If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Stephen Wolfram has had the sort of career that The Big Bang Theory's Sheldon Cooper might identify with: PhD in particle physics at 20, youngest-ever MacArthur Fellow at 21. At 26, Wikipedia tells us, Richard Feynman advised him to find a way to do his research with as little contact with non-technical people as possible because he didn't understand them, and Wolfram founded a company to do the work he wanted to do. In the years since, Wolfram Research has produced the computer language Mathematica and the computational knowledge engine Wolfram Alpha (which helps Bing and Siri answer questions), done widely-cited work on cellular automata, created Wolfram Language ("a general computational language for humans and computers"), and set scientists arguing by writing his 2002 book, A New Kind of Science. In Adventures of a Computational Explorer, Wolfram has assembled rambling tales of a variety of his exploits: the progressive mathematical development of'Spikey', the company's logo; his work developing Wolfram Alpha; the art of naming functions; speculation about how quantum computing, AI, and the blockchain might usefully intersect; how to communicate generally with aliens; and the habits he has adopted to lead a productive life. Most of these pieces will have the most resonance for those working in computing.
Machine learning is an intimidating topic to tackle for the first time. The term encompasses so many fields, research topics and business use cases, that it can be difficult to even know where to start. To combat this, it's often a good idea to turn to textbooks that will introduce you to the basic principles of your new field of research. This holds true for AI and machine learning, especially if you have a background in statistics or programming. When used alongside more focused online articles like our introduction to training data, they can be an essential part of a powerful toolkit with which to learn and grow.
This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.
Escher's artwork was an inspiration for Douglas Hofstadter's 1979 book "Gödel, Escher, Bach: An ... [ ] Eternal Golden Braid", sometimes referred to as the Bible of artificial intelligence. The field of artificial intelligence has never been the subject of more attention and analysis than it is today. Almost every week, it seems, a new bestselling book comes out examining the technology, business or ethics of AI. Yet few of the topics and debates at the center of today's AI discourse are new. While not always recognized by commentators, artificial intelligence as a serious academic discipline dates back to the 1950s.
A proper grasp of statistics is essential for any machine learning enthusiast to succeed in the competitive domain. Consequently, one should focus more on statistics than on the latest fancy techniques. The book -- All of Statistics -- consists of 24 chapters and covers every topic right from probability to statistical inference and statistical models and methods.
This means only one thing; you need to be prepared for constant learning. With all the abundance of abstract terms and an almost infinite number of details, the AI and ML learning curve can indeed be steep for many. But, getting started with anything new is hard, isn't it? Moreover, I believe everyone can learn it if only there is a strong desire. Besides, there is an effective approach that will facilitate your learning.
This textbook on Python 3 explains concepts such as variables and what they represent, how data is held in memory, how a for loop works and what a string is. It also introduces key concepts such as functions, modules and packages as well as object orientation and functional programming. Each section is prefaced with an introductory chapter, before continuing with how these ideas work in Python. Topics such as generators and coroutines are often misunderstood and these are explained in detail, whilst topics such as Referential Transparency, multiple inheritance and exception handling are presented using examples. A Beginners Guide to Python 3 Programming provides all you need to know about Python, with numerous examples provided throughout including several larger worked case studies illustrating the ideas presented in the previous chapters.
This textbook develops the essential tools of linear algebra, with the goal of imparting technique alongside contextual understanding. Applications go hand-in-hand with theory, each reinforcing and explaining the other. This approach encourages students to develop not only the technical proficiency needed to go on to further study, but an appreciation for when, why, and how the tools of linear algebra can be used across modern applied mathematics. No previous knowledge of linear algebra is needed to approach this text, with single-variable calculus as the only formal prerequisite. However, the reader will need to draw upon some mathematical maturity to engage in the increasing abstraction inherent to the subject.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of "boosting," how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry.
The publication titled as "ARTIFICIAL INTELLIGENCE: AN INDUCEMENT OF TECHNOLOGY IN HUMAN AFFAIRS" is an attempt to explore into the study of Artificial Intelligence and its numerous facets. The book contains scholarly articles from students, research scholars, academicians and experts from different fields medical, engineering, management etc. who have endeavoured to sightsee the various aspects of Artificial Intelligence, its use and effectiveness in the present times. This book aims to give its readers an insightful study of the contribution and development of Artificial Intelligence in other multidisciplinary disciplines like law, medical health care, management, history, economics, social sciences etc. The introductory and several other chapters give the historical perspective, definition, scope, aim and objective of Artificial Intelligence. Then there are few chapters which relate intelligence with medical science, physiology, medical robotics, etc.