AI & I: An Intellectual History of Artificial Intelligence (1版)

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【簡介】 A concise and illuminating history of the field of artificial intelligence from one of its earliest and most respected pioneers.AI & I is an intellectual history of the field of artificial intelligence from the perspective of one of its first practitioners, Eugene Charniak. Charniak entered the field in 1967, roughly 12 years after AI’s founding, and was involved in many of AI’s formative milestones. In this book, he traces the trajectory of breakthroughs and disappointments of the discipline up to the current day, clearly and engagingly demystifying this oft revered and misunderstood technology. His argument is controversial but well supported: that classical AI has been almost uniformly unsuccessful and that the modern deep learning approach should be viewed as the foundation for all the exciting developments that are to come. Written for the scientifically educated layperson, this book chronicles the history of the field of AI, starting with its origin in 1956, as a topic for a small academic workshop held at Dartmouth University. From there, the author covers reasoning and knowledge representation, reasoning under uncertainty, chess, computer vision, speech recognition, language acquisition, deep learning, and learning writ large. Ultimately, Charniak takes issue with the controversy of AI--the fear that its invention means the end of jobs, creativity, and potentially even humans as a species--and explains why such concerns are unfounded. Instead, he believes that we should embrace the technology and all its potential to benefit society.

Nicky Hockly’s 30 Essentials for Using Artificial Intelligence (1版)

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【簡介】 為【劍橋英語教學大師口袋書系列】書籍，內容以「如何有效運用AI提升語言教學」為主軸，提供教學實務上的運用指南與建議，可做為英語教師師培課程用書或課程設計參考書。In this user-friendly book, Nicky Hockly draws on research and her own experience to examine the benefits and challenges of using AI in language teaching. The book provides a range of guidance on good practices in using the technology, with simple tips for applying the learning. It explores some of the key ethical, moral, philosophical and legal questions around using AI and covers topics including accessibility, data ownership and concerns around students cheating. The book also includes support for using AI to help teachers and learners develop. Nicky Hockly's 30 Essentials for Using Artificial Intelligence is an essential guide for teachers of all levels of experience.

Artificial Intelligence: A Guide to Intelligent Systems (4版)

