Top Artificial Intelligence (AI) Books to Read in 2022-2023
The ability of a machine to reason, learn, and solve problems in the same ways that people do constitutes artificial intelligence. The beautiful thing about artificial intelligence is that you can construct a computer with pre-programmed algorithms that can function with its own intelligence, so you don’t need to pre-program a machine to perform something. Here are a few examples of AI that we experience frequently:
- Self -Driving cars
- Digital Assistants like Google Assistant and Siri
- Movie recommendations systems
- Smart farming
- Face recognition
One of the most frequently used buzzwords in technology today is artificial intelligence. We’re learning more and more about how computers can mimic human thought processes and even complete jobs that were once thought too complex for machines to complete, thanks to innovations like Siri and Alexa. For a while now, the concept of artificial intelligence has occupied the minds of philosophers, technologists, and science fiction writers. AI remains somewhat of a mystery to many people, which is difficult to define or comprehend. We have compiled a list of books to assist you in doing that so that you may learn more about the exciting field of artificial intelligence.
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.
Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades. You’ll find lucid, easy-to-read coverage of problem-solving methods, representation and models, game playing, automated understanding of natural languages, heuristic search theory, robot systems, heuristic scene analysis and specific artificial-intelligence accomplishments. Related subjects are also included: predicate-calculus theorem proving, machine architecture, psychological simulation, automatic programming, novel software techniques, industrial automation and much more.
A new age is just beginning for us. What was once science fiction is quickly becoming a reality as AI alters war, crime, law, work, society, and even our idea of what it means to be human. AI can potentially change our collective future more than any other technology. No one is better suited to do so than Max Tegmark, an MIT professor and co-founder of the Future of Life Institute, whose work has supported mainstream research on how to keep AI benign.
This book explores the next stage of human evolution by immersing readers in the most cutting-edge AI thought process. The book looks at how to profit from automation without eliminating jobs, ensure that future AI systems work as planned without malfunctioning or being compromised, and succeed in life with AI without falling victim to lethal autonomous robots.
When it comes to artificial intelligence, we either hear of a paradise on earth or of our imminent extinction. It’s time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we’ll be discussing these issues long after the last page is turned.
This book covers all of the critical developments in artificial intelligence (AI) since the 2003 publication of the previous version. Significant AI applications include practical speech recognition, machine translation, autonomous vehicles, and home robotics, all widely used. There have been algorithmic achievements, such as the checkers game solution. A theoretical study has been conducted, particularly in computer vision, machine learning, and probabilistic reasoning.
Ray Kurzweil, in his much-anticipated How to Create a Mind, he takes this exploration to the next step: reverse-engineering the brain to understand precisely how it works, then applying that knowledge to create vastly intelligent machines.
If data-ism is today’s rising philosophy, this book will be its bible. The quest for universal learning is one of the most significant, fascinating, and revolutionary intellectual developments of all time. A groundbreaking book, The Master Algorithm is the essential guide for anyone and everyone wanting to understand not just how the revolution will happen but how to be at its forefront.
Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning by James V Stone
This beautifully illustrated book provides a casual introduction to the main neural network learning techniques, followed by in-depth mathematical studies. We discuss modern deep neural networks and historically noteworthy neural networks (like perceptrons) (e.g., generative adversarial networks). This accessible introduction to algorithmic engines of contemporary artificial intelligence includes a thorough lexicon, educational supplements (such as Bayes’ theorem), and a list of additional readings. Online computer programs constructed from open-source repositories are utilized to give students hands-on experience with neural networks, and PowerPoint slides are also used to improve training.
In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization–whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly–or to completely reimagine them. AI is changing all the rules of how companies operate.
Thanks to this, anyone interested in using machine learning to solve real-world issues now has a much-needed starting point. This book makes machine learning understandable and practical. It includes:
- How machine learning is applied in daily life.
- In addition to learning how to carry out pattern-oriented tasks and conduct data analysis, Python and R are presented.
- Utilizing R studio to code in R.
- Python coding with Anaconda.
Business executives who wish to use artificial intelligence to boost the effectiveness of their organizations and the standard of living in their communities can use the practical guide Applied Artificial Intelligence. Here is your blueprint: if you want to generate enterprise-wide innovation by fusing data, technology, design, and people to address actual problems.
This book is committed to supporting you in using machine learning and artificial intelligence to make specific business decisions. You won’t find generalizations about the future of humanity or an abundance of information on TensorFlow code problems in this book. Instead, it will demonstrate how to organize a diverse team of specialists, select the best possibilities, conduct strategic experiments, and purposefully design your solutions to benefit both your business and society.
