits all of humanity. He advocates for a future where technology empowers people, promotes progress, and respects moral boundaries.

• $16.19 for hardcover
• $13.99 for kindle

18. The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb

Published by PublicAffairs in 2019, 'The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity' by Amy Webb presents a critical examination of the current state of artificial intelligence (AI) and its implications for society. Webb argues that the development of AI is being driven by a small group of powerful corporations, known as "The Big Nine", who prioritize their own interests over the well-being of humanity. The book highlights the lack of transparency and ethical considerations in the design and deployment of AI, raising concerns about the potential for unintended consequences and the erosion of human control. Webb emphasizes the need for greater public awareness and engagement in shaping the future of AI, urging readers to demand accountability from tech giants and advocate for ethical AI development.

• $2.43 for hardcover
• $12.99 for kindle

19. Artificial Intelligence: Foundations of Computational Agents by David L. Poole, Alan K. Mackworth

"'Artificial Intelligence: Foundations of Computational Agents' by David L. Poole, Alan K. Mackworth delivers a well-researched analysis that guides readers through complex ideas with clarity and precision". Published by Cambridge University Press in 2010, this book is designed for undergraduate and graduate students, as well as professionals, researchers, and independent learners. It presents artificial intelligence (AI) from the perspective of designing intelligent computational agents. The book focuses on a coherent framework for understanding the fundamentals of AI, demonstrating how different approaches fit together within a multidimensional design space. It effectively balances theory and practice, showing the link between them and promoting the development of both the science and engineering applications of AI. The authors include the latest developments in the field while maintaining a focus on core principles and tools, encouraging readers to further explore and learn on their own. The book is accompanied by an online learning environment, artint.info, offering interactive experiments, animations, lecture slides, and a knowledge representation system for further study and problem-solving.

• $18.84 - $73.95 for hardcover
• $7.99 for kindle

20. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy

"'Machine Learning: A Probabilistic Perspective' by Kevin P. Murphy is a thorough study of books about ai, allows readers to explore the central themes of the book, specifically focusing on the use of probabilistic models and inference as a foundation for machine learning. This book offers a comprehensive introduction to the field, covering fundamental concepts like probability, optimization, and linear algebra, while also delving into recent developments such as conditional random fields, L1 regularization, and deep learning. Through clear explanations, illustrative examples, and pseudo-code for key algorithms, the author emphasizes a model-based approach to machine learning, utilizing graphical models to represent complex relationships within data. The book's freely available MATLAB software package, PMTK (probabilistic modeling toolkit), provides readers with practical tools to implement the concepts learned."

• $57.98 - $100.56 for hardcover
• $68.99 for kindle

21. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric J. Topol

"The work 'Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again' provokes thought by Eric J. Topol, delving into the core ideas surrounding 'books about ai' by exploring how AI can revolutionize healthcare and enhance the doctor-patient relationship. Topol argues that by automating mundane tasks and assisting with diagnosis and treatment, AI can alleviate the burden on physicians, allowing them to focus on providing compassionate and personalized care. The book presents a vision for the future of medicine, where AI acts as a powerful tool to improve patient outcomes, reduce errors, and ultimately make healthcare more human-centered."

• $8.64 - $17.99 for hardcover

22. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World by Marco Iansiti, Karim R. Lakhani

"'Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World' by Marco Iansiti, Karim R. Lakhani delivers a well-researched analysis that guides readers through complex ideas with clarity and precision. The book delves into the transformative power of AI, outlining how AI-centric organizations are revolutionizing business models and redefining competition. The authors examine how AI-driven processes enable massive scalability, increased scope, and accelerated learning, creating opportunities for companies to redefine their strategies and operate in a way that transcends traditional boundaries. By analyzing the impact of AI on various industries and companies, the book presents a framework for adapting to the new era of AI-driven competition, providing insights for leaders to navigate the changing landscape and develop effective strategies for success in this rapidly evolving environment."

• $16.06 - $17.99 for hardcover