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【簡介】 What are the principles behind intelligent systems? How are they built? What are intelligent systems useful for? How do we choose the right tool for the job? These questions are answered by Michael Negnevitsky’s Artificial Intelligence: A Guide to Intelligent Systems. Unlike many books on computer intelligence, which use complex computer science terminology and are crowded with complex matrix algebra and differential equations, this text demonstrates that the ideas behind intelligent systems are simple and straightforward. This text assumes little or no programming experience as it tackles topics like expert systems, fuzzy systems, artificial neural networks, evolutionary computation, knowledge engineering, and data mining. 【目錄】 Introduction to Intelligent Systems 1.1 Intelligent Machines, or What Machines Can Do 1.2 The History of Artificial Intelligence, or From the ‘Dark Ages’ to Knowledge-based Systems 1.3 Generative AI 1.4 Summary Questions for Review References Expert Systems 2.1 Introduction, or Knowledge Representation Using Rules 2.2 The Main Players in the Expert System Development Team 2.3 Structure of a Rule-based Expert System 2.4 Fundamental characteristics of an expert system 2.5 Forward Chaining and Backward Chaining Inference Techniques 2.6 MEDIA ADVISOR: A Demonstration Rule-based Expert System 2.7 Conflict Resolution 2.8 Uncertainty Management in Rule-based Expert Systems 2.9 Advantages and Disadvantages of Rule-based Expert systems 2.10 Summary Questions for Review References Fuzzy Systems 3.1 Introduction, or What Is Fuzzy Thinking? 3.2 Fuzzy Sets 3.3 Linguistic Variables and Hedges 3.4 Operations of Fuzzy Sets 3.6 Fuzzy Inference 3.7 Building a Fuzzy Expert System 3.8 Summary Questions for Review References Frame-based Systems and Semantic Networks 4.1 Introduction, or What Is a Frame? 4.2 Frames as a Knowledge Representation Technique 4.3 Inheritance in Frame-based Systems 4.4 Methods and Demons 4.5 Interaction of Frames and Rules 4.6 Buy Smart: A Frame-based Expert System 4.7 The Web of Data 4.8 RDF – Resource Description Framework and RDF Triples 4.9 Turtle, RDF Schema and OWL 4.10 Querying the Semantic Web with SPARQL 4.11 Summary Questions for Review References Artificial Neural Networks 5.1 Introduction, or How the Brain Works 5.2 The Neuron as a Simple Computing Element 5.3 The Perceptron 5.4 Multilayer Neural Networks 5.5 Accelerated Learning in Multilayer Neural Networks 5.6 The Hopfield Network 5.7 Bidirectional Associative Memory 5.8 Self-organising Neural Networks 5.9 Reinforcement Learning 5.10 Summary Questions for Review References Deep Learning and Convolutional Neural Networks 6.1 Introduction, or How “Deep” Is a Deep Neural Network? 6.2 Image Recognition or How Machines See the World 6.3 Convolution in Machine Learning 6.4 Activation Functions in Deep Neural Networks 6.5 Convolutional Neural Networks 6.6 Back-propagation Learning in Convolutional Networks 6.7 Batch Normalisation 6.8 Summary Questions for Review References Evolutionary Computation 7.1 Introduction, or Can Evolution Be Intelligent? 7.2 Simulation of Natural Evolution 7.3 Genetic Algorithms 7.4 Why Genetic Algorithms Work 7.5 Maintenance Scheduling with Genetic Algorithms 7.6 Genetic Programming 7.7 Evolution Strategies 7.8 Ant Colony Optimisation 7.9 Particle Swarm Optimisation 7.10 Summary Questions for Review References Hybrid Intelligent Systems 8.1 Introduction, or How to Combine German Mechanics with Italian Love 8.2 Neural Expert Systems 8.3 Neuro-Fuzzy Systems 8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System 8.5 Evolutionary Neural Networks 8.6 Fuzzy Evolutionary Systems 8.7 Summary Questions for Review References Knowledge Engineering 9.1 Introduction, or What Is Knowledge Engineering? 9.2 Will an Expert System Work for My Problem? 9.3 Will a Fuzzy Expert System Work for My Problem? 9.4 Will a Neural Network Work for My Problem? 9.5 Will a Deep Neural Network Work for My Problem? 9.6 Will Genetic Algorithms Work for My Problem? 9.7 Will Particle Swarm Optimisation Work for My Problem? 9.8 Will a Hybrid Intelligent System Work for My Problem? 9.9 Summary Questions for Review References Data Mining and Knowledge Discovery 10.1 Introduction, or What Is Data Mining? 10.2 Statistical Methods and Data Visualisation 10.3 Principal Components Analysis 10.4 Relational Databases and Database Queries 10.5 The Data Warehouse and Multidimensional Data Analysis 10.6 Decision Trees 10.7 Association Rules and Market Basket Analysis 10.8 Summary Questions for Review References Glossary Index

Artificial Intelligence: Foundations of Computational Agents (3版)

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【簡介】 Introduces concepts at the right level of detail, using straightforward language and minimum mathematics Examines the social and ethical impacts of AI, connecting techniques to real-world benefits and harms Explains state-of-the-art algorithms and examples underlying the theory, demonstrating how concepts are applied Provides two complementary software systems – AIPython and AILog - for experimentation and extension Pedagogical features include examples, end-of-chapter reviews, further reading lists, and exercises 【目錄】 Part I - Agents in the World Chapter 1 - Artificial Intelligence and Agents Chapter 2 - Agent Architectures and Hierarchical Control Part II - Reasoning and Planning with Certainty Chapter 3 - Searching for Solutions Chapter 4 - Reasoning with Constraints Chapter 5 - Propositions and Inference Chapter 6 - Deterministic Planning Part III - Learning and Reasoning with Uncertainty Chapter 7 - Supervised Machine Learning Chapter 8 - Neural Networks and Deep Learning Chapter 9 - Reasoning with Uncertainty Chapter 10 - Learning with Uncertainty Chapter 11 - Causality Part IV - Planning and Acting with Uncertainty Chapter 12 - Planning with Uncertainty Chapter 13 - Reinforcement Learning Chapter 14 - Multiagent Systems Part V - Representing Individuals and Relations Chapter 15 - Individuals and Relations Chapter 16 - Knowledge Graphs and Ontologies Chapter 17 - Relational Learning and Probabilistic Reasoning Part VI - The Big Picture Chapter 18 - The Social Impact of Artificial Intelligence Chapter 19 - Retrospect and Prospect Appendix A - Mathematical Preliminaries and Notation Appendix B - Mapping to Open-Source Packages