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.
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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.
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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
原價:
1680
售價:
1596
現省:
84元
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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
原價:
1480
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1376
現省:
104元
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Artificial Intelligence for Improved Patient Outcomes: Principles for Moving Forward with Rigorous Science, 1E (1版)
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Artificial Intelligence for Improved Patient Outcomes provides new, relevant, and practical information on what AI can do in healthcare and how to assess whether AI is improving health outcomes.
With clear insights and a balanced approach, this innovative book offers a one-stop guide on how to design and lead pragmatic real-world AI studies that yield rigorous scientific evidence—all in a manner that is safe and ethical. Daniel Byrne, Director of Artificial Intelligence Research at AVAIL (the Advanced Vanderbilt Artificial Intelligence Laboratory) and author of landmark pragmatic studies published in leading medical journals, shares four decades of experience as a biostatistician and AI researcher. Building on his first book, Publishing Your Medical Research, the author gives the reader the competitive advantage in creating reproducible AI research that will be accepted in prestigious high-impact medical journals.
• Provides easy-to-understand explanations of the key concepts in using and evaluating AI in medicine.
• Offers practical, actionable guidance on the mechanics and implementation of AI applications in medicine.
• Shares career guidance on a successful future in AI in medicine.
• Teaches the skills to evaluate AI tools and avoid being misled by the hype.
• For a wide audience of healthcare professionals impacted by Artificial Intelligence in medicine, including physician-scientists, AI developers, entrepreneurs, and healthcare leaders who need to evaluate AI applications designed to improve safety, quality, and value for their institutions.
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原價:
3465
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3119
現省:
346元
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