Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI's turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent―really―are the best AI programs? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us?

Imran has been a part of cutting-edge research about Algorithms and Machine Learning for the last many years. He completed his PhD in 2010 in which he proposed a new Linear Programming based algorithm which can be used to optimally assign resources in a large scale cloud computing environment. In 2017, Imran developed a realtime analytics framework named StreamSensing. He has since authored multiple research papers that use StreamSensing to process multimedia data for various Machine Learning Algorithms. Imran is currently working at Advanced Analytics Solution Center (A2SC) at Canadian Federal Government as a Data Scientist where he is using Machine Learning Algorithms for critical use-cases. Imran is a visiting professor at Carleton University, Ottawa. Imran has also been teaching for Google and Learning Tree for the last many years. The topics Imran teaches include Algorithms, Cloud Computing and Deep Learning. Over his career, Imran has written many research papers and a couple of his recent papers have won the best paper award. Imran also regularly writes blogs on selected IT topics. In addition to his professional work, Imran is into Nature Photography. Over the years he has taken thousands of photos about nature. Imran's passion is to find a way to make technology work for the betterment of humanity. This passion is the main motivation behind his research.

Genetic Algorithms in Java Basics is a brief introduction to solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. This brief book will guide you step-by-step through various implementations of genetic algorithms and some of their common applications, with the aim to give you a practical understanding allowing you to solve your own unique, individual problems. After reading this book you will be comfortable with the language specific issues and concepts involved with genetic algorithms and you'll have everything you need to start building your own.

Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence ranging from Natural Language Processing(NLP), Computer Vision to Generative Modeling using GANs. He co-founded Mate Labs and currently leading the technical team of Mate Labs. He uses GANs for building different models such as turning painting into photos and enhancing the resolution of images etc. He is super optimistic about Artificial General Intelligence and believes that AI is going to be the workhorse of human evolution.

This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning.

This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You'll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics.

Dr. Reinhard Klette, Fellow of the Royal Society of New Zealand, is a Professor at the Auckland University of Technology (AUT). His numerous publications include the books "Computer Vision for Driver Assistance" (co-authored by Mahdi Rezaei), "Multi-target Tracking" (co-authored by Junli Tao), "Concise Computer Vision", "Euclidean Shortest Paths" (co-authored by Fajie Li), "Panoramic Imaging" (co-authored by Fay Huang and Karsten Scheibe), "Digital Geometry" (co-authored by the late Azriel Rosenfeld), "Computer Vision - Three-Dimensional Data from Images" (co-authored by Karsten Schluens and Andreas Koschan), "The Handbook of Image Processing Operators" (co-authored by the late Piero Zamperoni), and "Fast Algorithms and their Implementation on Specialized Parallel Computers" (co-authored by Jozef Miklosko, Marian Vajtersic, and Imre Vrto)

Conversation as an interface is the best way for machines to interact with us using the universally accepted human tool that is language. Chatbots and voice user interfaces are two flavors of conversational UIs. Chatbots are real-time, data-driven answer engines that talk in natural language and are context-aware. Voice user interfaces are driven by voice and can understand and respond to users using speech. This book covers both types of conversational UIs by leveraging APIs from multiple platforms.

Evolutionary computation, the use of evolutionary systems as computational processes for solving complex problems, is a tool used by computer scientists and engineers who want to harness the power of evolution to build useful new artifacts, by biologists interested in developing and testing better models of natural evolutionary systems, and by artificial life scientists for designing and implementing new artificial evolutionary worlds. In this clear and comprehensive introduction to the field, Kenneth De Jong presents an integrated view of the state of the art in evolutionary computation. Although other books have described such particular areas of the field as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, Evolutionary Computation is noteworthy for considering these systems as specific instances of a more general class of evolutionary algorithms. This useful overview of a fragmented field is suitable for classroom use or as a reference for computer scientists and engineers.

The days when switching from a brick-and-mortar store to an online store was considered a significant modification to the business model are long gone. Thanks to artificial intelligence (AI), the hottest trend in online shopping, the surge of new technologies has radically changed the way people shop. Artificial intelligence (AI) is not a future technology; rather, it is a very real and unavoidable part of the modern era. Artificial intelligence is transforming the retail sector. Retailers may use AI to communicate with customers and operate more efficiently, from deploying cutting-edge tools to customize marketing campaigns to implementing ML for inventory management.