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Book review by guest contributor, Adrian Culley: “Artificial Intelligence: A Modern Approach” (3rd Edition, 2015), by Stuart J. Russell, Peter Norvig.
For many, Artificial Intelligence: A Modern Approach is the de facto bible of artificial intelligence. It combines in-depth treatments of introductory and advanced concepts, along with historical background and accessible explanations. Including algorithms, code and pseudo-code, the book sits between master’s and Ph.D. level, but is accessible to all. Your journey on the road to the application of data science should start here.
Artificial intelligence and machine learning technology now permeate our lives. We are increasingly using them, or subject to them, whether we realise it or not. Increasingly ubiquitous implementations include practical speech recognition, machine translation, self-driving vehicles and household robotics. Artificial Intelligence: A Modern Approach helps provide clear understanding of exactly what AI and machine learning comprise, and what they can and cannot achieve. Such clarity of thought helps us move from buzzword-dropping to actual scientific understanding. Underlying concepts are explained with clear analogies and accessible language.
From algorithmic and coding perspectives, the tools provided are powerful, though we remain some distance from machine sentience, which should never be confused with AI. Turing’s “Imitation Game” remains an intriguing concept, although it is increasingly unclear whether it tests machine or human intelligence. Russell and Norvig’s book will help you gain insight about this field and enable you to apply your own critique and assessment to Turing’s test.
Algorithmic research has also seen numerous key developments since 1950, not the least of which are game theory-based thinking (particularly the work of mathematician John Nash) and the solution of the game of draughts. Much theoretical progress has also been made, particularly in such areas as probabilistic reasoning, machine learning and computer vision. The book will help you develop an appreciation for the critical role of data modelling over algorithm selection, and where the real value lies in machine learning.
The book is as close to exhaustive as is currently available in the field, including in-depth treatments of non-technical learning material whilst providing an accessible and understandable overview of major concepts.
Since the 2003 edition, increased coverage has been given to topics such as constraint satisfaction, local search planning methods, multi-agent systems, game theory, statistical natural language processing and uncertain reasoning over time. Attention has also been given to providing more detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics.
The book contains up-to-date and extensive exercises, delivering a unified, agent-based approach to AI: organising the material around the task of building intelligent agents. The comprehensive, up-to-date coverage includes a unified view of the field organized around the rational decision-making paradigm.
The author’s approach delivers in-depth coverage of basic and advanced topics, and provides a basic understanding of the frontiers of AI without compromising complexity or depth. It conveys in-depth understanding and clear explanation of such concepts as supervised and unsupervised machine learning, and thus to the layman, an understanding of why there will be no jobs for machine learning foremen!
Pseudo-code versions of the major AI algorithms are presented in a uniform fashion, and Actual Common Lisp and Python implementations of the presented algorithms are available online, as are test data sets and samples.
Although the field of research has grown considerably since its launch in Turing’s seminal 1950 paper, this volume represents both an access point for all interested and in-depth information for those with considerable exposure to the topic. It provides a lens which can be viewed from two directions: 1) towards the past and the history of the field to understand how we have come to where we are today, and 2) towards the future to better understand what is currently possible, and where research is taking us going forward.
This highly popular text, both at undergraduate and post-graduate level, does not claim to be all-encompassing or exhaustive. However, it is a comprehensive treatment given the wide range of the topic. It comes as close as possible, at this time, to being a one-stop reference. As Einstein famously said, ‘Everything should be made as simple as possible, but no simpler’. in the same vein, the book conveys how we can strive towards as much automation as possible, but no more than is necessary. Tacit knowledge and domain expertise remain, for the foreseeable future, beyond the grasp of AI. When it comes to context and corroboration, the input of the human analyst is invaluable. The discipline of data science requires both human and machine input. Completion of this text will help you appreciate why.
Highly recommended. Intellectually, Artificial Intelligence: A Modern Approach provides both a conceptual artificial intelligence gym and a running track to limber up on. The more you use it, the more you will get from it.