In the public’s mind, superhuman artificial intelligence is a rising tidal wave that threatens employment, interpersonal relationships, and civilization. The conflict between humans and machines is inevitable, with an all-too-obvious outcome. This groundbreaking book by renowned AI researcher Stuart Russell makes the case that this nightmare is avoidable, but only if we rethink AI from the ground up. In his opening chapter, Russell explores the idea of intelligence in both people and machines. He describes the near-term advantages we may anticipate, such as intelligent personal assistants and noticeably speedier scientific research, as well as the AI developments that must occur before we reach superhuman AI. Additionally, he describes how people are already exploiting AI, from lethal autonomous weapons to viral sabotage.
The fascinating and entertaining history of how artificial intelligence as we know it now is told in Genius Makers. The book is rife with vivid anecdotes and behind-the-scenes information on the birth of deep learning, informed by hundreds of individual interviews. It primarily focuses on the individuals who helped bring about the current AI revolution, including Geoff Hinton, Demis Hassabis, Yann LeCun, Fei-Fei Li, Jeff Dean, and others.
This book is required for anyone interested in artificial intelligence’s history and current state.
This profoundly ambitious and original book breaks down a vast track of difficult intellectual terrain. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom’s work nothing less than a reconceptualization of the essential task of our time.
This book is an excellent and brief overview of various ML techniques for solving supervised and unsupervised learning problems. The author covers every topic by providing just enough detail in a clear and concise way. Highly recommended for readers who want a solid understanding of ML in only 100-150 pages
More and more real-world responsibilities are being given to artificial intelligence, including those related to our homes, hospitals, schools, and financial institutions. How do we ensure that the judgment and values of these more sophisticated AI systems, whose decision-making we don’t directly control or even comprehend, match our own?
This book thoroughly analyzes the “alignment problem” in AI, including its philosophical and technical underpinnings. The book’s exploration of inverse reinforcement learning and its potential for creating AI systems we can trust is particularly fascinating.
Girl Decoded: A Scientist’s Quest to Reclaim Our Humanity by Bringing Emotional Intelligence to Technology by Rana el Kaliouby
In a captivating memoir, an Egyptian American visionary and scientist provides an intimate view of her personal transformation as she follows her calling—to humanize our technology and how we connect with one another.
In the field of AI, Gary Marcus is a divisive character. He is one of the most outspoken and steadfast critics of today’s dominant big data and deep learning-based AI paradigm. He frequently criticizes deep learning for lacking robustness and common sense, stating that old symbolic methods must be included in the future of AI.
Marcus’ primary points regarding the state of AI today are nicely summarized in Rebooting AI. You should read it. John Stuart Mill once said: “He knows little of that who knows only his own side of the argument.”
You will leave this book with a basic understanding of artificial intelligence and its effects. Important ideas like Machine Learning, Deep Learning, Natural Language Processing, Robotics, and more are introduced in a non-technical way. The author also elaborates on the issues surrounding the potential effects of AI on society trends, ethical issues, governmental systems, corporate structures, and daily living.
The construction of effective AI systems is the framework in which this book teaches advanced Common Lisp techniques. It develops and debugs robust, practical programs while showcasing superb programming styles and significant AI concepts. It reconstructs real, complicated AI applications using cutting-edge Common Lisp. It is a valuable addition to general AI courses and a must-have resource for any expert programmer.
It briefly introduces machine learning, which is the technology behind applications like recommendation systems, face recognition, and driverless automobiles. For the general reader, the author provides a succinct summary of the topic by outlining its history, crucial learning algorithms, and examples of applications.
Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, James Barrat’s Our Final Inventionexplores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?
In AI Superpowers, Kai-fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.
Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples and Case Studies (The MIT Press) by John D. Kelleher, Brian Mac Namee
It provides a thorough overview of the most powerful machine learning techniques in predictive data analytics, covering theoretical ideas and actual implementations. Case studies show how these models are applied in a larger corporate environment, and illustrative examples are used to supplement the technical and mathematical information. The book also presents two case studies that detail specific data analytics projects through each stage of development, from articulating the business challenges to putting the analytics solution into practice. Finally, it discusses approaches for evaluating prediction models.
The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind by Marvin Minsky
In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work. He argues persuasively that emotions, intuitions, and feelings are not distinct things, but different ways of thinking.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field’s intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
Note: The above list of AI books is selected on the basis of their reviews on Amazon, social media influence, popularity, and online mentions in AI domains
Please note: This is not a ranking article.
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Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications