Artificial Intelligence is the need of the hour. This technology of today is neither an elementary school math nor a rocket science application. The understanding of AI not only allows business decision makers and enthusiasts to make advancements in technologies but also let them make processes better. Another term that is doing the rounds is artificial general intelligence (AGI) which encompasses human-level cognitive ability making automation think and work like a human mind. So how do you benefit from AI and the latest advancements that move around it? Here are the Top 10 Books on AI you cannot afford to miss to stay in vogue about the latest happenings in Artificial Intelligence.
Scikit-Learn and TensorFlow underline practical examples, minimal theory, with two production-ready Python frameworks to gain an intuitive understanding of the concepts and tools used to build intelligent systems. Reading this book, the readers will learn how to use a range of techniques, from the simple Linear Regression and progressing to Deep Neural Networks. Readers who have some programming experience and are enthusiastic to code a machine learning project should get in hands this guide. Scikit-Learn is an accessible framework implementing many algorithms efficiently thus serving as a great machine learning beginning point. The book also guides on TensorFlow, which is a more complex library for distributed numerical computation and is ideal to train and run very large neural networks.
The Fourth Age offers a fascinating insight into AI, robotics, and their extraordinary implications for our species, in the new age digital era of today.
In The Fourth Age, Byron the author Reese makes the case that technology has reshaped humanity just three times in history, 100,000 years ago, when fire was harnessed, which led to language.10,000 years ago, when agriculture was developed, which led to cities and warfare and lastly, 5,000 years ago, when wheel and writing were invented leading to the nation-state. Currently, the humanity is at the doorstep of a fourth change which is brought about by two technologies encapsulating AI and robotics. The Fourth Age provides extensive background information about how to make the smooth transition from creative computers, radical life extension, artificial life, machine consciousness, automation, employment, the implications of extreme prosperity, AI ethics, the future of warfare and superintelligence.
Gödel, Escher, Bach: An Eternal Golden Braid, also known as GEB, was first published in 1979. Authored by Douglas Hofstadter, GEB explores the common themes in the lives and works of logician Kurt Gödel, artist M. C. Escher, and composer Johann Sebastian Bach. This book expounds concepts which are fundamental to symmetry, mathematics and intelligence. Through illustration and analysis, the book points to how systems can acquire meaning despite being made of meaningless components. It also discusses what it is to communicate and how knowledge can be represented and stored, the methods and the limitations of symbolic representation.
As the name goes, this book offers an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations which transform these principles into practical algorithms. The book starts with a presentation of the basics of the field and covers a wide array of central topics which have not been addressed by previous textbooks, which include a discussion of the computational complexity of learning and the concepts of stability and convexity. The book covers important algorithmic paradigms like the neural networks, structured output learning and stochastic gradient descent. Readers can catch a glimpse of the emerging theoretical concepts like the PAC-Bayes approach and compression-based bounds.
In this book, users learn how deep learning is applicable to a widening range of artificial intelligence problems, which include speech recognition, image classification, text classification, text-to-speech and optical character recognition. This book explains the technology behind photo tagging systems at Facebook and Google, speech recognition systems that work on your smartphone.
This book is published in October 2017 by Google AI researcher and creator of the popular Keras deep learning library, Francois Chollet. Francois includes examples for deep learning applied to computer vision, text, and sequences, making it a well-rounded book for readers looking forward to learning the Keras library while studying machine learning and deep learning fundamentals simultaneously.
This book speaks how AI will affect jobs, society, crime, war, justice, and our very sense of being human. The rise of AI has the potential to transform mankind more than any other technology. Thought changers who want to discuss how artificial intelligence is shaping the world should and must read this book.
Superintelligence: Paths, Dangers, Strategies is written by the Swedish philosopher Nick Bostrom. The book argues that if machine brains surpass human brains in general intelligence, it could replace humans as the dominant lifeform on Earth. The popularity of Bostrom’s book has made it to be translated into many languages and to be also made available as an audiobook
Machine Learning for Absolute Beginners Second Edition is written keeping the beginners in account. The book comes with simple explanations and fits the readers who do not have any coding experience. The book gives clear explanations and visual examples when any are added to make it easy and engaging to follow along at home. The book is a step-by-step guide in which the users learn how to download free datasets, data scrubbing techniques, and preparing data for analysis, including k-fold validation techniques.
An Introduction to Statistical Learning offers an accessible overview of the field of statistical learning, the book scores high as an essential toolset for the vast and complex data sets which have emerged in complicated fields ranging from finance to biology to marketing to astrophysics in the past twenty years. The topics covered in this book include resampling methods, shrinkage approaches, tree-based methods, support vector machines, linear regression, classification and clustering.
This book is built on the ideas introduced in Kurzweil’s previous books, The Age of Intelligent Machines (1990) and The Age of Spiritual Machines (1999). The book predicts that once the Singularity has been reached, the machine intelligence will be infinitely more powerful than all human intelligence combined. At the concluding levels, the intelligence will radiate outward from the planet until it saturates the universe.